Planning Tool

A GRAPHICAL NETWORK PLANNING TOOL FOR GSM AND OTHER CELLULAR NETWORKSby Burak Görkemli B.S. in Computer Engineering, İstanbul Technical University, 1997 Submitted to the Institute for Graduate Studies in Science and Engineering in partial fulfillment of the requirements for the degree of Master of Science in Computer Engineering Boğaziçi University 2002 ii A GRAPHICAL NETWORK PLANNING TOOL FOR GSM AND OTHER CELLULAR NETWORKS APPROVED BY: Assoc. Prof. Cem Ersoy (Thesis Supervisor) ............................ Assoc. Prof. Taner Bilgiç ............................ Prof. M. Ufuk Çağlayan ............................ DATE OF APPROVAL ............................ iii ACKNOWLEDGEMENTS I would like to thank to my thesis supervisor Assoc. Prof. Cem Ersoy for his kind support and patience. Without his guidance, it would be impossible for me to finish this thesis. I would also like to thank to Prof. M. Ufuk Çağlayan and Assoc. Prof. Taner Bilgiç for their participation in my thesis jury. I particularly want to thank to my family and Işıl Temizer for their love, support and encouragement. In this thesis. together with the wired structure. which leads to loss of quality of communication links due to interference between the reused frequencies. . and frequencies should be reused in different cells. Additionally. we tried to come up with a graphical network planning tool. in order to display the results graphically. and available frequencies should be assigned to cells in a manner that the capacity requirement of each cell is met. Frequency planning is a key point in cellular network design. It is a prerequisite for workable. Good network design is no longer a nicety. examine the results of the assignment performed and view the wired structure created. This is rather a hard task because the number of available frequencies is limited. Radio spectrum has become severely congested. Thus.iv ABSTRACT A GRAPHICAL NETWORK PLANNING TOOL FOR GSM AND OTHER CELLULAR NETWORKS The wireless telecommunications industry has undergone explosive growth in the last decade. but rather a necessity. cost-effective wireless systems. make frequency assignment both manually and automatically using a specific algorithm. we added an XML based interface to the tool so that the tool can import design results of other programs that do not support graphics. by which the user will be able to create a wireless cellular network. the tool can also be used as a graphical presentation program for other design tools that concentrate on the design problem itself rather than its presentation. kablosuz hücresel bir ağ yaratabileceği. Bu. yapılan atamanın sonuçlarını inceleyebileceği ve yaratılan kablolama altyapısını görebileceği grafiksel bir ağ planlama aracı geliştirmeye çalıştık. hem de bir algoritmaya göre otomatik olarak frekans ataması yapabileceği. çalışabilir ve düşük masraflı kablosuz sistemler için bir ön şart oldu. Bu zor bir iştir. . Radyo tayfı çok fazla doldu. ki bu da tekrar kullanılan frekanslar arasındaki karışım nedeniyle haberleşme hatlarında kalite kaybına sebep olur. İyi bir ağ tasarımı artık incelikten çok gereklilik haline geldi. grafik desteklemeyen diğer programların tasarım sonuçlarını alıp grafiksel olarak gösterebilmek için programa XML temelli bir arayüz ekledik. Frekans planlama hücresel ağ tasarımında anahtar noktadır. Ayrıca. tasarım probleminin sunumundan çok problemin kendisi üzerinde yoğunlaşmış tasarım programları için grafiksel bir sunum aracı olarak da kullanılabilir.v ÖZET GSM VE DİĞER HÜCRESEL AĞLAR İÇİN GRAFİKSEL BİR AĞ PLANLAMA ARACI Kablosuz haberleşme endüstrisi son on yılda çok büyük bir gelişme gösterdi. hem el ile. çünkü mevcut frekans sayısı limitlidir ve frekanslar atama sırasında tekrar kullanılmalıdır. Böylece program. Bu çalışmada kullanıcının kablolama altyapısıyla beraber. ve mevcut frekanslar hücrelere her hücrenin kapasite gereksinimleri karşılanacak şekilde atanmalıdır. .........4.......................................17 3............................33 Demonstration of Results ......................vi TABLE OF CONTENTS ACKNOWLEDGEMENTS .................................. COMPUTATIONAL EXPERIMENTS ..............31 Effect of Cell Size ................................................................... Base Problem........................................2......13 Hybrid Channel Allocation (HCA) .............................25 4......................................24 4.. 2..................... Fixed Channel Allocation (FCA) ................................... 2.................................................................................................................................3........................... Problem Formulation........................................................................... Greedy Search Algorithm...................................3..........................2..............2..... viii LIST OF TABLES .........................4 2........ Frequency Reuse ............1...............................v LIST OF FIGURES..........................................................................................2....2.......................24 4........30 5.............................27 4... WIRELESS CELLULAR NETWORKS ..........................2...............................2...............................................................................................4....2............................................ 2......................................................................................................................................22 4...1.................. Reuse Pattern Algorithm ........2........... iii ABSTRACT...................................................................................... FREQUENCY ASSIGNMENT PROBLEM ..............................1.............................................................................................. xii LIST OF SYMBOLS/ABBREVIATIONS ..........................................17 3.......15 3.. 5.......2......................... 2............................................. Effect of Some Parameters and Design Decisions ............3...................................................... Simulated Annealing Algorithm.... SOLUTION METHODS........4 Channel Allocation Schemes..1....................9 Dynamic Channel Allocation (DCA).................................................... iv ÖZET..28 5......................8 2...................2...........................1 2.....................................................................................................36 .............................................. xiii 1....2..................... 5....................................................................... Parameters .. Calculation of Objective and Constraint Functions..........................................................................................31 5..........................................................................................1..........................30 5............................ Effect of Call Arrival Rate .......1......15 Comparisons between FCA and DCA ..... INTRODUCTION.......... .............. 6....2............. Implementation Details ..4........1.. Comparison with Commercial Products....................2............................ 6.......63 Adding New Algorithms .... 6....... 6.............1.......65 Overview of the Commercial Products .......2....1..............................47 Creating Frequency Groups ................. Creating Cells... 6.............................................50 Creating the Location Areas. 6..............54 Results ......3..... Adding New Parameters ......................................3...........1...............................5...43 Modifying the Properties of Geographical Structures ................... XML ..............................3..................................... 6......1.............................................................72 ........1.......64 Hardware and Software Requirements.................................................1..............65 Program Files ....................49 Assigning the Frequency Groups to Cells..........................1....................vii 6............................................38 6.............................................................................................................4.... 6................................................ 6.................................38 6...................6......3.......................................................................................9...3...........1..65 Development Tools ........7............................. CONCLUSION AND FUTURE WORK. 6........................65 6.............................1.......... 7.................................. 6............................1.........................1...... 6.....................................................4................63 6...5....................8..48 Modifying Guard Channels........................... NETWORK PLANNING TOOL .....................................1............................................. 6..........5...68 Comparison ... 6...........................2........52 Viewing the Assignment ............4.......68 6.................5.......38 Creating the Wired Structure.. 6.............................................69 6.................................................60 Extending NPT ............................................................58 Interacting with Third Party Tools........................ The Usage...............71 REFERENCES... MSCs and BSCs......4......................................................................1.....................................................2.. ...20 Frequency group creation window ..........................................................2.................28 GS parameter window .......2............. Figure 3...... Figure 2...............................................................................29 Minimum SIR versus average lambda ........... GSM 900/1800/1900 subscribers ... Figure 5..........................................................................................................................17 Cell properties dialog window....................33 Minimum SIR versus number of cells..............2..........1............................32 Maximum probability of blocking versus lambda weight. Figure 5...... Figure 6.......3.......... zoomed-out to display the entire map ..6 Map that NPT uses ................................................. Figure 3...........................3.............39 .1.26 SA parameter window ...........1 Cellular layout with cluster size seven ........21 Flowchart of the SA Algorithm.1......................1...................................................................................................................................................viii LIST OF FIGURES Figure 1.................................... Figure 5..27 RP parameter window ............. Figure 4.................19 Hexagonal cellular layout drawn by NPT .......4........................39 Window displayed when the mouse is right-clicked.........1........3....4......... Figure 4........................ Figure 3................................34 NPT main window......... Figure 3....................... Figure 4...........................1.... Figure 6...... Figure 4.......2..................... .41 Cells that are created according to hexagonal pattern ................................................................3.......9.................................................................................................................... Figure 6.........................................41 Hexagonal layout window....................43 View menu............................47 Figure 6....... Geographic structures window ...........49 Figure 6............................................. Greedy search parameter window ................7..............................4............ix Figure 6....50 Figure 6............. Guard channels window ........46 Figure 6..................44 Viewing the wired structure............17............ BSC information window...................12.....19...52 ........16...............5...51 Figure 6..... with map and layout views disabled...........................................48 Figure 6.....8.........................6................................................10........14........ Figure 6......................................... Edit J Assign menu......................... Figure 6..................... Figure 6.....49 Figure 6........ MSC information window.40 Edit J Layout menu....................11.........................45 Figure 6....................42 Umbrella cell ................ Figure 6...................................46 Figure 6...................................................18................................... Figure 6....................... Cell information window..........50 Figure 6.15...................................................................................................... Reuse pattern parameter window ... Adding a cell manually............................... Frequency group creation/view window .......................................................... Simulated annealing parameter window ....................................................13.............. ..................32.. Cell properties given in the import/export file ....................57 Figure 6................................................x Figure 6............................................33.........60 Figure 6...............59 Figure 6......20............63 .............................26........................... Frequency details window................29........... frequency groups and location areas exposed.......23..................................54 Figure 6........................................................ SIR classes window.......... Probability of blocking for each cell is displayed by a color ......35.30....58 Figure 6.. The BSC and MSC exposed ...........53 Figure 6.......................................................................54 Figure 6....62 Figure 6........ Assigned location areas are displayed with distinct colors .......................59 Figure 6...........57 Figure 6.................... Location area creation/view window.......... Probability of blocking classes window .................................. Location area details window.................... Probability of blocking results window......24.............................................. Frequencies. Results menu .............25..............36...61 Figure 6................31................................56 Figure 6.................... SIR results window............................................................59 Figure 6..............................52 Figure 6... Minimum SIR for each cell is displayed by a color ....... XML based import/export file structure.........22..................34.. Cell information window.....28....56 Figure 6......................21.....................27.................................................................................................. Assigned frequency groups displayed on map by colors .....55 Figure 6........................ ......37...xi Figure 6..................64 .................... Adding new parameters with xtparam tag.......... 34 Results obtained with the SA.....3............... CARB and ODCA...........................................30 Parameters of the base problem..............................................xii LIST OF TABLES Table 2............ Table 5.......................................31 Numbers of cells versus average call arrival rates ........................................1....................31 The cost and the constraint values for the base problem...................................................................... Table 5.6...4....................... BA and BAR..........16 Decision parameters .................................5.. Comparisons between BFA.................. GS and RP algorithms..................... Table 2......22 Geographical structure parameters for the base problem .... SBR................... Table 2.2.3.. Table 2....... Table 5..............13 Comparisons between FCA and DCA..5..... Table 2....2........................1...... Table 5.........2................... Table 3.............................. BDCL and FCA ..........4...13 Comparisons between the FCA schemes ......... Table 5........ Table 3....13 Comparisons between FCA..21 User supplied parameters ..........................................................11 Comparisons between BCO.......... Table 2......11 Channel borrowing schemes........36 .........1................................................... xiii LIST OF SYMBOLS/ABBREVIATIONS Ac C CHc CHf CL COfg D Ffg FG FGc Hc Hm Ii M n N N0 P0 Pb.max Pc Pr Q R Sc SIR SIRmin vm Traffic intensity of cell c Total system cost Number of channels that are assigned to cell c Number of channels per frequency f Number of cells that are created via NPT Number of co-channel cells for the frequency group fg Distance between cell centers Number of frequencies per frequency group fg Number of frequency groups that are created via NPT Number of frequency groups that are assigned to cell c Mean call holding period in cell c Mean call holding period of geographical structure m Interference power caused by the ith interfering co-channel cell Geographical structures Path loss exponent Cluster size Environmental noise Mean received power at a small distance d0 Call blocking probability of cell c Maximum allowable call blocking probability Signal power of cell c transmitted Mean received power at a distance d Co-channel reuse ratio Radius of a cell The desired signal power of cell c Signal to interference ratio Minimum SIR value for sufficient voice quality Mean speed on geographical structure m .c Pb. xiv λc λm AMPS BA BAR BCO BDCL BFA BSC CABR DCA FAP FCA GS GSM HCA ITU IMTS LA MSC MTS NPT ODCA RP SA SB SBR SHB SHCB XML Call arrival rate in cell c Call arrival rate on geographical structure m Advanced mobile phone system Basic algorithm simple channel borrowing scheme Basic algorithm with reassignment simple channel borrowing scheme Borrowing with channel ordering hybrid channel borrowing scheme Borrowing with directional channel locking hybrid channel borrowing scheme Borrow first available simple channel borrowing scheme Base station controller Channel assignment with borrowing and reassignment hybrid channel borrowing scheme Dynamic channel allocation Frequency Assignment Problem Fixed channel allocation Greedy Search Global System for Mobile communications Hybrid channel allocation International Telecommunication Union Improved mobile telephone service Location area Mobile switching center Mobile telephone service Network planning tool Ordered dynamic channel assignment with rearrangement hybrid channel borrowing scheme Reuse pattern Simulated Annealing Simple channel borrowing scheme Borrow from the richest simple channel borrowing scheme Sharing with bias hybrid channel borrowing scheme Simple hybrid borrowing scheme Extensible Markup Language . GSM 900/1800/1900 subscribers [3] . 11 channels were available for all users within a geographic area. The GSM system became successful. In the late 1970s AT&T and Motorola Inc. In IMTS. which led a subscriber limit of 545 in New York region.000 subscribers after first year and 2. INTRODUCTION The development of cellular phone networks in the last decade has attracted the attention of the scientific community and influenced the whole society. which is an improved version. publicly introduced in 1983. Figure 1. Cellular phone networks. however. which was much more capable with 666 paired voice channels. AT&T introduced MTS in 1946. The system.6 million GSM 900/1800/1900 subscribers [3]. which developed a new digital-based mobile communication standard that is in use commercially since 1992 [2]. In 1982 the Groupe Spécial Mobile (GSM) is created. Figure 1. not only in Europe. developed the advanced mobile phone system (AMPS). By the end of June 2001 a total of 417 networks in 153 countries were serving 564. which is the first commercial mobile telephone service.2 displays the growth of GSM subscribers between December 1996 and December 2000.000. exist already more than 50 years.1 1. had 200.000 five years later [1]. but currently all around the world.1. and developed IMTS afterwards. when two transmitters use the same carrier frequency. two 25 MHz wide frequency bands are used. and the weather conditions. Frequencies. During the pace of the algorithm worse moves are accepted with a probability that decreases with time. The choice of SA helped to obtain near optimum solutions since the problem consists of many local minima. depending on the distance between transmitters/receivers. compared to millions of GSM subscribers. SA is a neighborhood search technique with incorporated probabilistic behavior. Cells can be drawn on the map. which enables the algorithm to search beyond local minima. are assigned to the cells by the optimization algorithm. It basically starts with an initial feasible solution and tries to find better solutions. which lead to 124 frequencies. either manually or automatically according to a predefined pattern. which are formed by the underlying road types. The tool operates on a specific map. they may interfere. called cells. High inference may result in loss of sound quality. which operates on a certain frequency [1. a mobile operator should carefully choose the frequencies on which each base station transmits to avoid high interference levels. A cellular phone within a cell is connected to the base station upon request via this frequency. 4]. a graphical network planning tool (NPT) is developed for solving a given frequency assignment problem (FAP) in GSM and other cellular networks.2 All terrestrial cellular phone systems can be characterized by the following properties. which serve over 10 million GSM users [5. antennas and much supporting equipment. Each base station is essentially a radio communication center comprising radios. A frequency used in a cell is reused in another cell. which is an implementation of Simulated Annealing (SA) [7-9]. the power of the signal. . Hence. 6]. In GSM. which are created by the user. resulting a total of 50 frequencies for each operator. the geographical position of the transmitters. while maximizing capacity by reusing the available frequencies as much as possible. the direction in which the signal is transmitted. However. this technique combines the advantages of random search with the ones of greedy search. or even call dropping. each of the two GSM 900 operators is given two 10 MHz wide frequency bands. Thus. In Turkey. which is colored according to different types of geographical structures. since the number of frequencies is far less from the number of subscribers. In this thesis. They consist of a number of base stations that divide a geographic area into smaller areas. call blocking. The section comprises two different problems. the stopping criteria and the cooling schedule. in which the results of different assignment algorithms are compared. Different colors are given to blocking probability and signal-to-interference value ranges and these values for each cell can be displayed by colors. . Several channel assignment schemes. The sixth section explains the usage and implementation details of NPT. which is an implementation of SA. together with performance comparisons. move and remove BSCs and MSCs. blocking probabilities and signal-to-interference ratios can be displayed by the tool thorough colorization. The objective function is given. such as assigned frequencies. The third section is dedicated to the formulation of the design problem. like frequency reuse and channel assignment is given. the conclusion of the thesis is done and some further studies on the thesis are suggested. the assigned location area (LA) can be shown on the created layout via colorization. The optimization algorithm. For this. Additionally. In the last section. Besides the display of frequency assignment results and location areas. Upon creation. connect. effects of input parameters and design decisions on the cost and constraint of the system. together with BSCs and MSCs can be shown on the map. constraints and assumptions are explained in this section. The fourth section explains the solution technique in detail. Furthermore. NPT gives user the ability to add. The fifth section is for presenting the results of the experiments performed.3 The frequency assignment results. The outline of the thesis is as follows: In the second section. is given. frequencies are given distinct colors. together with the creation of neighborhood. the underlying wired network. properties of wireless cellular networks. The decision parameters. which can be displayed on request. are mentioned. alternative solution techniques that are implemented in order to compare with the results of the SA are also described. e. WIRELESS CELLULAR NETWORKS Early mobile radio systems were implemented through mounting a high-powered transmitter on a tall tower. so as to minimize the interference between them.1) . 2. Each cell is allocated a portion of the radio frequencies available to the system.1. which is called a cell. where the average power received from a transmitter at any point decays as a power law of the distance of separation between the transmitter and the receiver. which is a scarce resource. the Bell mobile system in New York City in the 1970s could only support a maximum of 20 simultaneous calls over 1000 square miles [10]. The method of using the same frequencies in the system over a distance is called frequency reuse. thus achieving very good coverage at the cost of under-utilized spectrum usage. which are separated from one another by sufficient distances so that co-channel interference is not objectionable [11]. For example. early systems served to a limited number of mobile users. The radio spectrum. high powered transmitter with many low power transmitters. Thus. since high-powered transmitters hindered the reuse of the same frequencies throughout the system. Frequency Reuse Frequency reuse refers to the use of radio channels on the same carrier to cover different areas (i. It is possible because of physical characteristics of the radio environment.. each providing coverage to only a small portion of the service area. This limitation had been the primary motive for the system designers to come up with the cellular concept. cells).4 2. such that the same frequency group is assigned to cells separated by a sufficient distance. because of interference. was under-utilized. This can be expressed by the following equation [10]: −n d  Pr = P0  d    0 (2. Cellular concept is based on replacing the single. which uses frequency modulation and 20 kHz channels. While it might seem natural to choose a circle to represent a cell. This ratio can be given by the equation below: S SIR = ∑I i =1 c (2.1 shows the concept of frequency reuse. a minimum SIR value. hexagonal layout has been universally adopted since it permits easy and manageable analysis of a cellular system. the distance between co-channel cells can be increased to reduce the co-channel interference level.5 Pr is the average received power at distance d and P0 is the power at a small distance d0 from the transmitting antenna. However. which is pre-determined by SIRmin. where cells labeled with the same number use the same group of channels. Cell boundaries are shown with hexagons in the figure. subjective tests show that sufficient voice quality is provided when SIRmin is 18 dB [10]. Figure 2. due to terrestrial factors.2) i + N0 S is the desired signal power from the desired base station and Ii is the interference power caused by the ith interfering co-channel cell base station. This is the motive behind the power control schemes [13]. which typically ranges between two and four in urban cellular systems [12]. Signal-to-interference ratio (S/I or SIR) is defined to express the co-channel interference faced in frequency reuse. A region can be covered without gaps or overlaps with using three shapes: a . should be reached for that specific channel. The path loss exponent is represented by n. For the U.S. where the total number of co-channel base stations is given by c and N0 represents the environmental noise. which is the ratio of the desired signal power to the sum of interfering co-channel signal powers. AMPS cellular system. For example. different methods can be used. although the shape of the cells is anything but hexagon in practice. To achieve SIRmin value. In order to reuse a channel. circles cannot cover a region without leaving gaps or creating overlapping regions. Another method to increase SIR is to reduce the powers transmitted from the interfering base stations and/or to increase the power of the desired base station. Many channel allocation schemes are based on this idea of physical separation. which are called shift parameters. These N cells. Thus. since less base stations are required in covering an area.6 square. this shape can easily be obtained if the omni-directional antennas of the base stations are placed such that the cell boundaries are determined by the corresponding signal power [14]. a regular hexagon can cover a larger area with the same center-to-vertex distances. In order to find the nearest co-channel neighbors of a specific cell. Among these shapes. which means that a hexagonal layout requires fewer cells than square and triangular layouts. to serve a given total coverage area. each of these groups can be assigned to N cells so that each cell uses different channels from the others. N = i2 + ij + j2 (2. Furthermore. an equilateral triangle and a hexagon. the following steps should be taken: . are called a cluster and N is the cluster size of the cellular system. Cellular layout with cluster size seven As an example. Then. where they are divided into N frequency groups. 2 7 1 6 2 7 1 6 5 4 6 5 3 7 1 4 5 2 3 4 3 Figure 2.3) i and j are non-negative integers.1. the cluster size can only have values that satisfy Equation (2.3) [15]. hexagonal layout is more economic than the other two. which collectively use the complete set of available frequencies. consider a cellular system with a total of S duplex channels available. Due to the properties of hexagonal geometry. The ratio of D over R is called the co-channel reuse ratio Q.2. 8 19 18 7 17 6 16 8 19 18 7 17 6 16 15 14 19 18 7 17 6 16 15 14 5 13 1 4 12 16 15 14 2 3 11 17 6 5 13 5 13 8 9 10 18 7 1 4 12 1 4 12 16 15 14 19 2 3 11 2 3 11 17 6 5 13 8 9 10 9 10 18 7 1 4 12 16 15 14 15 14 19 2 3 11 17 6 5 13 5 13 8 9 10 18 7 1 4 12 1 4 12 16 15 14 19 2 3 11 2 3 11 17 6 5 13 8 9 10 9 10 18 7 1 4 12 19 2 3 11 8 9 10 Figure 2. SIR becomes dependant on only the radius of the cells (R) and the distance between centers of the nearest co-channel cells (D). move i cells along any chain of hexagons and then turn 60 degrees counter-clockwise and move j cells. where i is three and j is two. Q is given as [10]: . For hexagonal geometry. Locating co-channel cells when N = 19 (i = 3. This method is shown in Figure 2.7 First.2 for a cluster size of 19. j = 2) When the sizes of the cells are the same and the base stations transmit the same power. R−n SIR = ∑ (D ) i =1 i i0 (2. It is interesting to note that as the required SIR is determined. These schemes can be grouped into three categories.4). SIR = ( D / R) n ( 3N ) n = i0 i0 (2. For instance.4) It is obvious that larger values for Q means smaller co-channel interference. so that the Equation (2.8 Q= D = 3N R (2.5) −n The Equation (2. based on how co-channels are separated [13]: . when the power transmitted from each base station is equal. assuming a path loss exponent of four. to meet an SIR requirement of minimum 18 dB.6) is achieved.5) can further be simplified by considering only the immediate interfering cells. in hexagonal cell layout where all the interfering cells are equidistant from the base station receiver. the SIR for a mobile can be approximated as in Equation (2. the path loss exponent n is same throughout the coverage area and the environmental noise N0 is omitted. since the distance between co-channel cells are large. since number of channels per cell is proportional with 1/N. with base stations at an equal distance D from the desired base station. the cluster size should be at least seven.5). larger values for Q also mean larger cluster size.6) 2. which leads to smaller capacity. However. with respect to cell radius. Channel Allocation Schemes Channel allocation schemes deal with assigning channels to cells in such a manner that the required capacity is met while maintaining a minimum SIR.6). and by using Equation (2. cluster size is also found. For a hexagonal layout. according to the Equation (2.2. 2. Dynamic channel allocation (DCA). traffic in cellular systems is far from being uniform. Hybrid channel allocation (HCA). However. thus forming a uniform channel distribution. which is efficient when the traffic is also uniformly distributed. a cell that has used all of its channels can borrow free channels from its neighboring cells.1. the total available channels are grouped into sets. a new technique based on determining a relationship between the static and the dynamic SIRs can be used. . Fixed Channel Allocation (FCA) In FCA. provided that the borrowed channel does not interfere with existing calls. Borrowed channels are returned back to donor cells when the calls using them end. channels may be assigned to each cell depending on the expected traffic of the cell. several other cells are prohibited from using it.9 • • • Fixed channel allocation (FCA). In order to adapt to the nonuniform traffic. whereas the radio spectrum may be under-utilized in low-traffic cells. This scheme has already been told in Section 2. 17]. whereas borrowable channels are allowed to be used by neighboring cells in case of need. Local channels are only for local usage and cannot be borrowed. forming local channels and borrowable channels. as done in non-uniform channel allocation [16. any channel in each cell can be borrowed. as given in [18]. Channels assigned to each cell are grouped into two subsets in hybrid borrowing. while only channels marked as borrowable can be used by neighboring cells in hybrid channel borrowing.2. Simple FCA scheme assigns same number of channels to each cell.1. In simple channel borrowing. Another method that can be used to handle non-uniform traffic is channel borrowing. which is called channel locking. The channel borrowing schemes are further divided into simple and hybrid. When a channel is borrowed. In this scheme. and this scheme may result in high blocking probabilities in cells with high traffic figures. Also. where a set is assigned to each cell for its exclusive usage. CA summary of the comparison between the BFA. and the channel usage efficiency drops causing a higher probability of blocking [19]. BFA) have performance results comparable to schemes making complex searches for candidate channels. Basic algorithm with reassignment (BAR). which differ in the algorithm used to select the channel to be borrowed: • • • • Borrow from the richest (SBR). However. The tests done with the SBR. The BFA algorithm selects the first candidate channel that satisfies the co-channel reuse constraint. so does the number of locked channels due to borrowing. where the number of locked channels is small. a call using a borrowed channel is transferred to a nominal channel. while it also takes channel locking into account by trying to minimize the future call blocking probability in the cell that is most affected by the channel borrowing. In the BAR scheme.10 The simple borrowing strategy gives lower blocking probability than static FCA under light and moderate traffic. This is because channel borrowing is able to handle traffic fluctuations under light traffic. SBR. BA and BAR schemes is given in Table 2. instead of trying to optimize.1. Most studies on the performances of the simple channel borrowing schemes show that the algorithms with simple tests for borrowing (i. The BA scheme is same with the SBR algorithm. the BFA scheme has an advantage over the other two in that its complexity is significantly less [13]. But as the traffic increases. BA and BFA schemes result in nearly the same average blocking probability versus load. but static FCA performs better in heavy traffic conditions. a channel is borrowed from the cell with the greatest number of channels available for borrowing. There are several simple channel borrowing schemes. In the SBR. Basic algorithm (BA). All these schemes try to reduce the number of locked channels caused by channel borrowing.e. Borrow first available (BFA). . whenever a nominal channel becomes available. 1. Table 2. a summary of both simple and hybrid borrowing schemes are given in Table 2. Ordered dynamic channel assignment with rearrangement (ODCA). Borrowing with directional channel locking (BDCL). hybrid borrowing has several different schemes. Comparisons between BFA.11 Table 2. Channel assignment with borrowing and reassignment (CABR). BA and BAR Scheme Complexity Flexibility Performance # of tests to locate borrowable channel Few A lot A lot Very few Borrow from the richest (SBR) Basic algorithm (BA) Basic algorithm with reassignment (BAR) Borrow first available (BFA) Moderate High High Low Moderate Moderate Moderate Low Besides simple channel borrowing. Channel borrowing schemes Category Simple channel borrowing Scheme Simple borrowing (SB) Borrow from the richest (SBR) Basic algorithm (BA) Basic algorithm with reassignment (BAR) Borrow first available (BFA) Simple hybrid borrowing scheme (SHCB) Borrowing with channel ordering (BCO) Borrowing with directional channel locking (BDCL) Sharing with bias (SHB) Channel assignment with borrowing and reassignment (CABR) Ordered dynamic channel assignment with rearrangement (ODCA) Hybrid channel borrowing .2. Borrowing with channel ordering (BCO). Sharing with bias (SHB).2. which are listed below. Also. SBR. • • • • • • Simple hybrid channel borrowing strategy (SHCB). In this strategy. Also. the channels in the donor cells are grouped into standard and borrowable sets. Also. each cell is divided into three sectors. Simulations applied to BDCL. A similar scheme to BCO is given in [20]. Standard channels are assigned to cells nominally. whereas BCO locks the co-channels in three nearby co-channel cells. BCO and FCA shows that the BDCL scheme gives the lowest blocking probability. ODCA has a more complex algorithm than the other two. with the name fixed preference channel assignment. The performance comparison between the different hybrid channel borrowing schemes is . a call served by a standard channel is switched to a higher prioritized standard channel. Also.. BDCL restricts channel locking to those directions affected by the borrowing. but the criterion in the CARB scheme is used when ordering. for both uniform and nonuniform traffic.e. CARB chooses candidate channels to be borrowed in the manner of causing the least harm to neighboring cells in terms of future call blocking probability. only one of which can borrow channels from the two adjacent cells (donor cells). A call on a borrowed channel is transferred to a standard channel upon becoming available in ODCA. The BDCL strategy differs from BCO in channel locking.e.. BDCL also transfers a call on a borrowed channel to a nominal channel or to another borrowed channel. the number of channels available for borrowing is greater than that in the BCO algorithm.1.12 In SHCB. BCO keeps the ratio of standard channels to borrowable ones dynamic. In this strategy. However. which are standard (A) and borrowable (B) channels. the assigned channels are ordered from the highest probability for being used locally (i. lowest probability for being borrowed) to the lowest probability for being used locally (i. Thus. in order to minimize the channel borrowing for future calls. ODCA was tested in [21] for a highway microcellular environment with non-uniform traffic load and it was revealed that ODCA results in better channel utilization than CARB and FCA. if available. thus adapting to the variations in traffic better than SHCB. highest probability for being borrowed). as in SHCB. The ODCA scheme uses the merits of CARB and BCO with some modifications. followed by BCO and FCA. the channels assigned to each cell are ordered as done in BCO. and it results in more frequent switching of channels because of reassignment scheme. channels assigned to each cell are grouped in two sets. it supports more traffic than CARB and FCA. whereas borrowable channels are allowed to be borrowed. at blocking probabilities under 0. In the SHB scheme. Comparisons between BCO. Comparisons between the FCA schemes Scheme Simple FCA Static borrowing Simple channel borrowing Hybrid channel borrowing Complexity Low Low-moderate Moderate-high Moderate Flexibility Low Moderate High Moderate Performance Better than dynamic and hybrid borrowing in heavy traffic Better than FCA Better than FCA and static borrowing in light and moderate traffic Better than FCA in light and moderate traffic Better than simple channel borrowing in heavy loads 2.3. a summary of comparison between FCA schemes is given in Table 2. BCO. BCO. Table 2. CARB and ODCA Category Channel utilization Computational complexity Traffic carried capacity Results (increasing from left to right) FCA. there is no fixed relationship between channels and cells in DCA.5.3 and Table 2. a channel can be used only if the signal interference constraint is satisfied. ODCA ODCA.4. Comparisons between FCA. the channel used is returned to the central pool. After call completion. BDCL BDCL. ODCA Table 2. A variation of . CARB. FCA Table 2.2. BDCL and FCA Category Traffic carried capacity Blocking probability Results (increasing from left to right) FCA. In DCA. Also. FCA FCA.13 given in Table 2. All channels are kept in a central pool and are assigned to cells as new calls arrive. In contrast to FCA.4.2. CARB.5. CARB. Dynamic Channel Allocation (DCA) The DCA schemes are developed in order to adapt to the short-term temporal variations of traffic. where rearrangement of the channel assignment is considered. Also. depending on the type of management they employ. distributed schemes use either local information about the currently available channels in the cell’s vicinity or signal strength measurements. Another type of DCA that exploits the mobility of users is location adaptive DCA (LA-DCA). The existing DCA strategies are classified into three categories based on the type of network dynamics they exploit and the representation of the SIR constraint they employ. . Traffic adaptive DCA (TA-DCA) strategies take advantage of the unevenness of traffic among cells. each base station knows which channels are available in its vicinity. a channel from the central pool is assigned to a call by a centralized controller. use channel reuse factor and compatibility matrix to represent the SIR constraint. at the expense of high centralization overhead. These can produce near-optimum results. Cell-vicinity based DCA schemes produce near-optimum channel allocation at the expense of status information traffic between base stations. a cell is split into a number of concentric subcells and the SIR constraint is approximated by different channel reuse factors for different subcell groups. In cell vicinity based schemes. which differ in the cost function used for selecting one of the candidate cells for assignment. There is no formal representation of the SIR constraint in IADCA schemes [23]. The third type of DCA that assigns channels based on real-time interference measurement is called interference adaptive DCA (IA-DCA). Status information is exchanged between base stations upon modifications in channel allocation. Instead of a centralized information base.14 DCA is given in [22] under the name of Maximum Packing Algorithm. Distributed DCA schemes are developed in order to cope with the high centralization overhead present in the centralized DCA schemes. and channels are assigned to calls by base stations. In centralized DCA schemes. A distributed DCA scheme is proposed in [24]. Many centralized schemes developed. that is. the DCA schemes can be divided into centralized and distributed schemes. In the typical implementation of LA-DCA. reuse partitioning. Thus. . channels can be placed anywhere. since DCA is not as successful as FCA in frequency reuse.3. while DCA can adapt to variations in traffic. However. The channels that belong to the fixed set are assigned to cells. Hybrid Channel Allocation (HCA) HCA schemes are a combination of FCA and DCA schemes. as in FCA. the available channels in the system are grouped into fixed and dynamic sets. where channels are assigned from a pool upon request. Comparisons between FCA and DCA Simulation results show that DCA schemes over-performs FCA schemes under low/moderate load and non-uniform traffic [25]. In HCA.6. a channel from the dynamic set is assigned to the call. with some cells having unused channels while others have all channels occupied. in contrast to FCA strategies.4.2. and makes DCA schemes perform worse than FCA schemes under heavy load conditions. 2.15 DCA schemes that are based on signal strength measurements use local information only. with contrast to the cell-vicinity based strategies. this parameter is a function of the traffic load and may vary according to the load distribution estimations [13]. In general. This is because of the dynamic nature of DCA methods. in order to increase capacity or improve radio coverage. In these schemes. when a call needs to be established and there are no nominal channels available in the cell. The channels in the dynamic set are kept for future requests such that.2. FCA cannot use all the channels in the same intensity. A summary of the performance comparison of FCA and DCA schemes is given in Table 2. 2. as needed. where channels are pre-assigned to cells. This leads to low channel utilization. Frequency reuse is maximized in these DCA schemes at the expense of increased cochannel interference. the dynamic nature of DCA schemes becomes a disadvantage when the traffic load is increased. The ratio of fixed to dynamic channels is an important system parameter that affects the system performance. 16 Table 2. labor intense frequency planning • No frequency planning Low signaling load • Moderate to high signaling load Centralized control • Centralized. decentralized.6. Comparisons between FCA and DCA FCA • Performs better under heavy traffic • • • • • • • • • • • • • • • DCA • Performs better under light/moderate traffic Low flexibility in channel assignment • Flexible allocation of channels Maximum channel reusability • Not always maximum channel reusability Sensitive to time and spatial changes • Insensitive to time and time spatial changes Not stable grade of service per cell in an • Stable grade of service per cell in an interference cell group interference cell group High forced call termination probability • Low to moderate forced call termination probability Suitable for large cell environment • Suitable in micro-cellular environment Low flexibility • High flexibility Radio equipment covers all channels • Radio equipment covers the temporary assigned to the cell channels assigned to the cell Independent channel-control fully • Control dependent on the scheme centralized to fully distributed Low computational effort • High computational effort Low call set up delay • Moderate to high call setup delay Low implementation complexity • Moderate to high implementation complexity Complex. distributed control depending on the scheme . However.1. FREQUENCY ASSIGNMENT PROBLEM NPT. NPT allows the user to change the cost and constraint. and use cochannel interference as constraint while trying to minimize the probability of blocking. 3. assigns frequencies to cells in such a way that the co-channel interference is minimized.1. Map that NPT uses .17 3. while the probability of blocking constraint is satisfied. Parameters Figure 3. which is developed in this thesis. house. Signal power of the cell transmitted. MSC that the cell is assigned. Call arrival rate per second. • • • Number of guard channels that won’t be used in the frequency assignment. boulevard. which is calculated automatically according to the geographical structures covered (call arrivals to the cells follow a Poisson distribution regardless of the geographical structures covered). Seven types of structures are used in the current map. street. as shown in Figure 3. Each of them has several properties. LA of the cell. which are listed below and shown in Figure 3. which are empty. given in decibels. Probability of blocking calculated after the frequency assignment. Frequency groups assigned. Currently. Mean call holding period in seconds.2: • • • • • • Label of the cell. colored depending on the geographical structures comprised. which is noneditable. avenue. like: • • • • Color by which the structure will be displayed on the map. a Kadikoy map is used.18 NPT operates on a special map. given in seconds. hotspot and main road. which is calculated automatically according to the geographical structures covered (call duration is exponentially distributed). . which is updated automatically as the shape of the cell is changed according to a specific ratio. BSC that the cell is assigned. Mean speed in km/hour. Call arrival rate. Each cell created on the map has some properties.1. • Average call holding time. λ. 3. according to a pre-determined pattern. Two layout patterns are available for this purpose.19 Figure 3. letting the user to draw each cells by mouse. the user can modify any cell by adding new nodes to the cell boundary. Thus. as in Figure 3. . which are hexagonal layout and Manhattan (rectangle) layout. calculated from the average of the coordinates of the nodes. shown by triangle. removing the existing cell boundary nodes or moving nodes/lines of the cell boundary. the transmitter node also moves when the cell nodes are moved. Cell properties dialog window NPT allows the user to create cells either manually. or automatically.2. After creating the cellular layout. The transmitter of a cell are assumed to be in the middle of the cell boundary nodes. As the user enters the number of frequency groups to be created. as shown in Figure 3. along with the number of frequencies per group.20 Figure 3. NPT gives the user a simple interface for creating frequency groups. Each frequency group has several properties. the frequency groups are created with frequencies. indicating the adjacency relationship.4. Hexagonal cellular layout drawn by NPT Frequency groups should be created by the user before assignment.3. non-editable. Frequencies comprised. Label of the frequency group. non-editable. which are: • • • Index of the frequency group. . each of which is assigned to the group in a manner that the adjacent-channel interference is minimal. Frequency group creation window Consequently. Table 3.4.c Description Total system cost The desired signal power of cell c Number of co-channel cells for the frequency group fg Interfering signal power from the ith co-channel cell. the parameters decided and the ones supplied by the user are given in Table 3.2.1. Number of frequency groups assigned to cell c Number of channels assigned to cell c. automatically assigned. Decision parameters Parameter C Sc COfg Ii FGc CHc Pb.1 and Table 3. used when the frequency groups are displayed on the cellular layout.21 • Color of the frequency group. Figure 3. the result of FGc*Ffg*CHf Call blocking probability of cell c . as a summary. in km/hr Mean call holding period on geographical structure m.2. the objective function can be given as follows:     Sc   Maximize Min(SIRmin ) = Min CO    ∑ Ii + N0   i =1  λm vm Hm CL FG Ffg CHf Pc λc Hc Ac Pb. in 1/seconds Mean speed on geographical structure m. in seconds Number of cells that are created via NPT Number of frequency groups that are created via NPT Number of frequencies per frequency group fg Number of channels per frequency f Signal power of cell c transmitted. User supplied parameters Parameter M Description Geographical structures given by the underlying map Call arrival rate on geographical structure m. Thus. NPT tries to maximize the minimum signal-to-interference ratio among all the cells. in decibels Call arrival rate in cell c. in 1/seconds.22 Table 3.2. calculated according to the geographical structures covered Mean call holding period in cell c. found by λc*Hc Maximum allowable call blocking probability Minimum SIR allowable Path loss exponent of the environment Environmental noise 3. Problem Formulation The approaches to solve the frequency assignment problem can be subdivided in two main streams: minimization of the total cost (minsum) and minimization of the maximum cost (minmax) [1].max SIRmin N N0 (3. in seconds.1) . so that the interference between the co-channels is minimized. according to the geographical structures covered Traffic intensity of cell c. c ≤ Pb . Erlang B formula is used in order to calculate the blocking probability for each cell.max for all cells.4) Ac is the traffic intensity of the cell c. .2) FGc ≤ FG . The constraints are given below: Pb .max .23 The objective function is maximized in such a way that the call blocking probability is less than or equal to Pb. as given in [10]: CH Pr [blocking ] = Ac c CH c ! CH c ∑ k =0 Ac k! k (3. and is calculated by multiplying the call arrival rate with call holding period. ∀c (3.3) Since the system is modeled via M/M/m queues. ∀c (3. randomized saturation degree heuristic [37]. The signal powers to be used when finding out SIR are calculated by Equation (2. ANT heuristic [35]. that is. Calculation of Objective and Constraint Functions The objective in NPT is the maximization of the minimum SIR. other than graph coloring algorithms. When determining the SIR for a specific cell and a specific frequency group. Therefore. More generally.1). 4.24 4. genetic algorithms [34]. 40].26-32]. Thus. Many heuristic methods. simulated annealing (SA). as done in [1. ANTS algorithm [38] and others [39. the cells that use the desired frequency group. the following steps are followed: • The co-channel cells. neural network algorithm [36]. that is. co-channel relationship is considered on the frequency group level. co-channel cells are the ones using the same frequency groups. as mentioned in the previous section.1. it is assumed that co-channel interference may occur between the frequencies that belong to the same frequency group. the problem is equivalent to the so-called set T-coloring problem. Also. it is unlikely to find any efficient algorithm for this problem [33]. Only the interference caused by co-channels are taken into account. tabu search [33]. The basic FAP can be shown to be NP-hard in its simplest form because it is reduced to the graph coloring problem.1). SA optimization technique is used. SOLUTION METHODS Graph theory and algorithms are used extensively in the literature for solving frequency assignment problems. and the adjacent channel interferences are not calculated. have been proposed to deal with the FAP. The SIR for a specific frequency group and a specific cell is calculated by Equation (3. In this thesis. including constraint programming. . are determined. the minimum SIR value is selected as the output of the objective function. Simulated Annealing Algorithm The SA algorithm [7-9] is chosen for its ease of implementation and success in finding a near optimal solution.1. This value is tried to be kept under a selected maximum value for all cells. • Within the SIR values calculated for each node of the boundary of the desired cell. However. frequency groups that are assigned to neighboring cells are also used. the move is accepted. The algorithm starts with an initial feasible solution and makes random moves within the range of the neighbors that can be reached from the current solution. Otherwise. only considering co-channel interferences in the neighboring cells. If the cost of the neighbor is less than the current cost. the move is accepted with a probability that decreases in time. frequency groups other than the ones assigned to neighboring cells are tried to be assigned to each cell. Initial solution is found by assigning frequency groups to the cells in a random manner. 4. Flowchart of the SA algorithm is given in Figure 4. in a manner to make the probability of blocking less than the maximum specified value. That is.25 • The SIR is calculated at each node of the boundary of the desired cell. The cost associated with the neighbor is considered before really passing on to the neighbor. Each step in the algorithm corresponds to visiting a feasible neighbor.4). The distances are taken as between the cell boundary node and the transmit nodes of the co-channel cells. . Probability of blocking for a specific cell is calculated via Equation (3. which is also the value to be maximized.2. which is two per cent by default. if probability of blocking constraint cannot be satisfied. The constraint is selected as the probability of blocking. the minimum SIR value is taken into account. After calculating the SIR values for all the cells and frequency groups. Flowchart of the SA Algorithm Before starting the annealing process. specified by the user. the temperature is increased so that the generated neighbors are accepted regardless of their costs. the system is melted. at each step. Then.1.26 find an initial feasible solution melt the system cool the system generate neighbor calculate cost of the neighbor NO exp(-delta(cost)/T) > random() cost better than previous YES accept move reset fail counter increase iteration counter NO increase fail counter YES both counters < maximum values NO get best move YES Figure 4. the temperature is decreased by a scale. that is. . which is given by Equation (2. which is given by the user via the window shown in Figure 4.3.27 Neighborhood generation is performed by randomly selecting a cell and assigning a new frequency group to it. Figure 4. SA parameter window 4. one of the existing frequency groups of the cell is removed. When the number of moves that are rejected one after another reach to a specified maximum number. . SA also stops when this maximum number. assignment by using Reuse Pattern (RP) is also implemented.3). The second stopping criterion is the number of moves made since the algorithm started.2. There exist two stopping criteria in the SA algorithm. but the user may modify this value. Reuse Pattern Algorithm In order to compare the results that are obtained by SA.2. The default value for this maximum reject-count is given as 20. The first criterion is the number of moves that are not accepted sequentially. if needed. Also. if the blocking probability constraint is satisfied. the SA stops. the cluster size equation is used. In this algorithm. is reached. This is repeated until a maximum count is reached. then the GS algorithm is run starting from a point of the feasible solution space.28 As mentioned in the second section. The cluster size field in the figure is not editable. is the Greedy Search algorithm (GS). i and j are non-negative integers. which are called shift parameters. RP parameter window 4.3. . since the cluster size is restricted to the equation given in Equation (2. and is automatically calculated from switch parameters. algorithm comparison is performed for some specific number of frequency groups. the algorithm is stopped and then rerun starting from a different feasible solution [14]. However. This algorithm is used mostly for problems having convex cost functions. If the cost function is not convex. Figure 4.4. which is used for comparing the results obtained by different algorithms.2. Blocking probability is not taken into account in this assignment. as shown in Figure 2. the number of frequency groups that are to be used in the assignment is also bound to the same equation.3). Thus. These parameters are used in assigning the frequency groups to cells. like in this case. When the cost cannot be improved any further. Greedy Search Algorithm The other neighborhood search algorithm.3. The shift parameters are taken from the user by the window shown in Figure 4. 4. and the worse moves are not taken into account. is taken from the user. the better moves are accepted only. GS parameter window . Thus.29 The GS algorithm is implemented by the SA algorithm that starts with an initial temperature of zero. by the window displayed in Figure 4. Figure 4. along with other parameters. The number of times that the GS algorithm is run.4. The properties of the geographical structures are given in Table 5.061. where the effects of parameters to the cost and constraint functions are examined.20 0. together with other parameters.25 0. several groups of tests are performed.1. The average call arrival rate is 0. Next.30 Empty House Street Avenue Boulevard Hotspot Main road 0 1 10 35 50 35 90 60 60 60 60 60 60 60 Number of cells created in the base problem is 151.50 0.22 kilometers on the map.45 kilometers approximately. Each cell has a radius of 0. GS and RP algorithms are compared. Base Problem All the experiments are performed on a special map.10 0. COMPUTATIONAL EXPERIMENTS In order to present the characteristics of the frequency assignment problem.2. . so total number of channels per cell equals to number of channels per frequency group multiplied by number of frequency groups per cell.15 0. Lastly. where one centimeter represents approximately 0. in terms of the problem cost and constraint values.30 5. In this part. several computational experiments are performed. each having one frequency. the SA algorithm is used in frequency assignment. Nineteen frequency groups are added.1.1. are listed in Table 5. These values. performance of the SA. First. Table 5. 5. The map comprises seven different geographical structures. Geographical structure parameters for the base problem Geographical structure vm Hm λm 0 0. Cells do not use guard channels. a sample problem and the corresponding solution are presented. Effect of Call Arrival Rate In order to examine the effect of the call arrivals on the minimum SIR and maximum probability of blocking. starting with possibly different initial assignments.2.1. cost) and the maximum probability of blocking (i.31 Table 5. Parameters of the base problem Parameter CL λc.. the effects of several parameters on the minimum SIR and maximum probability of blocking values are examined in detail. while the others are kept the same. constraint) values obtained by SA for the base problem are given in Table 5. 5. Table 5..40 5. the algorithm is executed 10 times.02 14 dB 4 0 The minimum SIR (i. The cost and the constraint values for the base problem Minimum SIR Maximum Probability of Blocking 0. For each parameter set. The results are presented as charts. Only the parameter that is examined is changed.e. In all the experiments.e.3. the input parameters are the parameters for the base problem.061 19 1 8 15 dB 0.3. SA method is used during assignments. Effect of Some Parameters and Design Decisions In this sub-section. frequency assignment is performed for various sets of call arrival .2.2.max SIRmin N N0 Value 151 0.avg FG Ffg CHf Pc Pb. with maximum number of iterations equal to 20000.019 7. and the best value is taken into account. Minimum SIR versus average lambda The maximum probability of blocking values for the average lambda values are shown in Figure 5. .08 0. From the figure.01 0.1. The call arrival rates of the geographical structures for the base problem are not modified.06. it can be derived that the cellular network created can support a maximum traffic value of 0.1. The results obtained for the minimum SIR are given in Figure 5. which is not acceptable.08 calls per second in average.06.03 0.09 and 27 per cent for lambda 0. The set of values are achieved by varying the call arrival weight that is used when calculating the call arrival rates of the cells according to the covered geographical structures. the probability of blocking rate is kept under two per cent. However.07 0.02 0.09 0. being 14 per cent for lambda 0.1 Average Lambda (1/sec) Figure 5. Changing the lambda weight from 0.1 does not affect the cost function.04 0. Up to this value.1.2.05 0. As seen in the figure.08. since the SIR drops below 14 dB. there is a big change between average lambda values 0. 30 25 Minimum SIR (dB) 20 15 10 5 0 0.05 and 0.06 to 0. after the average lambda value 0. and the sound quality becomes unacceptable after lambda weight 0. the probability of blocking increases dramatically.32 rates for the geographical structures.06 0. as done in Section 5.2. without any gaps.10 0.33 0. .04 0. Effect of Cell Size The effect of cell size to minimum SIR and maximum probability of blocking values is also examined.03 0. are given in Table 5.05 0.2.09 0.3.06 0.4. in order to tile the map completely.1.30 0.1 Average Lambda (1/sec) Figure 5. which are basically calculated by taking the average of the lambda values of all the cells.2.00005. Distinct values are used for the number of cells. Also. The sizes of the cells are changed by the number of the cells created.05 0.15 0.00 0.08 0. Average lambda values corresponding to various numbers of cells. Maximum probability of blocking versus lambda weight 5. The average lambda is not changed directly.2. Again the call arrival rates on the geographical structures for the base problem are used.07 0. the results obtained for the minimum SIR are given in Figure 5.02 0.25 Maximum Probability of Blocking 0.20 0. The lambda weight is taken as 0. but changing the cell sizes affected this value.01 0. meaning that the speech quality is below the acceptable levels.3.042 234 0. In order to . Minimum SIR versus number of cells In Figure 5. Also.34 Table 5.071 141 0.4.064 151 0.049 194 0.3.037 277 0. Numbers of cells versus average call arrival rates Number Average call of cells arrival rate (1/sec) 95 0. the minimum SIR value in this range is below 14 dB.080 121 0.039 249 0.033 35 30 25 Minimum SIR (dB) 20 15 10 5 0 95 102 112 121 129 141 151 164 172 186 194 207 219 234 249 277 Number of Cells Figure 5.055 172 0.045 219 0.076 129 0.061 164 0.094 102 0. it can be noticed that the minimum SIR value does not change much between number of cell values 95 and 164.087 112 0.053 186 0.048 207 0. Again.35 increase the speech quality. As seen in the figure.4.1 0. . system capacity should be increased. 0.15 0.25 Maximum Probability of Blocking 0.2 0. The effect of the latter option can be seen in the Figure 5.4. the minimum number of cells required by the system is 172.05 0 95 102 112 121 129 141 151 164 172 186 194 207 219 234 249 277 Number of Cells Figure 5. and continues to decrease as the number of cells is increased. for having an acceptable speech quality. The maximum probability of blocking values for various numbers of cells are shown in Figure 5.3. either by adding new frequencies or adding new cells to the system. 172 is found to be the minimum number of cells required. maximum probability of blocking decreases as the number of cells is increased. At 172 cells. in order to meet the probability of blocking constraint. splitting the cells in which the probability of blocking values are above acceptable levels can be applied to decrease these values. the minimum SIR value exceeds 14 dB. As the number of cells is increased to 172. Thus. Thus. Maximum probability of blocking versus number of cells Up to 172 cells. the probability of blocking reaches 2 per cent. the maximum probability of blocking of the system is above the acceptable 2 per cent value. 36 5.3. Demonstration of Results In this section, the results obtained from the SA, GS and RP algorithms are compared. Both for SA and GS, maximum number of iterations is taken as 20000. For each problem, SA and GS algorithms are run 10 times, and the best results are taken into account. In the RP algorithm, a reuse pattern for cluster size equal to 19 (i = 3, j = 2) is used. This algorithm is run only once for each problem, since the results are exact and do not change. Table 5.5. Results obtained with the SA, GS and RP algorithms Prob. No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Base Problem Cell No=95 Cell No=102 Cell No=112 Cell No=121 Cell No=129 Cell No=141 Cell No=164 Cell No=172 Cell No=186 Cell No=194 Cell No=207 Cell No=219 Cell No=234 Cell No=249 Cell No= 277 Av. Lambda=0.01 Av. Lambda=0.02 Av. Lambda=0.03 Av. Lambda=0.04 Av. Lambda=0.05 Av. Lambda=0.07 Av. Lambda=0.08 Av. Lambda=0.09 Av. Lambda=0.1 Prob. Description SA Cost min SIR 7.40 7.27 7.38 7.33 7.20 7.25 7.46 7.49 17.20 20.19 19.03 18.75 20.09 26.89 3 1 .1 2 27.87 28.94 55.55 39.05 27.73 18.53 7.24 7.02 7.14 6.73 SA Const max Blck 0.019 0.017 0.019 0.018 0.019 0.020 0.017 0.019 0.020 0.020 0.019 0.020 0.019 0.020 0.019 0.017 1.4E-5 0.001 0.011 0.020 0.020 0.019 0.017 0.018 0.019 GS Cost min SIR 7.20 7.07 7.09 6.99 7.35 7.32 7.41 7.92 16.62 19.35 19.81 19.02 20.24 19.85 30.70 21.00 27.95 25.86 57.39 20.00 18.66 7.08 7.12 7.02 6.98 GS Const max Blck 0.019 0.017 0.056 0.018 0.019 0.020 0.017 0.019 0.020 0.020 0.019 0.020 0.019 0.020 0.019 0.017 1.4E-5 0.001 0.011 0.020 0.020 0.019 0.017 0.018 0.019 RP Cost min SIR 354.15 475.58 405.14 461.50 409.80 354.70 423.44 416.17 375.77 406.25 397.42 347.04 398.26 358.56 393.00 371.48 354.15 354.15 354.15 354.15 354.15 354.15 354.15 354.15 354.15 RP Const max Blck 0.15 0.32 0.30 0.30 0.25 0.20 0.18 0.14 0.09 0.13 0.12 0.081 0.065 0.068 0.056 0.044 1.4E-5 0.001 0.011 0.04 0.08 0.20 0.26 0.32 0.37 All the problems presented are variations of the base problem. The changed parameter is given in the description column. Same problems are also used in Sections 5.2.1 and 5.2.2. The results are shown in Table 5.5. From problem two to 16, number of cells in the system is increased, meaning that the average lambda is decreased. In problems 17-25, the average lambda is modified. 37 In all the problems, the SA and GS algorithms give similar results, with SA performing better mostly than GS. However, there are times that the GS algorithm produces better results, as in problem 19. The probability of blocking constraint is always kept under the maximum two per cent value by the SA algorithm, resulting in low minimum SIR values, especially in high-density traffic. The GS algorithm isn’t able to satisfy the probability of blocking constraint once in problem three. The RP algorithm gives the best minimum SIR value always, but without taking the probability of blocking constraint into account. That’s why the probability of blocking value is mostly unacceptable, especially in moderate and high-density traffic. However, in low-density traffic, the RP algorithm gives the best results, as in problems 17, 18 and 19, where the probability of blocking of the system is below the maximum two per cent value. 38 6. NETWORK PLANNING TOOL This section explains the usage of NPT, together with some implementation details. Also, in the end of the section, NPT is compared with several commercial mobile network planning tools, such as Asset [41], Fase [42] and CelOptima [43]. 6.1. The Usage The basic usage of NPT, which comprises starting-up NPT, creating the cellular layout, the wired structure, frequencies and the location areas, assigning frequencies to cells by using various methods, and displaying the results is explained in this subsection. NPT is started with executing the nptstart.bat in Windows or nptstart.sh in Unix. Upon start-up, the main window given in Figure 6.1 is displayed. The map that is shown in the background belongs to a part of Kadıköy, in a scale where one centimeter on the map represents approximately 0.22 kilometers in real. The colors on the map represent a total of seven geographical structures, which are mainly roads. 6.1.1. Creating Cells The user can create a cellular layout either manually, by using the menu displayed when the mouse is right-clicked, or automatically, by selecting a layout pattern from the Edit Layout menu, which is recommended. When the user right-clicks on the map, a menu, which is shown in Figure 6.2, is displayed. By choosing the Add Add Cell item from the menu, the user starts drawing a cell, node by node at each left-mouse-click, as shown in Figure 6.3. The triangle in the middle of the cell represents the BTS. However, adding all the cells in this way is rather hard, so NPT gives the user ability to add a number of cells in two built-in patterns, which are hexagonal and rectangular patterns. NPT main window.1. Window displayed when the mouse is right-clicked . zoomed-out to display the entire map Figure 6.39 Figure 6.2. 40 Figure 6.3. Adding a cell manually When the user selects the Edit Layout menu, a sub-menu is displayed, from which the layout pattern can be selected, either Hexagonal Layout, or Manhattan Layout, as given by Figure 6.4. Hexagonal Layout creates hexagonal cells on the map, while selecting Manhattan Layout creates rectangular cells. Both of the items in the Edit Figure 6.5. Layout submenu ask the number of cells, the width- to-height ratio of the cells, and whether to remove cells with zero traffic or not, like in 41 Figure 6.4. Edit Layout menu Figure 6.5. Hexagonal layout window After entering the desired values, NPT creates the cells requested, adjusting their width and height in order to fit them to the map. If removing cells with zero traffic is requested, NPT removes the cells having zero traffic according to the underlying map. When the user selects Hexagonal Layout and wants to create 108 cells with width-toheight ratio equal to one and zero traffic cells removed, the layout shown in Figure 6.6 is get. When the cells are created automatically according to a specific pattern, some of their properties are also calculated with respect to the map below. Call arrival rate (lambda) and call hold time values of each cell are calculated according to the geographical structures that are covered by the cell. NPT removes zero traffic cells according to these values, if requested. 42 Figure 6.6. Cells that are created according to hexagonal pattern NPT gives the user ability to modify the created cells by adding/removing nodes, moving the nodes/edges/cells, splitting cells into two, separating cells from their neighbors or joining them with new neighboring cells. Adding/removing nodes and cells, splitting/separating/joining cells can be done through the popup menu displayed in Figure 6.2. Moving the nodes, edges or cells can be done by dragging the related component with the mouse. It should be noted that moving the nodes, edges or cells also affect the neighboring cells. For instance, when a cell is moved, the nodes common with the neighboring cells also move, thus, the neighboring cells change shape. This is something logical for small For instance. In order to avoid this deformation.7 is divided into two. But when a cell is moved more than its size.1.43 moves.7. NPT also supports umbrella cells. the cells may overlay as in Figure 6.7. and assigned to the two overlaying cells. After enabling the wiring view. That is.8. the layout gets deformed. as shown in . Wiring view is enabled through the View Figure 6. But before all.2. and the traffic figure calculated from the geographic structures under the overlaying cells is divided among the related cells equally. Umbrella cell 6.2. the user can move the mouse to a desired position and add an MSC or BSC via the popup menu given in Figure 6. Creating the Wired Structure. Wiring menu. the related cell should be separated from its neighbors before being moved. MSCs and BSCs NPT allows user to create and display the wired structure of the planned network. BSCs and cells created. since changing the position of cell affects the signal propagation of the neighboring cells.2. through the popup menu given in Figure 6. the wiring view should be enabled in order to see the MSCs. The user can add MSCs and BSCs on the map. Figure 6. the traffic that belongs to the overlaying regions in Figure 6. BSCs or cells are moved. the user can enable all three views as well. that is. even the shapes of the components can be modified through the XML based import/export files. only BTSs are displayed.2.44 Figure 6. if desired. they also move when MSCs. However. When layout view disabled. In order to have a clear display. These links are attached to the network elements. and BSCs are displayed as blue circles. The links between MSCs and BSCs are shown in red. which are told in Section 6. while the links between BSCs and cells are displayed in blue. cells are shown as triangles. without the cell region. as given in Figure 6. View menu MSCs are displayed as red rectangles. that is.8. the map and layout views are disabled in this figure. .9. The colors of the network components and the colors of the links. 45 Figure 6. from the popup menu displayed.10 is displayed. When Add item is selected from the menu displayed. the window shown in Figure 6. . with map and layout views disabled Double-clicking on the MSCs. BSCs and cells display the information window for the related structure. When the user double-clicks on an MSC. When Remove item is selected from the popup menu. the user can link new BSCs to the MSC or remove the existing ones. Viewing the wired structure. where the user can view and modify several properties. a new BSC is added to the table. By right clicking on the BSC table.9. the highlighted BSC is removed from the table. The user can modify an existing BSC by clicking on the BSC Label column and selecting a BSC from the list displayed. MSC information window Double-clicking on a BSC structure displays the BSC information window. cell information window is displayed when a cell is double-clicked. which is shown in Figure 6.11.12. Figure 6.11. The user can link the related cell to a new BSC from this window.46 Figure 6. which is given in Figure 6. .10. The user can change the linked MSC and cells from this window. BSC information window Similarly. In order to disable this feature. Modifying the Properties of Geographical Structures NPT gives user the ability to display and modify the properties of geographical structures that are shown in the map with distinct colors.3. Even if more cells are added to the system. NPT adjusts the lambda weight so that the average lambda stays the same.1. as requested. in order to reach to the requested average lambda. the lambda weight field should be set. Two fields are shown in the window below. which is used in calculating the cell traffic from the geographic structures. one for setting the average lambda and the other for lambda weight. When average lambda field is set. the window shown in Figure 6.47 Figure 6. When Edit menu is selected. When Mobility Classes . Only one of these fields is active at a time.13 is displayed. meaning that the user can either set the average lambda or the lambda weight field. NPT calculates the cell lambdas from the underlying geographic structures such that the average of these lambdas equals to the value given in the field. Cell information window 6. NPT modifies the lambda weight factor.12. Frequency column in Figure 6. in which the properties of the geographic structures are displayed.1.48 lambda weight is given by the user. Geographic structures window The geographic structures window has also a table. When more than one frequency per frequency .14.14 shows the indexes of the frequencies that are present in the relevant frequency group. Creating Frequency Groups Frequencies are grouped by frequency groups in NPT. speed and hold time values of a specific structure via this table. each frequency group has an index. which determines the places of its frequencies on the frequency band. As can be seen in the figure. 6.4. rather than frequencies alone. consequent indexes show frequency groups with adjacent frequencies. Frequency groups are created by using the window shown in Figure 6. NPT calculates the average lambda by using the underlying geographic structures and the lambda weight factor supplied. instead of trying to come up with a specific average lambda value. That is. Figure 6. and frequency groups are assigned to cells. The user can also modify the lambda.13. which is shown in Figure 6.15.1.14. 6.5. the frequencies are assigned to groups in a manner to minimize a possible interference.49 group is requested. selected automatically during creation. Frequency group creation/view window Each frequency group has a color.15. This window is accessible through Edit Channels menu. Figure 6. by coloring the cells according to the frequency groups assigned. are used to display the results of the frequency assignment on the cellular layout. Guard Figure 6. Guard channels window . Modifying Guard Channels NPT allows the user to set the number of guard channels of all the cells from a single window. which can be modified anytime. These colors. as shown in Figure 6. which are simulated annealing. greedy search and reuse pattern algorithms.1. Simulated annealing parameter window .16.16. Edit Assign menu Figure 6.50 6.17. Assigning the Frequency Groups to Cells The user can use one of the three built-in assignment methods while performing the assignment. The assignment method is selected through Edit Assign menu.6. Figure 6. 17. .18. Only one of them can be set at a time. using the Equation (2. the excess frequency groups are not used in the assignment. After entering the desired values. When there are more frequency groups than the cluster size. as given in Figure 6.19. or minimum SIR can be used as the constraint in the SA algorithm. with the given parameters.51 If the user selects simulated annealing. the assignment process starts. a similar window. An important point here is that the number of frequency groups should be equal to or greater than the cluster size calculated by using the switch parameter values entered.3). the window. is displayed.18.19 is automatically updated when new values are entered for the switch parameters. is displayed. the greedy search starts. is displayed to get and display several parameters. Greedy search parameter window When Edit Assign by Reuse Pattern is selected. which is given in Figure 6. shown in Figure 6. After setting the values. which are to be used in the RP algorithm. This is because the algorithm uses cluster size number of frequency groups in assignment. Figure 6. If the user selects greedy search. This window is where values for the switch parameters are entered. a window. Either maximum probability of blocking. The cluster size field in the window given by Figure 6. which are set automatically during creation. can be modified from the location area table displayed.19.20. Reuse pattern parameter window 6. Location area creation/view window . The labels and the colors. which is accessible through the Edit Location Area menu. Creating the Location Areas Location areas are created by the window given in 6.7. shown in Figure 6. they can be assigned to cells via the cell information window. Figure 6.1.20.12.52 Figure 6. Then. 21. while the ones that are not marked are hidden. . as displayed in Figure 6. which is accessible through View Location Areas menu. via the location area details window shown in Figure 6. rather than viewing them all.53 When the location area view is enabled. the user should mark the view checkboxes of the location areas that are desired to be viewed. Figure 6.21. the user can view specific location areas. Assigned location areas are displayed with distinct colors Also. each cell is filled with the color of its location area. The marked location areas are displayed.22. In this window. probability of blocking in each cell can be examined by a color for each value range. Location area details window 6. which are modifiable thorough View Probability of Blocking Classes. again by a distinct color for each value range.54 Figure 6. Viewing the Assignment The assignment can be examined in three different views listed in the View menu shown in Figure 6. in each cell can be displayed. the minimum SIR SIR Classes menu. the frequency groups assigned to cells can be viewed.8. Frequency details window .1.23.22. Second. Third. First.8. These colors and value ranges may be modified via View Figure 6. 24.55 In order to see the assigned frequency groups on the map. Frequencies should be selected. as given in Figure 6.24. they are shown on the map. Then. As the desired frequency groups are marked. View examined and modified through View Probability of Blocking should be selected. first View displayed should be marked from the View displayed in Figure 6. the view checkboxes of the frequency groups that are to be Frequency Details window.23. Assigned frequency groups displayed on map by colors To see the probability blocking results for each cell. which . like Figure 6. The colors that represent probability of blocking value ranges can be Probability of Blocking Classes window. 25. Figure 6.56 is shown in Figure 6.25. The selected probability of blocking classes are viewed on the map as given in Figure 6.26.26. Probability of blocking for each cell is displayed by a color . Probability of blocking classes window Figure 6. Minimum SIR for each cell is displayed by a color . shown in Figure 6. SIR classes window Figure 6.28.27.57 In order to see the minimum SIR for each cell.27. The colors that represent SIR value ranges can be examined and SIR Classes window. Figure 6. View modified via View Signal-to-interference Ratio should be selected. in which the SIR values for all the cells are listed in ascending order. Results The probability of blocking and minimum SIR values for all the cells can also be viewed through the Results menu shown in Figure 6.29.30.1. by the window displayed in Figure 6. Besides viewing the results globally.9.31 is displayed. Cell information window 6. When Results Signal-to-interference Ratio menu item is selected. Figure 6. individual cell information can be examined by doubleclicking on a desired cell. the window shown in Figure 6. .29. The direction of the order can be changed by clicking on the column headers.28.58 The selected SIR classes are viewed on the map as given in Figure 6. SIR results window Figure 6.59 Figure 6.32.31. Probability of blocking results window .30. Results menu Figure 6. and the plus or minus signs next to several tags are the result of the XML interpretation of the browser. as shown in Figure 6.32 is displayed. and all the properties are exposed. The direction of the order can be changed by clicking on the column headers. In the actual XML file. A plus sign tells that the corresponding tag has some hidden properties. and clicking on the sign exposes these properties. In order to save space. the window given in Figure 6. there are no plus or minus signs. XML based import/export file structure . An exported XML file contains all the necessary information to construct the network elements present in the system.2.60 As Results Probability of Blocking menu item is picked. called celllayout. XML NPT allows the user to import/export network data from/to third party planning tools in terms of XML formatted files.33.33. Interacting with Third Party Tools. and then a tag for each network structure. which lists the probability of blocking values for all the cells in descending order. 6. A sample file basically contains a main tag. only the related tags are exposed in the following figures. Figure 6. The figures displayed in this section are the snapshots of a web browser output. The user can import data from or export data to XML files through File Import and File Export menus. frequencies and location areas can be observed. The file shown in Figure 6. frequency groups and location areas exposed .34. Figure 6.61 Frequency groups and contained frequencies are included with freqgroup and frequency tags. to save space. A more detailed view is given in Figure 6.34. where the properties of frequency groups. Frequencies.33 contains two frequencies and two frequency groups. Details of the others are kept closed. respectively. To save space. the details of two nodes of the cell are opened. . while the other is kept closed. Cell properties given in the import/export file Figure 6. while the third node is kept closed. one of the two BSCs is opened.36.35. Figure 6.36 shows the details of the BSC and MSC present in the network. Again.62 Similarly. one of the two cells can be exposed as in Figure 6. 3. the user can add extra parameters to a network component. Extending NPT 6.1. The BSC and MSC exposed 6.36. Adding New Parameters NPT enables the user to add new parameters to network components via the XML based import/export file.3. By using the xtparam tag in the XML file. which are later displayed automatically in the .63 Figure 6. For instance. Figure 6.2. however it requires coding in Java.37. two parameters can be added to a cell.64 component information window. as shown in Figure 6. The system offers two entry points to the optimization algorithms.3. Adding new parameters with xtparam tag 6. The algorithm only calls these two functions during the optimization . Adding New Algorithms Adding a SA like algorithm to the system is easy.37. which are cost() and alter() functions. While coding in Java.3. Implementation Details 6.java can be examined.1. API documentation of the language [45] and the language tutorial [46] is extensively used.algorithms is used [47].4.0 [44]. which can be downloaded from [48]. Hardware and Software Requirements NPT is written by pure java.4. using Java 2 SDK. .java: Implements the dialog used for getting the values of parameters to be used during assignment by SA. for graphical issues. The program is developed and tested on an Intel based machine.4. Development Tools NPT is developed by using Java 2 SDK. AssignBySaDialog. For more details. 6.3. 6. 6. the files that implement the NPT are described briefly. the SimulatedAnnealer. it can run on any operating system supporting Java Runtime environment.4.3.65 process.0. Thus.graphics. Frequently Asked Questions of comp.java: Implements the dialog used for getting the values of parameters to be used during assignment by RP. version 1. • • AssignByPatternDialog. without knowing what is going behind the scenes. The files are sorted according to their names. version 1. with a Java development environment add-on downloaded from [50].2. which has a Pentium III processor operating at 650 Mhz and 128 Mbytes of RAM. together with a GUI framework called JHotDraw. Also. GNU Emacs is used as an editor [49]. Program Files In this section. DataFileFilter.java: Implements the CellPoint object.java: Implements the dialog used in creating.java: Implements the Cell object. Cell. in which the map and the cells are displayed. which is used for representing the probability of blocking and signal-to-interference ratio values with colors.java: Implements a generic dialog used for getting values for specific parameters. which is used for representing the parameters fed into CustomDialog object. which shows the relevant BSC information. CellDialog. modifying and viewing frequency groups. Each cell drawn in the layout frame is a Cell object.java: Implements DialogParam object. Frequency. CustomDialog. BscDialog.java: Implements the cell layout frame.java: Implements the file-filtering object used in the file chooser dialog to filter files according to their extensions. which comprises Frequency objects.java: Implements the dialog displayed when a cell is double-clicked. • • • • • • • • CustomClassDialog.java: Implements the color chooser dialog window. CellLayout. which shows the relevant cell information.java: Implements the BSC object.java: Implements the FrequencyGroup object. . Geom. DialogParam.java: Implements the Frequency object that is comprised by frequency groups. ColorChooser.java: Implements the dialog displayed when a BSC is double-clicked.66 • • • • • • • • • Bsc. CustomClass.java: Implements the CustomClass object. FrequencyGroupDialog.java: Implements the dialog that is used in viewing and modifying CustomClass objects. which are used as nodes of the cells. so that they can be displayed on the map.java: Implements the color renderer to be used when displaying colors in tables. ColorRenderer.java: Implements several geometric functions. FrequencyGroup. CellPoint. manages the geographical structures and sets the mobility parameters of cells. which optimizes a given function using simulated annealing. Util. MobilityMap.java: Implements the interface that is used by the SimulatedAnnealer object. .java: Implements several utility functions used. which shows the relevant MSC information.java: Implements the TableSorter object used for sorting tables according to a specific column. • • • • • • SirResultDialog. Planner.java: Implements the generic interface that cells.java: Implements the location area object.java: Implements the dialog that is used for displaying the probability of blocking results for all the cells. modifying and viewing location areas.67 • • • • • • • • • • • LocationArea.java: The file that comprises the main class of the program. NetworkElement. ProbBlockingResultDialog. TableMap: Implements the base class of TableSorter object.java: Implements the dialog used in displaying the minimum signal-to-interference ratio for each cell. which reads the mobility file. MscDialog.java: Implements the dialog used in viewing the assigned location areas. which is used to represent the geographical structures comprised in the mobility map.java: Implements the MSC object. Msc. BSCs and MSCs should implement.java: Implements the MobilityClass object. It implements the main NPT window. Objects that want to use the SimulatedAnnealer object should implement this interface. ViewFrequencyGroupDialog. SimulatedAnnealerClient. ViewLocationAreaDialog.java: Implements the MobilityMap object.java: Implements the SimulatedAnnealer object. menu bar and contains the cell layout frame.java: Implements the dialog used in viewing the assigned frequency groups. TableSorter. LocationAreaDialog. SimulatedAnnealer.java: Implements the dialog displayed when a MSC is double-clicked. MobilityClass.java: Implements the dialog used in creating. Import/export via XML. Fast predictions using modified Hata propagation Model. It has the following list of features [42]: • • • • • Grouping voice channels into frequency groups. Overview of the Commercial Products Mainly. Integrated dual band planning. Cost and interference based planning. PCS. TDMA and TETRA. three network planning tools are investigated while developing NPT. is a suite of programs for the frequency planning of AMPS and GSM cellular mobile networks.java: Implements the XtParam object used for adding new parameters to network elements via XML based import/export files. NPS/X import/export. Specifying frequencies as voice or control. which is the acronym of Frequency Assignment by Stochastic Evolution.1. Comparison with Commercial Products 6. Coverage export to Intergraph and MapInfo. Optimized frequency plan.5.68 • XtParam. Defining underlay/overlay cells.5. Macro and Microcell Models. EGPRS. which are Asset [41]. Asset is advertised as the network planning and information management tool. . User friendly traffic and neighbor planning. HSCSD. FASE. 6. Displaying the sorted list of cells currently suffering the worst SIR. FASE [42] and CelOptima [43]. Optimization for all co-channel and adjacent channel interfaces. AMPS. ECSD. GPRS. having the following features [41]: • • • • • • • • • • Full support and planning capabilities for GSM. . FASE expects to read a cell-to-cell interference table provided from Telstra’s own signal propagation modeling package [42].e.2. Also. Using four propagation models through the companion tool CelPlanner. like NPT does. as listed in the product description given in [43]: • • • • Minimization of the overall interference. cell coordinates) supplied by the user when calculating the signal interferences.. called stochastic evolution. Asset uses a heuristic in which local minima are avoided by making big changes in the frequency allocation when the cost cannot be decreased furthermore [41]. which is the companion product of CelOptima. FASE uses a variation of SA. CelPlanner. Additional to using several models like Hata and diffraction models. it can also accept cell coordinates generated by a third party tool. CelOptima. FASE displays the value of the objective function as solution proceeds. TMR and TOM files [41]. 6. a Microcell model and the Line-of-sight model [43]. as a cost function curve [42]. Rhode & Schwarz. Comparison First of all. and focuses on the worst interference signals. such as a propagation analyzer. like NPT. commercial products use signal interference data provided by RF propagation analyzer tools. This is not supported currently by NPT. try to minimize the cost using some kind of optimization algorithm. Asset can import data from SIGNIA. which is another planning tool. which are a modified Lee-Picquenard based model.69 • Displaying the objective function value as solution proceeds. has the following features. Grayson. a Korowajczuk-Picquenard model. Factoring the geographic traffic distribution into the optimization solution so that any potential interference is restricted to areas with the least traffic. provides four RF propagation models. NPT does only use the graphical data (i. Commercial products.5. However. and work with the data supplied. Optimization of CDMA systems. Moreover. However. together with co-channels. XML is the only way NPT exchanges data with third party tools. the commercial planning tools do calculate adjacent channel interferences. Adding this feature to NPT is planned as a future work. Asset also supports XML as an import/export method. which is another network planner tool from Nokia. and does not take the adjacent channels into account for the time being. NPT does not make such a distinction between frequencies. On the other hand. Asset can import/export data from/to third party tools. It can exchange data with NPS/X. FASE gives user the opportunity to specify frequencies as voice or control and assign these frequencies together or separately [42]. . it can output its coverage results to Intergraph or MapInfo. as in NPT [42]. FASE gives the user ability to group voice channels into frequency groups. as does NPT [41].70 NPT calculates the co-channel interferences only. For example. The values of these fields are only displayed in the information windows of the corresponding components. which is known as the frequency assignment problem (FAP) in the literature. This introduces an optimization problem. GS and RP algorithms. we enabled the tool to import/export XML based files so that it can import design results of other programs that do not support graphics. we tried to come up with a graphical network planning tool. together with the wired structure. Out of these three. mostly concerning the graphical part. The tool. uses three algorithms when assigning frequencies to cells. CONCLUSION AND FUTURE WORK While designing a cellular network. SA outperforms the others in most of the cases. First of all. the tool can also be used as a graphical presentation program for other design tools that concentrate on the design problem itself rather than its presentation. in order to display the results graphically and export design data to other tools. by which the user will be able to create a wireless cellular network. Thus. Additionally. while the signal quality is kept over an acceptable level. In this thesis. which are SA. Furthermore. examine the results of the assignment performed and view the wired structure created. namely NPT. available frequencies should be assigned to cells in such a manner that the traffic requirements of each cell should be satisfied. adding new fields to network components without changing the program code can be enhanced. which will give a better intuition to the user. make frequency assignment both manually and automatically using a specific algorithm. but displaying the values of the added fields via colorization is not possible. drawing cells can be simplified by adding a copy paste utility. . Progress of the optimization algorithm can be displayed as a chart also. Currently. Several enhancements can be performed on NPT.71 7. new fields are added through XML based import/export files. T. Painton. Morgan. “Optimization Using Simulated Annealing”.pdf. Wagner. pp. 2001. A. J. 8. Rumeli Telekom Grubu. 1995. The GSM System for Mobile Communications. T. 1999. 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