FITRA- A New Algorithm for MU-MIMO OFDM for Reducing PAR



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IJOSAT Vol.2, Issue 2, Oct - Dec 2014 ISSN: 0976-7578 (Online) FITRA - A New Algorithm For MU-MIMO OFDM For Reducing PAR 1 Kottugumada Sravani, 2T.Lakshman Rao, Student of M.Tech, Dept. of Electronics and Communication Engineering, SISTAM, Srikakulam 2 Assistant Professor, Dept. of Electronics and communications Engineering, SISTAM, Srikakulam 1 [email protected], [email protected] 1 Abstract: Orthogonal frequency division multiplexing (OFDM) is an optimal solution for transmitting multiple signals within a limited bandwidth for large scale Multiple Input Multiple Output. Multiuser multi input multiple output systems can use orthogonal frequency division multiplexing for better transmission rates within a limited amount of time for higher throughputs and improved quality of service for future generations. Usage of OFDM led to peak to average power ratio, not apposite for a healthy signal, The signal merges out of the limited bandwidth because of PAR .This Paper discusses about a new algorithm FITRA-a fast iterative truncation Algorithm to reduce the PAR for an MU-MIMO OFDM system for PAR reduction which can be implemented by using Convex Optimization by using SVD method. Keywords: FITRA, MU-MIMO OFDM, OFDM, PAR, SVD. Orthogonal Frequency Division Multiplexing (OFDM) is a scheme used in the area of high-data-rate mobile wireless communications such as cellular phones, satellite communications and digital audio broadcasting. This technique is mainly utilized to combat inter-symbol interference. www.ijosat.co.in I. Introduction: The growing demand of multimedia services and the growth of Internet related contents lead to increasing interest to high speed communications. The requirement for wide bandwidth and flexibility imposes the use of efficient transmission methods that would fit to the characteristics of wideband channels especially in wireless environment where the channel is very challenging. In wireless environment the signal is propagating from the transmitter to the receiver along number of different paths, collectively referred as multipath. While propagating the signal power drops of due to three effects: path loss, macroscopic fading and microscopic fading. Fading of the signal can be mitigated by different diversity techniques. To obtain diversity, the signal is transmitted through multiple (ideally) independent fading paths e.g. in time, frequency or space and combined constructively at the receiver II. MU MIMO OFDM: Large scale multiple-input multipleoutput (MIMO) wireless communication is a promising means to meet the growing demands for higher throughput and improved quality-of-service of nextgeneration multi-user (MU) wireless communication systems. The vision is that a large number of antennas at the base-station (BS) would serve a large number of users concurrently and in the same frequency __________________________________________________ International Journal of Science And Technology IJOSAT Vol. 2, Issue 2, Oct - Dec 2014 band, but with the number of BS antennas being much larger than the number of users, say a hundred antennas serving ten users. Large-scale MIMO systems also have the potential to reduce the operational power consumption at the transmitter and enable the use of low-complexity schemes for suppressing MU interference (MUI). All these properties render large-scale MIMO a promising technology for next-generation wireless communication systems. While the theoretical aspects of large-scale MU-MIMO systems have gained significant attention in the research community, e.g., , much less is known about practical transmission schemes. As pointed out in, practical realizations of large-scale MIMO systems will require the use of low cost and low-power radio-frequency (RF) components. To this end, reference proposed a novel MU pre-coding scheme for frequency-flat channels, which relies on perantenna constant envelope (CE) transmission to enable efficient implementation using non-linear RF components. Moreover, the CE pre-coder of forces the peak-to-average (power) ratio (PAR) to unity, which is not necessarily optimal as in practice there is always a trade-off between PAR, error-rate performance, and power amplifier efficiency. Practical wireless channels typically exhibit frequency selective fading and a low-PAR pre-coding solution suitable for such channels would be desirable. Preferably, the solution should be such that the complexity required in each (mobile) terminal is small (due to stringent area and power constraints), whereas heavier processing could be afforded at the BS. Orthogonal frequency-division multiplexing (OFDM) is an efficient and well-established way of dealing with frequency selective channels. In addition to simplifying the equalization at the receiver, OFDM also www.ijosat.co.in ISSN: 0976-7578 (Online) facilitates per-tone power and bit allocation, scheduling in the frequency domain, and spectrum shaping. However, OFDM is known to suffer from a high PAR, which necessitates the use of linear RF components (e.g., power amplifiers) to avoid out-of-band radiation and signal distortions. Unfortunately, linear RF components are, in general, more costly and less power efficient than their non-linear counterparts, which would eventually result in exorbitant costs for large-scale BS implementations having hundreds of antennas. Therefore, it is of paramount importance to reduce the PAR of OFDMbased large-scale MU-MIMO systems to facilitate corresponding low-cost and lowpower BS implementations. To combat the challenging linearity requirements of OFDM, a plethora of PAR-reduction schemes have been proposed for point-topoint single-antenna and MIMO wireless systems, e.g. For MU-MIMO systems, however, a straightforward adaptation of these schemes is non-trivial, mainly because MU systems require the removal of MUI using a pre-coder. PAR-reduction schemes suitable for the MU-MISO and MU-MIMO downlink were described in and, respectively, and rely on Tomlinson-Harashima pre-coding. Both schemes, however, require specialized signal processing in the (mobile) terminals (e.g., modulo reduction), which prevents their use in conventional MIMO-OFDM systems, such as IEEE 802.11n or 3GPP LTE. Fig 1.a Trasmitting Section of large scale Multi user MIMO OFDM __________________________________________________ International Journal of Science And Technology IJOSAT Vol. 2, Issue 2, Oct - Dec 2014 Fig 2. Receiving section of fig 1 III.aSingular Value Decomposition: ISSN: 0976-7578 (Online) However, the eigen value decomposition and the singular value decomposition differ for all other matrices M: the eigen value decomposition is where U is not necessarily unitary and D is not necessarily positive semi-definite, while the SVD is where Σ is a diagonal positive semi-definite, and U and V are unitary matrices that are not necessarily related except through the matrix M. The singular value decomposition is very general in the sense that it can be applied to any mxn matrix whereas eigen value decomposition can only be applied to certain classes of square matrices. Nevertheless, the two decompositions are related. Given an SVD of M, as described above, the following two relations hold The right-hand sides of these relations describe the eigen value decompositions of the left-hand sides. Consequently: The columns of V (right-singular vectors) are eigenvectors of .The columns of U (left-singular vectors) are eigenvectors of .The non-zero elements of Σ (non-zero singular values) are the square roots of the non-zero eigen values of or . Algorithm-I Fast Iterative Truncanation algorith1.intilize In the special case that M is a normal matrix, which by definition must be square, the spectral theorem says that it can be unitarily diagonalized using a basis of eigenvectors, so that it can be written for a unitary matrix U and a diagonal matrix D. When M is also positive semi-definite, the decomposition is also singular value decomposition. IV. Outputs: www.ijosat.co.in xo← 0 𝑁𝑥1, 𝑦1 ← 𝑥𝑜, 𝑡1 ← 1, L←SVD(H) In the above Algorithm 1 the first step which is used to minimize Lipstichz constant is replaced by the above equation which uses singular value decomposition and it is the modified version of FITRA algorithm termed as Modified FITRA. Fig IV.1 PAR of LS, LS clip, MF,PMP __________________________________________________ International Journal of Science And Technology IJOSAT Vol. 2, Issue 2, Oct - Dec 2014 ISSN: 0976-7578 (Online) V. Conclusion: Fig IV.2 OFDM tone Index vs Spectrum In this paper, we analyzed the PAPR of MUI MIMO OFDM with different precoding Techniques like DHT-Pre-coded OFDM system for M-QAM (where M=16, 32, 64, 256). Matlab simulation shows that SVD-& FITRA Pre-coded OFDM System shows better PAPR gain as compared to other OFDM-Original systems, Thus, it is concluded that SVD Pre-coder Based OFDM System shows better PAPR reduction then other Pre-coder Based OFDM Systems with MU MIMO VI. References: Fig IV.3 PAR vs CCDF [1]. PAR aware large scale Multi user mimo Ofdm systems downlink IEEE 2013 [2] C. Studer and E. G. Larsson, “PARaware multi-user pre-coder for the largescale MIMO-OFDM downlink,” in Proc. of the 9th International Symposium on Wireless Communication Systems (ISWCS), Paris, France, August 2012. Fig IV.4 SNR vs Average SER Similarly the outputs using SVD are Fig IV.5 PAR of LS, LS clip, MF, PMP [3] F. Rusek, D. PerssonS, B. K. Lau, E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, “Scaling up MIMO: opportunities and challenges with very large arrays,” arXiv:1201.3210v1, Jan. 2012. [4] T. L. Marzetta, “Non-cooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Comm., vol. 9, no. 11, pp. 3590– 3600, Nov. 2010. [5]“How much training is required for multiuser MIMO?” in Proc. 40th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, USA, Oct. 2006, pp. 359–363. Fig IV.6 PAR of LS, LS clip, MF, PMP with SVD www.ijosat.co.in __________________________________________________ International Journal of Science And Technology IJOSAT Vol. 2, Issue 2, Oct - Dec 2014 [6] J. Hoydis, S. ten Brink, and M. Debbah, “Massive MIMO: How many antennas do we need?” in Proc. IEEE 49th Ann. Allerton Conf. on Comm. Control, and Computing (Allerton), Monticello, IL, USA, Sept. 2011, pp. 545–550. [7] H. Q. Ngo, E. G. Larsson, and T. L. Marzetta, “Energy and spectral efficiency of very large multiuser MIMO systems,” arXiv:1112.3810v1, Dec. 2011. ISSN: 0976-7578 (Online) average power ratio reduction in MIMO OFDM,” IEE Elec. Letters, vol. 42, no. 2, pp. 1289–1290, Oct. 2006. [15] J. Illic and T. Strohmer, “PAPR reduction in OFDM using Kashin’s representation,” in Proc. IEEE 10th Workshop on Sig. Proc. Advances in Wireless Comm. (SPAWC), Perugia, Italy, June 2009, pp. 444–448. [8] S. K. Mohammed and E. G. 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Hoch, “Directed selected mapping for peakto- www.ijosat.co.in __________________________________________________ International Journal of Science And Technology
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