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http://dx.doi.org/10.4218/etrij.2018-0190

Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO-OFDM systems  

Singh, Davinder (Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology)
Sarin, Rakesh Kumar (Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology)
Publication Information
ETRI Journal / v.41, no.3, 2019 , pp. 298-307 More about this Journal
Abstract
This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.
Keywords
multiuser MIMO-OFDM; PAPR reduction; sparse Bayesian leaning (SBL);
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1 E. Manasseh, S. Ohno, and M. Nakamoto, Combined channel estimation and PAPR reduction technique for MIMO-OFDM systems with null subcarriers, EURASIP J. Wirel. Commun. Netw. 2012 (2012), no. 1, 1-15.   DOI
2 H. B. Jeon, J.-S. No, and D.-J. Shin, A low‐complexity SLM Scheme using additive mapping sequences for PAPR Reduction of OFDM signals, IEEE Trans. Broadcast. 57 (2011), no. 4, 866-875.   DOI
3 S.-J. Ku, Low‐complexity PTS‐based schemes for PAPR reduction in SFBC MIMO‐OFDM systems, IEEE Trans. Broadcast. 60 (2014), no. 4, 650-658.   DOI
4 C. Studer and E. G. Larsson, PAR‐aware large‐scale multi‐user MIMO OFDM downlink, IEEE J. Sel. Areas Commun. 31 (2013), no. 2, 303-313.   DOI
5 D. L. Donoho, Compressed sensing, IEEE Trans. Inform. Theory 52 (2006), no. 4, 1289-1306.   DOI
6 B. Ebrahim et al., Peak reduction and clipping mitigation in OFDM by augmented compressive sensing, IEEE Trans. Sig. Process. 60 (2012), no. 7, 3834-3839.   DOI
7 B. Liu et al., A low‐complexity compressive sensing algorithm for PAPR reduction, Wireless Pers. Commun. 78 (2014), no. 1, 283-295.   DOI
8 M. E. Tipping, Sparse Bayesian learning and the relevance vector machine, J. Mach. Learn. Res 1 (2001), no. 1, 211-244.
9 T. T. Ballen, J. A. Tropp, and A. C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit, IEEE Trans. Inform. Theory 53 (2007), no. 2, 4655-4666.   DOI
10 H. Ochiai and H. Imai, On the distribution of the peak‐to‐average power ratio in OFDM signals, IEEE Trans. Commun. 49 (2001), no. 2, 282-289.   DOI
11 T. L. Marzetta, Noncooperative cellular wireless with unlimited numbers of base station antennas, IEEE Trans. Wireless Commun. 9 (2010), no. 11, 3590-3600.   DOI
12 B. M. Hochwald, T. L. Marzetta, and V. Tarokh, Multiple‐antenna channel hardening and its implications for rate feedback and scheduling, IEEE Trans. Info. Theory 50 (2004), no. 9, 1893-1909.   DOI
13 L. Wang and C. Tellambura, A simplified clipping and filtering technique for PAR reduction in OFDM systems, IEEE Sig. Process. Lett. 12 (2005), no. 6, 453-456.   DOI
14 L. Lu et al., Overview of massive MIMO: Benefits and challenges, IEEE J. Sel. Topics in Sig. Process. 8 (2014), no. 5, 742-758.   DOI
15 G. Wunder et al., The PAPR problem in OFDM transmission: New directions for a long‐lasting problem, IEEE Sig. Process. Mag. 30 (2013), no. 6, 130-144.   DOI
16 M. J. Hao and C. H. Lai, Precoding for PAPR reduction of OFDM signals with minimum error probability, IEEE Trans. Broadcast. 56 (2010), no. 1, 120-128.   DOI
17 L. Yang et al., PAPR reduction of an OFDM signal by use of PTS with low computational complexity, IEEE Trans. Broadcast. 52 (2006), no. 1, 83-86.   DOI
18 J. C. Chen, M. H. Chiu, and Y. S. Yang, A suboptimal tone reservation algorithm based on cross‐entropy method for PAPR reduction in OFDM systems, IEEE Trans. Broadcast. 57 (2011), no. 3, 752-756.   DOI
19 J. Yang et al., A modified selected mapping technique to reduce the peak‐to‐ average power ratio of OFDM signal, IEEE Trans. Consumer Electr. 53 (2007), no. 3, 846-851.   DOI
20 E. J. Candes and T. Tao, Decoding by linear programming, IEEE Trans. Inform. Theory 51 (2005), no. 12, 4203-4215.   DOI
21 J. P. Vila and P. Schniter, Expectation‐maximization gaussian‐mixture approximate message passing, IEEE Trans. Sig. Process. 61 (2013), no. 19, 4658-4672.   DOI
22 S. Som and P. Schniter, Compressive imaging using approximate message passing and a Markov‐tree prior, IEEE Trans. Sig. Process. 60 (2012), no. 7, 3439-3448.   DOI
23 M. Joham, W. Utschick, and J. Nossek, Linear transmit processing in MIMO communications systems, IEEE Trans. Sig. Process. 53 (2005), no. 8, 2700-2712.   DOI
24 J. Fang, L. Zhang, and H. Li, Two‐dimensional pattern‐coupled sparse Bayesian learning via generalized approximate message passing, IEEE Trans. Image Process. 25 (2016), no. 6, 2920-2930.   DOI
25 H. Bao et al., An efficient Bayesian PAPR reduction method for OFDM‐ based massive MIMO systems, IEEE Trans. Wireless Commun. 15 (2016), no. 6, 4183-4195.   DOI
26 H. Duan et al., Fast inverse‐free sparse Bayesian learning via relaxed evidence lower bound maximization, IEEE Sig. Process. Lett. 24 (2017), no. 6, 774-778.   DOI
27 C. Studer et al., Democratic representations, ArXiv preprint (2015), arXiv: 1401.3420.
28 D. G. Tzikas, A. C. Likas, and N. P. Galatsanos, The variational approximation for Bayesian inference, IEEE Sig. Process. Mag. 25 (2008), no. 6, 131-146.   DOI
29 J. P. Vila and P. Schniter, An empirical‐bayes approach to recovering linearly constrained non‐negative sparse signals, IEEE Trans. Sig. Process. 62 (2014), no. 18, 4689-4703.   DOI
30 IEEE 802.11 Working Group, Part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications, amendment 5: Enhancements for higher throughput, IEEE, 2009.
31 B. S. Krongold and D. L. Jones, PAR reduction in OFDM via active constellation extension, IEEE Trans. Broadcast. 49 (2003), no. 3, 258-268.   DOI
32 M. Iwasaki and K. Higuchi, Clipping and filtering-based PAPR reduction method for precoded OFDM-MIMO signals, in Proc. IEEE 71st Veh. Tech. Conf., Taipei, Taiwan, 2010, pp. 1-5.