A Bayesian approach for PAPR and MUI reduction in OFDM-based massive MIMO systems

  • Abstract
  • Keywords
  • References
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  • Abstract

    Orthogonal frequency division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems face the issues of high peak-to-average power ratio (PAPR) and multiuser interference (MUI) which significantly affect their performance. The solution lies in finding an OFDM-modulated signal that possesses a low PAPR and also enables MUI cancellation. In this paper, a com-parative analysis has been performed based on a Bayesian PAPR reduction algorithm. This method models the problem into a hierarchical truncated Gaussian mixture prior (TGM) model which makes use of the redundant degrees of freedom of the transmission array. This leads to a low PAPR signal as most of the samples are concentrated on the boundaries. A variational expectation-maximization (EM) tactic is incorporated to obtain an estimate of the hyperparameters. This is followed by the implementation of the generalized approximate message passing (GAMP) algorithm to reduce the complexity of computation. MATLAB simulations show a significant improvement in PAPR reduction and MUI cancellation with this Bayesian approach leading to better power efficiency and system performance.


  • Keywords

    Bayesian Learning; EM; GAMP; Massive MIMO; MUI; OFDM; PAPR; TGM.

  • References

      [1] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, and F. Tufvesson, “Scaling up MIMO: Opportunities and challenges with very large arrays,” IEEE Signal Process. Mag., vol. 30, no. 1, pp. 40–60, Jan. 2013. https://doi.org/10.1109/MSP.2011.2178495.

      [2] G. Wunder, R. F. Fischer, H. Boche, S. Litsyn, and J. No, “The PAPR problem in OFDM transmission: New directions for a long-lasting problem,” IEEE Signal Process. Mag., vol. 30, no. 6, pp. 130–144, Jan. 2014. https://doi.org/10.1109/MSP.2012.2218138.

      [3] T. Jiang and Y. Wu, “An overview: Peak-to-average power ratio reduction techniques for OFDM signals,” IEEE Trans. Broadcasting, vol. 54, no. 2, pp. 257–268, Jun. 2008. https://doi.org/10.1109/TBC.2008.915770.

      [4] S. H. Han and J. H. Lee, “An overview of peak-to-average power ratio reduction techniques for multicarrier transmission,” IEEE Wireless Commun., vol. 12, no. 2, pp. 56–65, Apr. 2005. https://doi.org/10.1109/MWC.2005.1421929.

      [5] Hengyao Bao, Jun Fang, Zhi Chen, Hongbin Li, Senior Member, IEEE, and Shaoqian Li” An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems, IEEE Transactions on Wireless Communications, 15(6), 4183-4195. https://doi.org/10.1109/TWC.2016.2536662.

      [6] S. Rangan, “Generalized approximate message passing for estimation with random linear mixing,” IEEE International Symposium, Information Theory Proceedings (ISIT), pp. 2168- 2172, Aug. 2011. https://doi.org/10.1109/ISIT.2011.6033942.

      [7] J. Tellado, “Peak to average power reduction for multi-carrier modulation”, PhD thesis, Stanford University, 2000.

      [8] C. L. Wang, S. S. Wang, & H. L. Chang, “A low-complexity SLM based PAPR reduction scheme for SFBC MIMO-OFDM systems”, Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1449-1453, 2011. https://doi.org/10.1109/WCNC.2011.5779373.

      [9] Y. L. Lee, Y. H. You, W. G. Jeon, J. H. Paik, & H. K. Song, “Peak-to-average power ratio in MIMO-OFDM systems using selective mapping”, IEEE Communications letters, 7(12), 575-577, 2003. https://doi.org/10.1109/LCOMM.2003.821329.

      [10] S. J. Ku, C. L. Wang, & C. H. Chen, “A reduced-complexity PTS-based PAPR reduction scheme for OFDM systems”, IEEE Transactions on Wireless Communications, 9(8), 2455-2460, 2010. https://doi.org/10.1109/TWC.2010.062310.100191.

      [11] S. A. Adegbite, S. G. McMeekin, & B. G. Stewart, “A PAPR reduction and data decoding for SLM based OFDM systems without SI”, Vehicular Technology Conference (VTC Spring), IEEE, pp. 0001-0005, May 2015. https://doi.org/10.1109/VTCSpring.2015.7145638.

      [12] A. Thakur & N. Dhillon, “Hybrid Approach using SLM and PTS Techniques to Reduce PAPR”, International Journal of Science and Research (IJSR), Vol. 4, Issue 5, pp. 173-177, May 2015.

      [13] K. Padarti & N. Venkateswara, “A novel method for joint PAPR mitigation in OFDM based massive MIMO downlink systems”, International Journal of Engineering & Technology (IJET), 7 (3), pp. 1185- 1188, 2018. https://doi.org/10.14419/ijet.v7i3.13009.

      [14] S. H. Han and J. H. Lee, “An overview of peak-to-average power ratio reduction techniques for multicarrier transmission,” IEEE Wireless Commun., Vol. 12, No. 2, pp. 56–65, Apr. 2005. https://doi.org/10.1109/MWC.2005.1421929.

      [15] K. Prasanna K, “Reducing the PAPR of OFDM Based MIMO Systems Using Bayesian Approach”, International Journal of Advanced Technology and Innovative Research, Vol.09, Issue 06, pp. 0958-0963, May 2017.

      [16] C. Studer and E. G. Larsson, “PAR-aware large-scale multi-user MIMO OFDM downlink,” IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 303–313, Feb. 2013. https://doi.org/10.1109/JSAC.2013.130217.

      [17] J. Chen, C. Wang, K. Wong, and C. Wen, “Low-complexity precoding design for massive multiuser MIMO systems using approximate message passing,” IEEE Trans. Vehicular Technology, vol. PP, no. 99, pp. 1–8, Jul. 2015.

      [18] R. W. B¨auml, R. F. Fischer, and J. B. Huber, “Reducing the peak-to-average power ratio of multicarrier modulation by selected mapping,” IEE Elec. Letters, Vol. 32, No. 22, pp. 2056–2057, Oct. 1996. https://doi.org/10.1049/el:19961384.

      [19] C. Tellambura, “Computation of the continuous-time PAR of an OFDM signal with BPSK subcarriers,” IEEE Commun. Lett., vol. 5, no. 5, pp. 185–187, May 2001. https://doi.org/10.1109/4234.922754.

      [20] M. Tipping, “Sparse Bayesian learning and the relevance vector machine,” Journal of Machine Learning Research, vol. 1, pp. 211–244, 2001.

      [21] D. G. Tzikas, A. C. Likas, and N. P. Galatsanos, “The variational approximation for Bayesian inference,” IEEE Signal Process. Mag., vol. 25, no. 6, pp. 131–146, Jan. 2008. https://doi.org/10.1109/MSP.2008.929620.

      [22] Q. Guo, D. Huang, S. Nordholm, J. Xi, and Y. Yu, “Iterative frequency domain equalization with generalized approximate message passing,” IEEE Signal Process. Lett., vol. 20, no. 6, pp. 559–562, Jun. 2013. https://doi.org/10.1109/LSP.2013.2256783.

      [23] J. Vila and P. Schniter, “Expectation-maximization Gaussian-mixture approximate message passing,” IEEE Trans. Signal Process., vol. 61, no. 19, pp. 4658–4672, Oct 2013. https://doi.org/10.1109/TSP.2013.2272287.

      [24] M. Abramowitz and I. A. Stegun, Handbook of mathematical functions: with formulas, graphs, and mathematical tables. New York: Dover Publication, 1965. https://doi.org/10.1115/1.3625776.

      [25] J. Vila, P. Schniter, S. Rangant, F. Krzakala, and L. Zdeborovd, “Adaptive damping and mean removal for the generalized approximate message passing algorithm,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), South Brisbane, QLD, pp. 2021-2025, Apr. 19-24, 2015. https://doi.org/10.1109/ICASSP.2015.7178325.




Article ID: 19427
DOI: 10.14419/ijet.v7i4.19427

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