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Large Scale MIMO Analysis Using Enhanced LAMA

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Abstract

In this research work, we propose a enhanced large scale multi-input multi-output (MIMO) approximate message passing (LAMA) based optimal data detector for large scale MIMO systems. Existing LAMA and sub-optimal detection techniques suffer from iteration complexity and performance loss in finite dimensional systems due to large scale user fading. To overcome these, Gram matrix and message damping techniques are incorporated in the traditional LAMA. The effectiveness of the proposed enhanced LAMA and existing techniques are analyzed with 64, 32 and 16 user antennas, 256, 128, 64 and 16 base station elements with 64QAM, 16QAM, QPSK and BPSK. The simulation results show that the proposed enhanced LAMA gives better performance when compared to existing matrix inversion methods such as Gauss-Seidel and Neumann, box techniques such as optimal co-ordinate descent and alternating direction method of multipliers based on the infinity norm, minimum mean square error and LAMA.

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References

  1. Khoso, I. A., Zhang, X., & Shaikh, A. H. (2020). Low-complexity signal detection for large-scale mimo systems with second-order richardson method. Electronics Letters, 56, 467–469.

    Article  Google Scholar 

  2. Challa, N. R., & Bagadi, K. (2021). Design of large scale mu-mimo system with joint precoding and detection schemes for beyond 5g wireless networks. Wireless Personal Communications, 121, 1627.

    Article  Google Scholar 

  3. Cheng, Z., Liao, B., He, Z., & Li, J. (2019). Transmit signal design for large-scale mimo system with 1-bit dacs. IEEE Transactions on Wireless Communications, 18, 4466–4478.

    Article  Google Scholar 

  4. Chataut, R., & Akl, R. (2020). Massive mimo systems for 5g and beyond networks-overview, recent trends, challenges, and future research direction. Sensors, 20, 2753.

    Article  Google Scholar 

  5. Priya, T. S., Manish, K., & Prakasam, P. (2021). Hybrid beamforming for massive mimo using rectangular antenna array model in 5g wireless networks. Wireless Personal Communications, 120, 2061.

    Article  Google Scholar 

  6. Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). What will 5g be? IEEE Journal on selected areas in communications, 32, 1065–1082.

    Article  Google Scholar 

  7. Björnson, E., Sanguinetti, L., Wymeersch, H., Hoydis, J., & Marzetta, T. L. (2019). Massive mimo is a reality-what is next? Five promising research directions for antenna arrays. Digital Signal Processing, 94, 3–20.

    Article  Google Scholar 

  8. Albreem, M. A., Juntti, M., & Shahabuddin, S. (2019). Massive mimo detection techniques: A survey. IEEE Communications Surveys & Tutorials, 21, 3109–3132.

    Article  Google Scholar 

  9. Kisialiou, M., Luo, X., & Luo, Z.-Q. (2009). Efficient implementation of quasi-maximum-likelihood detection based on semidefinite relaxation. IEEE Transactions on Signal Processing, 57, 4811–4822.

    Article  MathSciNet  Google Scholar 

  10. Jiang, Y., Varanasi, M. K., & Li, J. (2011). Performance analysis of zf and mmse equalizers for mimo systems: An in-depth study of the high snr regime. IEEE Transactions on Information Theory, 57, 2008–2026.

    Article  MathSciNet  Google Scholar 

  11. Wu, M., Yin, B., Wang, G., Dick, C., Cavallaro, J. R., & Studer, C. (2014). Large-scale mimo detection for 3g pp lte: Algorithms and fpga implementations. IEEE Journal of Selected Topics in Signal Processing, 8, 916–929.

    Article  Google Scholar 

  12. Dai, L., Gao, X., Su, X., Han, S., Chih-Lin, I., & Wang, Z. (2014). Low-complexity soft-output signal detection based on gauss-seidel method for uplink multiuser large-scale mimo systems. IEEE Transactions on Vehicular Technology, 64, 4839–4845.

    Article  Google Scholar 

  13. Hu, Y., Wang, Z., Gaol, X., & Ning, J. Low-complexity signal detection using cg method for uplink large-scale mimo systems. In 2014 IEEE International Conference on Communication Systems, IEEE (pp. 477–481).

  14. Vardhan, K. V., Mohammed, S. K., Chockalingam, A., & Rajan, B. S. (2008). A low-complexity detector for large mimo systems and multicarrier cdma systems. IEEE Journal on Selected Areas in Communications, 26, 473–485.

    Article  Google Scholar 

  15. Shahabuddin, S. (2019). MIMO detection and precoding architectures, Ph.D. thesis, University of Oulu.

  16. Castaneda, O., Goldstein, T., & Studer, C. (2016). Data detection in large multi-antenna wireless systems via approximate semidefinite relaxation. IEEE Transactions on Circuits and Systems I: Regular Papers, 63, 2334–2346.

    Article  Google Scholar 

  17. Wu, M., Dick, C., Cavallaro, J. R., & Studer, C. (2016). High-throughput data detection for massive mu-mimo-ofdm using coordinate descent. IEEE Transactions on Circuits and Systems I: Regular Papers, 63, 2357–2367.

    Article  Google Scholar 

  18. Shahabuddin, S., Juntti, M., & Studer, C. Admm-based infinity norm detection for large mu-mimo: Algorithm and vlsi architecture. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE (pp. 1–4).

  19. Donoho, D. L., Maleki, A., & Montanari, A. (2009). Message-passing algorithms for compressed sensing. Proceedings of the National Academy of Sciences, 106, 18914–18919.

    Article  Google Scholar 

  20. Zhu, J., Yuan, Q., Song, C., & Xu, Z. (2019). Phase retrieval from quantized measurements via approximate message passing. IEEE Signal Processing Letters, 26, 986–990.

    Article  Google Scholar 

  21. Kamilov, U. S., Goyal, V. K., & Rangan, S. (2012). Message-passing de-quantization with applications to compressed sensing. IEEE Transactions on Signal Processing, 60, 6270–6281.

    Article  MathSciNet  Google Scholar 

  22. Wu, S., Yao, H., Jiang, C., Chen, X., Kuang, L., & Hanzo, L. (2019). Downlink channel estimation for massive mimo systems relying on vector approximate message passing. IEEE Transactions on Vehicular Technology, 68, 5145–5148.

    Article  Google Scholar 

  23. Metzler, C. A., Maleki, A., & Baraniuk, R. G. (2016). From denoising to compressed sensing. IEEE Transactions on Information Theory, 62, 5117–5144.

    Article  MathSciNet  Google Scholar 

  24. Zhang, Z., Cai, X., Li, C., Zhong, C., & Dai, H. (2017). One-bit quantized massive mimo detection based on variational approximate message passing. IEEE Transactions on Signal Processing, 66, 2358–2373.

    Article  MathSciNet  Google Scholar 

  25. Zeng, J., Lin, J., & Wang, Z. (2018). Low complexity message passing detection algorithm for large-scale mimo systems. IEEE Wireless Communications Letters, 7, 708–711.

    Article  Google Scholar 

  26. Maleki, A. (2011). Approximate message passing algorithms for compressed sensing, a degree of Doctor of Philosophy. Stanford University.

    Google Scholar 

  27. Jeon, C., Ghods, R., Maleki, A., & Studer, C. Optimality of large mimo detection via approximate message passing. In: 2015 IEEE International Symposium on Information Theory (ISIT), IEEE (pp. 1227–1231).

  28. Guo, D., & Verdú, S. (2005). Randomly spread cdma: Asymptotics via statistical physics. IEEE Transactions on Information Theory, 51, 1983–2010.

    Article  MathSciNet  Google Scholar 

  29. Choi, J. W., Lee, B., Shim, B., & Kang, I. Low complexity detection and precoding for massive mimo systems. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC), IEEE (pp. 2857–2861).

  30. Čirkić, M., & Larsson, E. G. (2014). Sumis: Near-optimal soft-in soft-out mimo detection with low and fixed complexity. IEEE Transactions on Signal Processing, 62, 3084–3097.

    Article  MathSciNet  Google Scholar 

  31. Mezard, M., & Montanari, A. (2009). Information, physics, and computation. Oxford University Press.

    Book  Google Scholar 

  32. Hochwald, B. M., & Ten Brink, S. (2003). Achieving near-capacity on a multiple-antenna channel. IEEE Transactions on Communications, 51, 389–399.

    Article  Google Scholar 

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Correspondence to Hanumantharao Bitra.

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Bitra, H., Ponnusamy, P. Large Scale MIMO Analysis Using Enhanced LAMA. Wireless Pers Commun 126, 2469–2482 (2022). https://doi.org/10.1007/s11277-022-09762-3

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