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Trainable Joint Channel Estimation, Detection and Decoding for MIMO URLLC Systems (2404.07721v1)

Published 11 Apr 2024 in eess.SP, cs.IT, and math.IT

Abstract: The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximum a posteriori (MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

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References (43)
  1. H. Ji, S. Park, J. Yeo, Y. Kim, J. Lee, and B. Shim, “Ultra-reliable and low-latency communications in 5G downlink: Physical layer aspects,” IEEE Wireless Commun., vol. 25, no. 3, pp. 124–130, Jul. 2018.
  2. G. J. Sutton et al., “Enabling technologies for ultra-reliable and low latency communications: From PHY and MAC layer perspectives,” IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2488–2524, Feb. 2019.
  3. H. Chen et al., “Ultra-reliable low latency cellular networks: Use cases, challenges and approaches,” IEEE Commun. Mag., vol. 56, no. 12, pp. 119–125, Sep. 2018.
  4. G. Durisi, T. Koch, and P. Popovski, “Toward massive, ultrareliable, and low-latency wireless communication with short packets,” Proc. IEEE, vol. 104, no. 9, pp. 1711–1726, Aug. 2016.
  5. G. Durisi, T. Koch, J. Östman, Y. Polyanskiy, and W. Yang, “Short-packet communications over multiple-antenna Rayleigh-fading channels,” IEEE Trans. Commun., vol. 64, no. 2, pp. 618–629, Feb. 2016.
  6. X. Sun et al., “Short-packet downlink transmission with non-orthogonal multiple access,” IEEE Trans. Wireless Commun., vol. 17, no. 7, pp. 4550–4564, Jul. 2018.
  7. Y. Polyanskiy, H. V. Poor, and S. Verdú, “Channel coding rate in the finite blocklength regime,” IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2307–2359, May 2010.
  8. W. Yang, G. Durisi, T. Koch, and Y. Polyanskiy, “Quasi-static multiple antenna fading channels at finite blocklength,” IEEE Trans. Inf. Theory, vol. 60, no. 7, pp. 4232–4265, Jul. 2014.
  9. J. Östman, A. Lancho, G. Durisi, and L. Sanguinetti, “URLLC with massive MIMO: Analysis and design at finite blocklength,” IEEE Trans. Wireless Commun., vol. 20, no. 10, pp. 6387–6401, Oct. 2021.
  10. M. Shirvanimoghaddam et al., “Short block-length codes for ultra-reliable low latency communications,” IEEE Commun. Mag., vol. 57, no. 2, pp. 130–137, Dec. 2019.
  11. L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,” IEEE J. Sel. Topics in Signal Process., vol. 8, no. 5, pp. 742–758, Oct. 2014.
  12. M. Tuchler, R. Koetter, and A. C. Singer, “Turbo equalization: Principles and new results,” IEEE Trans. Commun., vol. 50, no. 5, pp. 754–767, Aug. 2002.
  13. C. Studer, S. Fateh, and D. Seethaler, “ASIC implementation of soft-input soft-output MIMO detection using MMSE parallel interference cancellation,” IEEE J. Solid-State Circuits, vol. 46, no. 7, pp. 1754–1765, Jun. 2011.
  14. B. M. Hochwald and S. ten Brink, “Achieving near-capacity on a multiple-antenna channel,” IEEE Trans. Commun., vol. 51, no. 3, pp. 389–399, Apr. 2003.
  15. D. Verenzuela et al., “Massive-MIMO iterative channel estimation and decoding (MICED) in the uplink,” IEEE Trans. Commun., vol. 68, no. 2, pp. 854–870, Feb. 2020.
  16. B. Lee, S. Park, D. Love, H. Ji, and B. Shim, “Packet structure and receiver design for low latency wireless communications with ultra-short packets,” IEEE Trans. Commun., vol. 66, no. 2, pp. 796–807, Feb. 2018.
  17. Y. Li, L. Wang, and Z. Ding, “An integrated linear programming receiver for LDPC coded MIMO-OFDM signals,” IEEE Trans. Commun., vol. 61, no. 7, pp. 2816–2827, Jul. 2013.
  18. A. Jalali and Z. Ding, “Joint detection and decoding of polar coded 5G control channels,” IEEE Trans. Wireless Commun., vol. 19, no. 3, pp. 2066–2078, Mar. 2020.
  19. K. Wang, H. Shen, W. Wu, and Z. Ding, “Joint detection and decoding in LDPC-based space-time coded MIMO-OFDM systems via linear programming,” IEEE Tran. Signal Process., vol. 63, no. 13, pp. 3411–3424, Jul. 2015.
  20. S. Cammerer, F. A. Aoudia, S. Dörner, M. Stark, J. Hoydis, and S. ten Brink, “Trainable communication systems: Concepts and prototype,” IEEE Trans. Commun., vol. 68, no. 9, pp. 5489–5503, Sep. 2020.
  21. F. A. Aoudia and J. Hoydis, “Model-free training of end-to-end communication systems,” IEEE J. Sel. Areas Commun., vol. 37, no. 11, pp. 2503–2516, Nov. 2019.
  22. M. Honkala, D. Korpi, and J. M. J. Huttunen, “DeepRx: Fully convolutional deep learning receiver,” IEEE Trans. Wireless Commun., vol. 20, no. 6, pp. 3925–3940, Jun. 2021.
  23. M. Goutay, F. A. Aoudia, J. Hoydis, and J.-M. Gorce, “Machine learning for MU-MIMO receive processing in OFDM systems,” IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2318–2332, Aug. 2021.
  24. J. Pihlajasalo et al., “Deep learning OFDM receivers for improved power efficiency and coverage,” IEEE Trans. Wireless Commun., vol. 22, no. 8, pp. 5518–5535, Aug. 2023.
  25. S. Dörner, J. Clausius, S. Cammerer, and S. ten Brink, “Learning joint detection, equalization and decoding for short-packet communications,” IEEE Trans. Commun., vol. 71, no. 2, pp. 837–850, Feb. 2023.
  26. H. He, S. Jin, C. Wen, F. Gao, G. Y. Li, and Z. Xu, “Model-driven deep learning for physical layer communications,” IEEE Wireless Commun., vol. 26, no. 5, pp. 77–83, Oct. 2019.
  27. V. Monga, Y. Li, and Y. C. Eldar, “Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,” IEEE Signal Process. Mag., vol. 38, no. 2, pp. 18–44, Mar. 2021.
  28. S. Boyd et al., “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1–122, Jan. 2011.
  29. X. Liu and S. C. Draper, “The ADMM penalized decoder for LDPC codes,” IEEE Trans. Inf. Theory, vol. 62, no. 6, pp. 2966–2984, Jun. 2016.
  30. 3GPP Tech. Spec. TS38.211 V15.6.0, “NR Physical channels and modulation,” Jun. 2019.
  31. J. Feldman, M. Wainwright, and D. Karger, “Using linear programming to decode binary linear codes,” IEEE Trans. Inf. Theory, vol. 51, no. 3, pp. 954–972, Mar. 2005.
  32. N. Goela, S. B. Korada, and M. Gastpar, “On LP decoding of polar codes,” in Proc. IEEE Inf. Theory Workshop (ITW), Dublin, Ireland, Aug. 2010, pp. 1–5.
  33. J. Bai, Y. Wang, and F. Lau, “Minimum-polytope-based linear programming decoder for LDPC codes via ADMM approach,” IEEE Wireless Commun. Lett., vol. 8, no. 4, pp. 1032–1035, Aug. 2019.
  34. M. Shao and W.-K. Ma, “Binary MIMO detection via homotopy optimization and its deep adaptation,” IEEE Trans. Signal Process., vol. 69, pp. 781–796, Feb. 2021.
  35. T. Goldstein, B. O’Donoghue, S. Setzer, and R. Baraniuk, “Fast alternating direction optimization methods,” SIAM J. Imag. Sci., vol. 7, no. 3, pp. 1588–1623, Jul. 2014.
  36. J. Mo, P. Schniter, and R. W. Heath, “Channel estimation in broadband millimeter wave MIMO systems with few-bit ADCs,” IEEE Trans. Signal Process., vol. 66, no. 5, pp. 1141–1154, Mar. 2018.
  37. N. Parikh and S. Boyd, “Proximal algorithms,” Found. Trends Optim., vol. 1, no. 3, pp. 127–239, Jan. 2014.
  38. S. L. Loyka, “Channel capacity of MIMO architecture using the exponential correlation matrix,” IEEE Commun. Lett., vol. 5, no. 9, pp. 369–371, Sep. 2001.
  39. O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding in millimeter wave MIMO systems,” IEEE Trans. Wireless Commun., vol. 13, no. 3, pp. 1499–1513, Mar. 2014.
  40. Y. Xiong, N. Wei, and Z. Zhang, “A low-complexity iterative GAMP-based detection for massive MIMO with low-resolution ADCs,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), San Francisco, CA, USA, Mar. 2017, pp. 1–6.
  41. I. Daubechies, M. Defrise, and C. D. Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math., vol. 57, pp. 1413–1457, Nov. 2004.
  42. M. Helmling, S. Scholl, F. Gensheimer, T. Dietz, K. Kraft, S. Ruzika, and N. Wehn, “Database of channel codes and ML simulation results,” www.uni-kl.de/channel-codes, 2019.
  43. Y. Sun, P. Babu, and D. P. Palomar, “Majorization-minimization algorithms in signal processing, communications, and machine learning,” IEEE Trans. Signal Process., vol. 65, no. 3, pp. 794–816, Feb. 2017.
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