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Study of Adaptive LLR-based AP selection for Grant-Free Random Access in Cell-Free Networks (2312.16781v1)

Published 28 Dec 2023 in cs.IT, eess.SP, and math.IT

Abstract: This paper presents an iterative detection and decoding scheme along with an adaptive strategy to improve the selection of access points (APs) in a grant-free uplink cell-free scenario. With the requirement for the APs to have low-computational power in mind, we introduce a low-complexity scheme for local activity and data detection. At the central processing unit (CPU) level, we propose an adaptive technique based on local log-likelihood ratios (LLRs) to select the list of APs that should be considered for each device. Simulation results show that the proposed LLRs-based APs selection scheme outperforms the existing techniques in the literature in terms of bit error rate (BER) while requiring comparable fronthaul load.

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References (49)
  1. “Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions,” IEEE Access, vol. 8, pp. 180928–180954, 2020.
  2. I. F. Akyildiz et al., “6G and Beyond: The Future of Wireless Communications Systems,” IEEE Access, vol. 8, pp. 133995–134030, 2020.
  3. Rodrigo C. de Lamare, “Massive mimo systems: Signal processing challenges and future trends,” URSI Radio Science Bulletin, vol. 2013, no. 347, pp. 8–20, 2013.
  4. “Large-scale antenna systems with ul/dl hardware mismatch: Achievable rates analysis and calibration,” IEEE Transactions on Communications, vol. 63, no. 4, pp. 1216–1229, 2015.
  5. H. Q. Ngo et al., “Cell-Free Massive MIMO Versus Small Cells,” IEEE Trans. on Wireless Commun., vol. 16, no. 3, pp. 1834–1850, 2017.
  6. E. Björnson and L. Sanguinetti, “Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation,” IEEE Trans. on Wireless Commun., vol. 19, no. 1, pp. 77–90, 2020.
  7. E. Björnson et al., “Scalable Cell-Free Massive MIMO Systems,” IEEE Trans. on Commun., vol. 68, no. 7, pp. 4247–4261, 2020.
  8. P. Popovski et al., “5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View,” IEEE Access, vol. 6, pp. 55765–55779, 2018.
  9. T. H. Nguyen et al., “Optimal Power Control and Load Balancing for Uplink Cell-Free Multi-User Massive MIMO,” IEEE Access, vol. 6, pp. 14462–14473, 2018.
  10. “Clustered cell-free multi-user multiple-antenna systems with rate-splitting: Precoder design and power allocation,” IEEE Transactions on Communications, vol. 71, no. 10, pp. 5920–5934, 2023.
  11. “Transmit diversity and relay selection algorithms for multirelay cooperative mimo systems,” IEEE Transactions on Vehicular Technology, vol. 61, no. 3, pp. 1084–1098, 2012.
  12. H. Q. Ngo et al., “On the Total Energy Efficiency of Cell-Free Massive MIMO,” IEEE Trans. on Green Commun. and Networking, vol. 2, no. 1, pp. 25–39, 2018.
  13. H. T. Dao and S. Kim, “Effective Channel Gain-Based Access Point Selection in Cell-Free Massive MIMO Systems,” IEEE Access, vol. 8, pp. 108127–108132, 2020.
  14. C. D’Andrea and E. G. Larsson, “Improving Cell-Free Massive MIMO by Local Per-Bit Soft Detection,” IEEE Commun. Lett., vol. 25, no. 7, pp. 2400–2404, 2021.
  15. “Graph Neural Network Based Access Point Selection for Cell-Free Massive MIMO Systems,” in 2021 IEEE Global Commun. Conf. (GLOBECOM), 2021, pp. 01–06.
  16. T. X. Vu et al., “Joint Power Allocation and Access Point Selection for Cell-free Massive MIMO,” in ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, pp. 1–6.
  17. T. Van Chien et al., “Joint Power Allocation and Load Balancing Optimization for Energy-Efficient Cell-Free Massive MIMO Networks,” IEEE Trans. Wireless Commun., vol. 19, no. 10, pp. 6798–6812, 2020.
  18. G. Dong, “Energy-Efficiency-Oriented Joint User Association and Power Allocation in Distributed Massive MIMO Systems,” IEEE Trans. Veh. Technol., vol. 68, no. 6, pp. 5794–5808, 2019.
  19. R. Wang et al., “Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection,” IEEE Access, vol. 9, pp. 40860–40870, 2021.
  20. “Robust MMSE Precoding and Power Allocation for Cell-Free Massive MIMO Systems,” IEEE Trans. Veh. Technol., vol. 70, no. 5, pp. 5115–5120, 2021.
  21. U. K. Ganesan et al., “Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO,” IEEE Trans. on Commun., vol. 69, no. 11, pp. 7520–7530, 2021.
  22. J. Ding and J. Choi, “SIC Aided K𝐾Kitalic_K-Repetition for Mission-Critical MTC in Cell-Free Massive MIMO,” in 2021 IEEE Conf. on Standards for Commun. and Networking (CSCN), 2021, pp. 167–173.
  23. A. Mishra et al., “Rate-Splitting assisted Massive Machine-Type Communications in Cell-Free Massive MIMO,” IEEE Commun. Lett., pp. 1–1, 2022.
  24. Z. Wang et al., “NOMA in Cell-Free mMIMO Systems with AP Selection,” in 2020 Int. Conf. on Wireless Commun. and Signal Proc. (WCSP), 2020, pp. 430–435.
  25. “Adaptive Activity-Aware Iterative Detection for Massive Machine-Type Communications,” IEEE Wireless Commun. Lett., vol. 8, no. 6, pp. 1631–1634, 2019.
  26. A. Goldsmith et al., “Capacity limits of mimo channels,” IEEE J. Sel. Areas Commun., vol. 21, no. 5, pp. 684–702, 2003.
  27. R. C. de Lamare and R. Sampaio-Neto, “Adaptive reduced-rank processing based on joint and iterative interpolation, decimation, and filtering,” IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2503–2514, 2009.
  28. R. C. de Lamare and R. Sampaio-Neto, “Minimum mean-squared error iterative successive parallel arbitrated decision feedback detectors for ds-cdma systems,” IEEE Transactions on Communications, vol. 56, no. 5, pp. 778–789, 2008.
  29. “Multiple feedback successive interference cancellation detection for multiuser mimo systems,” IEEE Transactions on Wireless Communications, vol. 10, no. 8, pp. 2434–2439, 2011.
  30. P. Li and R. C. de Lamare, “Adaptive Decision-Feedback Detection With Constellation Constraints for MIMO Systems,” IEEE Trans. Veh. Technol., vol. 61, no. 2, pp. 853–859, 2012.
  31. “Distributed iterative detection with reduced message passing for networked mimo cellular systems,” IEEE Transactions on Vehicular Technology, vol. 63, no. 6, pp. 2947–2954, 2014.
  32. “Iterative Detection and Decoding Algorithms for MIMO Systems in Block-Fading Channels Using LDPC Codes,” IEEE Trans. Veh. Technol., vol. 65, no. 4, pp. 2735–2741, 2016.
  33. “Iterative Detection and Decoding for Large-Scale Multiple-Antenna Systems With 1-Bit ADCs,” IEEE Wireless Commun. Lett., vol. 7, no. 3, pp. 476–479, 2018.
  34. “Adaptive activity-aware iterative detection for massive machine-type communications,” IEEE Wireless Communications Letters, vol. 8, no. 6, pp. 1631–1634, 2019.
  35. “Dynamic oversampling for 1-bit adcs in large-scale multiple-antenna systems,” IEEE Transactions on Communications, vol. 69, no. 5, pp. 3423–3435, 2021.
  36. “Detection techniques for massive machine-type communications: Challenges and solutions,” IEEE Access, vol. 8, pp. 180928–180954, 2020.
  37. “Multiuser-mimo systems using comparator network-aided receivers with 1-bit quantization,” IEEE Transactions on Communications, vol. 71, no. 2, pp. 908–922, 2023.
  38. Roberto B. Di Renna and Rodrigo C. de Lamare, “Dynamic message scheduling based on activity-aware residual belief propagation for asynchronous mmtc,” IEEE Wireless Communications Letters, vol. 10, no. 6, pp. 1290–1294, 2021.
  39. “Joint channel estimation, activity detection and data decoding based on dynamic message-scheduling strategies for mmtc,” IEEE Transactions on Communications, vol. 70, no. 4, pp. 2464–2479, 2022.
  40. P. S. Bradley, “Feature selection via concave minimization and support vector machines,” in Proc. 13th ICML, 1998, pp. 82–90.
  41. “Soft-Output MMSE MIMO Detector Under ML Channel Estimation and Channel Correlation,” IEEE Signal Process. Lett., vol. 16, no. 8, pp. 667–670, 2009.
  42. S. M. Kay, Fundaments of Statistical Signal Processing: Estimation Theory, Prentice Hall, Englewood Cliffs, NJ, 1st edition, 1993.
  43. “Iterative List Detection and Decoding for Massive Machine-Type Communications,” IEEE Trans. Commun., vol. 68, no. 10, pp. 6276–6288, 2020.
  44. “Iterative (turbo) soft interference cancellation and decoding for coded CDMA,” IEEE Trans. Commun., vol. 47, no. 7, pp. 1046–1061, 1999.
  45. “Design of capacity-approaching irregular low-density parity-check codes,” IEEE Transactions on Information Theory, vol. 47, no. 2, pp. 619–637, 2001.
  46. “Design of ldpc codes based on multipath emd strategies for progressive edge growth,” IEEE Transactions on Communications, vol. 64, no. 8, pp. 3208–3219, 2016.
  47. “Low-latency reweighted belief propagation decoding for ldpc codes,” IEEE Communications Letters, vol. 16, no. 10, pp. 1660–1663, 2012.
  48. “Knowledge-aided informed dynamic scheduling for ldpc decoding of short blocks,” IET Communications, vol. 12, no. 9, pp. 1094–1101, 2018.
  49. S. Chen et al., “Structured Massive Access for Scalable Cell-Free Massive MIMO Systems,” J. Sel. Areas Commun., vol. 39, no. 4, pp. 1086–1100, 2021.

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