Papers
Topics
Authors
Recent
Search
2000 character limit reached

ZF Beamforming Tensor Compression for Massive MIMO Fronthaul

Published 6 Mar 2024 in cs.IT, eess.SP, and math.IT | (2403.03675v1)

Abstract: In the rapidly evolving landscape of 5G and beyond 5G (B5G) mobile cellular communications, efficient data compression and reconstruction strategies become paramount, especially in massive multiple-input multiple-output (MIMO) systems. A critical challenge in these systems is the capacity-limited fronthaul, particularly in the context of the Ethernet-based common public radio interface (eCPRI) connecting baseband units (BBUs) and remote radio units (RRUs). This capacity limitation hinders the effective handling of increased traffic and data flows. We propose a novel two-stage compression approach to address this bottleneck. The first stage employs sparse Tucker decomposition, targeting the weight tensor's low-rank components for compression. The second stage further compresses these components using complex givens decomposition and run-length encoding, substantially improving the compression ratio. Our approach specifically targets the Zero-Forcing (ZF) beamforming weights in BBUs. By reconstructing these weights in RRUs, we significantly alleviate the burden on eCPRI traffic, enabling a higher number of concurrent streams in the radio access network (RAN). Through comprehensive evaluations, we demonstrate the superior effectiveness of our method in Channel State Information (CSI) compression, paving the way for more efficient 5G/B5G fronthaul links.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. M. Joham, W. Utschick, and J. Nossek, “Linear transmit processing in mimo communications systems,” IEEE Transactions on Signal Processing, vol. 53, no. 8, pp. 2700–2712, 2005.
  2. E. Björnson, M. Bengtsson, and B. Ottersten, “Optimal multiuser transmit beamforming: A difficult problem with a simple solution structure [lecture notes],” IEEE Signal Processing Magazine, vol. 31, no. 4, pp. 142–148, 2014.
  3. P.-H. Kuo, H. T. Kung, and P.-A. Ting, “Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays,” in 2012 IEEE Wireless Communications and Networking Conference (WCNC), 2012, pp. 492–497.
  4. D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proceedings of the National Academy of Sciences, vol. 106, no. 45, pp. 18 914–18 919, 2009.
  5. I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, vol. 57, no. 11, pp. 1413–1457, 2004.
  6. Y. Zhang, X. Zhang, and Y. Liu, “Deep learning based csi compression and quantization with high compression ratios in fdd massive mimo systems,” IEEE Wireless Communications Letters, vol. 10, no. 10, pp. 2101–2105, 2021.
  7. Z. Yin, W. Xu, R. Xie, S. Zhang, D. W. K. Ng, and X. You, “Deep csi compression for massive mimo: A self-information model-driven neural network,” IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 8872–8886, 2022.
  8. J. Yang, X. Chen, H. Zou, D. Wang, Q. Xu, and L. Xie, “Efficientfi: Toward large-scale lightweight wifi sensing via csi compression,” IEEE Internet of Things Journal, vol. 9, no. 15, pp. 13 086–13 095, 2022.
  9. H. Xie, F. Gao, and S. Jin, “An overview of low-rank channel estimation for massive mimo systems,” IEEE Access, vol. 4, pp. 7313–7321, 2016.
  10. Z. Wei, H. Li, H. Liu, B. Li, and C. Zhao, “Randomized low-rank approximation based massive mimo csi compression,” IEEE Communications Letters, vol. 25, no. 6, pp. 2004–2008, 2021.
  11. D. Pandey and H. Leib, “A tensor based precoder and receiver for mimo gfdm systems,” in ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1–6.
  12. T.-H. Chou, N. Michelusi, D. J. Love, and J. V. Krogmeier, “Fast position-aided mimo beam training via noisy tensor completion,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 774–788, 2021.
  13. Y. Gong, H. Zhao, K. Hu, Q. Lu, and Y. Shen, “A multipath-aided localization method for mimo-ofdm systems via tensor decomposition,” IEEE Wireless Communications Letters, vol. 11, no. 6, pp. 1225–1228, 2022.
  14. Y. Lin, S. Jin, M. Matthaiou, and X. You, “Tensor-based algebraic channel estimation for hybrid irs-assisted mimo-ofdm,” IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3770–3784, 2021.
  15. G. T. de Araújo, A. L. F. de Almeida, and R. Boyer, “Channel estimation for intelligent reflecting surface assisted mimo systems: A tensor modeling approach,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 789–802, 2021.
  16. J. Du, Y. Cheng, L. Jin, and F. Gao, “Time-varying phase noise estimation, channel estimation, and data detection in ris-assisted mimo systems via tensor analysis,” IEEE Transactions on Signal Processing, vol. 71, pp. 3426–3441, 2023.
  17. B. Sokal, P. R. B. Gomes, A. L. F. d. Almeida, and M. Haardt, “Tensor-based receiver for joint channel, data, and phase-noise estimation in mimo-ofdm systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 803–815, 2021.
  18. J. Du, M. Han, Y. Chen, L. Jin, and F. Gao, “Tensor-based joint channel estimation and symbol detection for time-varying mmwave massive mimo systems,” IEEE Transactions on Signal Processing, vol. 69, pp. 6251–6266, 2021.
  19. L. Cheng and Q. Shi, “Towards overfitting avoidance: Tuning-free tensor-aided multi-user channel estimation for 3d massive mimo communications,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 832–846, 2021.
  20. W.-C. Chang and Y. T. Su, “Sparse bayesian learning based tensor dictionary learning and signal recovery with application to mimo channel estimation,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 847–859, 2021.
  21. H. Chen, F. Ahmad, S. Vorobyov, and F. Porikli, “Tensor decompositions in wireless communications and mimo radar,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 438–453, 2021.
  22. J. D. Carroll and J.-J. Chang, “Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition,” Psychometrika, vol. 35, no. 3, pp. 283–319, 1970.
  23. L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika, vol. 31, no. 3, pp. 279–311, 1966.
  24. F. Yuan, “Tucker decomposition for rotated codebook in 3-d mimo system under spatially correlated channel,” IEEE Transactions on Vehicular Technology, vol. 65, no. 9, pp. 7813–7818, 2016.
  25. Y. Zhu, Y. Tian, and C. Yang, “Hosvd-based limited feedback and precoding design for massive mimo systems,” in 2016 IEEE/CIC International Conference on Communications in China (ICCC Workshops), 2016, pp. 1–6.
  26. R. Ballester-Ripoll, P. Lindstrom, and R. Pajarola, “Tthresh: Tensor compression for multidimensional visual data,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 9, pp. 2891–2903, 2020.
  27. R. Li, Z. Pan, Y. Wang, and P. Wang, “The correlation-based tucker decomposition for hyperspectral image compression,” Neurocomputing, vol. 419, pp. 357–370, 2021.
  28. R. Li, Z. Pan, and Y. Wang, “Correlation-based initialization algorithm for tensor-based hsi compression methods,” Multimedia Tools and Applications, vol. 79, pp. 21 925–21 940, 2020.
  29. M. Yin, S. Liao, X.-Y. Liu, X. Wang, and B. Yuan, “Compressing recurrent neural networks using hierarchical tucker tensor decomposition,” arXiv preprint arXiv:2005.04366, 2020.
  30. Y. Liu and M. K. Ng, “Deep neural network compression by tucker decomposition with nonlinear response,” Knowledge-Based Systems, vol. 241, p. 108171, 2022.
  31. J. Cai, Z. Cao, and L. Zhang, “Learning a single tucker decomposition network for lossy image compression with multiple bits-per-pixel rates,” IEEE Transactions on Image Processing, vol. 29, pp. 3612–3625, 2020.
  32. R. Ballester-Ripoll and R. Pajarola, “Lossy volume compression using tucker truncation and thresholding,” The Visual Computer, vol. 32, pp. 1433–1446, 2016.
  33. G. Ballard, A. Klinvex, and T. G. Kolda, “Tuckermpi: A parallel c++/mpi software package for large-scale data compression via the tucker tensor decomposition,” ACM Transactions on Mathematical Software (TOMS), vol. 46, no. 2, pp. 1–31, 2020.
  34. M. Beaupere, D. Frenkiel, and L. Grigori, “Higher-order qr with tournament pivoting for tensor compression,” SIAM Journal on Matrix Analysis and Applications, vol. 44, no. 1, pp. 106–127, 2023.
  35. F. Zhang, S. Sun, and Q. Gao, “Beam combination scheme for multi-user massive mimo systems,” Electronics Letters, vol. 53, no. 14, pp. 966–968, 2017. [Online]. Available: https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/el.2016.4704
  36. Z. Liu, S. Sun, Q. Gao, and H. Li, “Csi feedback based on spatial and frequency domains compression for 5g multi-user massive mimo systems,” in 2019 IEEE/CIC International Conference on Communications in China (ICCC), 2019, pp. 834–839.
  37. M. Xie, X. Liu, X. Yang, and W. Cai, “Multichannel image completion with mixture noise: Adaptive sparse low-rank tensor subspace meets nonlocal self-similarity,” IEEE Transactions on Cybernetics, pp. 1–14, 2022.
  38. A. Molisch, M. Win, Y.-S. Choi, and J. Winters, “Capacity of mimo systems with antenna selection,” IEEE Transactions on Wireless Communications, vol. 4, no. 4, pp. 1759–1772, 2005.
  39. J. Bolte, S. Sabach, and M. Teboulle, “Proximal alternating linearized minimization for nonconvex and nonsmooth problems,” Mathematical Programming, vol. 146, no. 1-2, pp. 459–494, 2014.

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 1 tweet with 1 like about this paper.

Sign Up for Free