Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
158 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Meta-Learning-Based Fronthaul Compression for Cloud Radio Access Networks (2403.09004v1)

Published 13 Mar 2024 in cs.IT, eess.SP, and math.IT

Abstract: This paper investigates the fronthaul compression problem in a user-centric cloud radio access network, in which single-antenna users are served by a central processor (CP) cooperatively via a cluster of remote radio heads (RRHs). To satisfy the fronthaul capacity constraint, this paper proposes a transform-compress-forward scheme, which consists of well-designed transformation matrices and uniform quantizers. The transformation matrices perform dimension reduction in the uplink and dimension expansion in the downlink. To reduce the communication overhead for designing the transformation matrices, this paper further proposes a deep learning framework to first learn a suboptimal transformation matrix at each RRH based on the local channel state information (CSI), and then to refine it iteratively. To facilitate the refinement process, we propose an efficient signaling scheme that only requires the transmission of low-dimensional effective CSI and its gradient between the CP and RRH, and further, a meta-learning based gated recurrent unit network to reduce the number of signaling transmission rounds. For the sum-rate maximization problem, simulation results show that the proposed two-stage neural network can perform close to the fully cooperative global CSI based benchmark with significantly reduced communication overhead for both the uplink and the downlink. Moreover, using the first stage alone can already outperform the existing local CSI based benchmark.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. R. Qiao, T. Jiang, and W. Yu, “Learning-based fronthaul compression for uplink cloud radio access networks,” in Proc. IEEE Int. Conf. Commun. (ICC), Rome, Italy, May 2023, pp. 5928–5933.
  2. A. Checko et al., “Cloud RAN for mobile networks—a technology overview,” IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 405–426, Firstquarter 2014.
  3. O. Simeone, A. Maeder, M. Peng, O. Sahin, and W. Yu, “Cloud radio access network: Virtualizing wireless access for dense heterogeneous systems,” J. Commn. Net., vol. 18, no. 2, pp. 135–149, Apr. 2016.
  4. M. Peng, C. Wang, V. Lau, and H. V. Poor, “Fronthaul-constrained cloud radio access networks: Insights and challenges,” IEEE Wirel. Commun., vol. 22, no. 2, pp. 152–160, Apr. 2015.
  5. E. Björnson and L. Sanguinetti, “Scalable cell-free massive MIMO systems,” IEEE Trans. Commun., vol. 68, no. 7, pp. 4247–4261, July 2020.
  6. S. Elhoushy, M. Ibrahim, and W. Hamouda, “Cell-free massive MIMO: A survey,” IEEE Commun. Surveys Tuts., vol. 24, no. 1, pp. 492–523, Oct. 2021.
  7. H. Masoumi and M. J. Emadi, “Performance analysis of cell-free massive MIMO system with limited fronthaul capacity and hardware impairments,” IEEE Trans. Wireless Commun., vol. 19, no. 2, pp. 1038–1053, Nov. 2019.
  8. W. Yu, P. Patil, B. Dai, and Y. Zhou, “Cooperative beamforming and resource optimization in C-RAN,” in Cloud Radio Access Networks Principles, Technologies, and Applications.   Cambridge, UK: Cambridge Univ. Press, 2017, pp. 54–81.
  9. L. Liu and R. Zhang, “Optimized uplink transmission in multi-antenna C-RAN with spatial compression and forward,” IEEE Trans. Signal Process., vol. 63, no. 19, pp. 5083–5095, Oct. 2015.
  10. L. Liu, W. Yu, and O. Simeone, “Fronthaul-aware design for cloud radio access networks,” in Key Technologies for 5G Wireless Systems.   Cambridge, UK: Cambridge Univ. Press, 2017, p. 48–75.
  11. F. Wiffen, M. Z. Bocus, A. Doufexi, and A. Nix, “Distributed MIMO uplink capacity under transform coding fronthaul compression,” in Proc. IEEE Int. Conf. Commun. (ICC), Shanghai, China, May 2019, pp. 1–6.
  12. F. Wiffen, M. Z. Bocus, A. Doufexi, and W. H. Chin, “MF-based dimension reduction signal compression for fronthaul-constrained distributed MIMO C-RAN,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Virtual Conference, May 2020, pp. 1–8.
  13. F. Wiffen, W. H. Chin, and A. Doufexi, “Distributed dimension reduction for distributed massive MIMO C-RAN with finite fronthaul capacity,” in Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, USA, Nov. 2021, pp. 1228–1236.
  14. N. Arad and Y. Noam, “Precode and quantize channel state information sharing for cloud radio access networks,” in Proc. IEEE Int. Conf. Commun. (ICC), Kansas City, MO, USA, May 2018, pp. 1–6.
  15. I. D. Schizas, G. B. Giannakis, and Z.-Q. Luo, “Distributed estimation using reduced-dimensionality sensor observations,” IEEE Trans. Signal Process., vol. 55, no. 8, pp. 4284–4299, July 2007.
  16. F. Sohrabi, T. Jiang, and W. Yu, “Learning progressive distributed compression strategies from local channel state information,” IEEE J. Sel. Areas Commun., vol. 16, no. 3, pp. 573–584, Apr. 2022.
  17. Y. Zhou, Y. Xu, W. Yu, and J. Chen, “On the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks,” IEEE Trans. Inf. Theory, vol. 62, no. 12, pp. 7402–7418, Dec. 2016.
  18. L. Liu, S. Bi, and R. Zhang, “Joint power control and fronthaul rate allocation for throughput maximization in OFDMA-based cloud radio access network,” IEEE Trans. Commun., vol. 63, no. 11, pp. 4097–4110, Aug. 2015.
  19. V. K. Goyal, “Theoretical foundations of transform coding,” IEEE Signal Process. Mag., vol. 18, no. 5, pp. 9–21, Sept. 2001.
  20. P. Patil, B. Dai, and W. Yu, “Hybrid data-sharing and compression strategy for downlink cloud radio access network,” IEEE Trans. Commun., vol. 66, no. 11, pp. 5370–5384, June 2018.
  21. S.-H. Park, O. Simeone, O. Sahin, and S. Shamai, “Joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks,” ” IEEE Trans. Signal Process., vol. 61, no. 22, pp. 5646–5658, Aug. 2013.
  22. L. Liu and W. Yu, “Joint sparse beamforming and network coding for downlink multi-hop cloud radio access networks,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Washington, DC, USA, Dec. 2016, pp. 1–6.
  23. B. Dai and W. Yu, “Sparse beamforming and user-centric clustering for downlink cloud radio access network,” IEEE Access, vol. 2, pp. 1326–1339, Oct. 2014.
  24. M. Joham, W. Utschick, and J. A. Nossek, “Linear transmit processing in MIMO communications systems,” IEEE Trans. Signal Process., vol. 53, no. 8, pp. 2700–2712, Aug. 2005.
  25. C. B. Peel, B. M. Hochwald, and A. L. Swindlehurst, “A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization,” IEEE Trans. Commun., vol. 53, no. 1, pp. 195–202, Jan. 2005.
  26. H. Choi, T. Jiang, Y. Shi, X. Liu, Y. Zhou, and K. B. Letaief, “Large-scale beamforming for massive MIMO via randomized sketching,” IEEE Trans. Veh. Technol., vol. 70, no. 5, pp. 4669–4681, Apr. 2021.
  27. A. Mueller, A. Kammoun, E. Björnson, and M. Debbah, “Linear precoding based on polynomial expansion: Reducing complexity in massive MIMO,” EURASIP J. Wirel. Commun. Netw., vol. 2016, pp. 1–22, Feb. 2016.
  28. L. Liang, W. Xu, and X. Dong, “Low-complexity hybrid precoding in massive multiuser MIMO systems,” IEEE Wirel. Commun., vol. 3, no. 6, pp. 653–656, Oct. 2014.
  29. E. Björnson, M. Bengtsson, and B. Ottersten, “Optimal multiuser transmit beamforming: A difficult problem with a simple solution structure [lecture notes],” IEEE Signal Process. Mag., vol. 31, no. 4, pp. 142–148, July 2014.
  30. M. Andrychowicz et al., “Learning to learn by gradient descent by gradient descent,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 29, Barcelona, Spain, Dec. 2016, pp. 3988–3996.
  31. K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder–decoder approaches,” in Proc. SSST-8, Workshop Syntax Semant. Struct. Stat. Transl., Doha, Qatar, Oct. 2014, pp. 103–111.
  32. J.-Y. Xia, S. Li, J.-J. Huang, Z. Yang, I. M. Jaimoukha, and D. Gündüz, “Meta-learning based alternating minimization algorithm for nonconvex optimization,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 9, pp. 5366–5380, Apr. 2022.
  33. C. Zhu and W. Yu, “Stochastic modeling and analysis of user-centric network MIMO systems,” IEEE Trans. Commun., vol. 66, no. 12, pp. 6176–6189, Dec. 2018.
  34. A. Paszke et al., “Pytorch: An imperative style, high-performance deep learning library,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 32, Vancouver, Canada, Dec. 2019, pp. 8026–8037.
  35. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Representations (ICLR), San Diega, CA, USA, Dec. 2015.
  36. S. Sun et al., “Investigation of prediction accuracy, sensitivity, and parameter stability of large-scale propagation path loss models for 5G wireless communications,” IEEE Trans. Veh. Technol., vol. 65, no. 5, pp. 2843–2860, Mar. 2016.
  37. G. Hinton, (2018). Neural Networks for Machine Learning Lecture 6. [online]. Available: https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf.
  38. B. Dai and W. Yu, “Energy efficiency of downlink transmission strategies for cloud radio access networks,” IEEE J. Sel. Areas Commun., vol. 34, no. 4, pp. 1037–1050, Apr. 2016.
  39. C. Pan, H. Zhu, N. J. Gomes, and J. Wang, “Joint precoding and RRH selection for user-centric green MIMO C-RAN,” IEEE Trans. Wireless Commun., vol. 16, no. 5, pp. 2891–2906, Mar. 2017.
  40. T. M. Kim, F. Sun, and A. J. Paulraj, “Low-complexity MMSE precoding for coordinated multipoint with per-antenna power constraint,” IEEE Signal Process. Lett., vol. 20, no. 4, pp. 395–398, Mar. 2013.
  41. A. Chowdhury, G. Verma, C. Rao, A. Swami, and S. Segarra, “Unfolding WMMSE using graph neural networks for efficient power allocation,” IEEE Trans. Wirel. Commun., vol. 20, no. 9, pp. 6004–6017, Apr. 2021.
  42. Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, “An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,” ” IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4331–4340, Apr. 2011.
Citations (1)

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com