Papers
Topics
Authors
Recent
Search
2000 character limit reached

Enhancing Sum-Rate Performance in Constrained Multicell Networks: A Low-Information Exchange Approach

Published 3 Apr 2024 in eess.SP and cs.AI | (2404.02477v1)

Abstract: Despite the extensive research on massive MIMO systems for 5G telecommunications and beyond, the reality is that many deployed base stations are equipped with a limited number of antennas rather than supporting massive MIMO configurations. Furthermore, while the cell-less network concept, which eliminates cell boundaries, is under investigation, practical deployments often grapple with significantly limited backhaul connection capacities between base stations. This letter explores techniques to maximize the sum-rate performance within the constraints of these more realistically equipped multicell networks. We propose an innovative approach that dramatically reduces the need for information exchange between base stations to a mere few bits, in stark contrast to conventional methods that require the exchange of hundreds of bits. Our proposed method not only addresses the limitations imposed by current network infrastructure but also showcases significantly improved performance under these constrained conditions.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (18)
  1. 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 Signal Process., vol. 8, no. 5, pp. 742–758, Oct 2014.
  2. H. Tabassum, A. H. Sakr, and E. Hossain, “Analysis of massive MIMO-enabled downlink wireless backhauling for full-duplex small cells,” IEEE Trans. Commun., vol. 64, no. 6, pp. 2354–2369, June 2016.
  3. “Study on new radio access technology physical layer aspects,” 3GPP TR 38.802, Release 14, Sep. 2017.
  4. S. He, Y. Huang, and L. Yang, “Coordinated beamforming for sum rate maximization in multi-cell downlink systems,” Signal Processing, vol. 105, pp. 22–29, Dec. 2014.
  5. E. G. Larsson and E. A. Jorswieck, “Competition versus cooperation on the MISO interference channel,” IEEE J. Select. Areas in Commun., vol. 26, no. 7, pp. 1059–1069, Sep. 2008.
  6. 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, Sep. 2011.
  7. Y. Kim and H. J. Yang, “Sum-rate maximization of multicell MISO networks with limited information exchange,” IEEE Transactions on Vehicular Technology, Mar. 2020.
  8. H. Huang, W. Xia, J. Xiong, J. Yang, G. Zheng, and X. Zhu, “Unsupervised learning-based fast beamforming design for downlink MIMO,” IEEE Access, vol. 7, pp. 7599–7605, 2019.
  9. W. Xia, G. Zheng, Y. Zhu, J. Zhang, J. Wang, and A. P. Petropulu, “A deep learning framework for optimization of MISO downlink beamforming,” IEEE Transactions on Communications, vol. 68, no. 3, pp. 1866–1880, March 2020.
  10. H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos, “Learning to optimize: Training deep neural networks for interference management,” IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5438–5453, Oct 2018.
  11. C. Sun, Z. Shi, and F. Jiang, “A machine learning approach for beamforming in ultra dense network considering selfish and altruistic strategy,” IEEE Access, vol. 8, pp. 6304–6315, 2020.
  12. H. J. Kwon, J. H. Lee, and W. Choi, “Machine learning-based beamforming in K-user MISO interference channels,” IEEE Access, vol. 9, pp. 28 066–28 075, 2021.
  13. R. Zakhour and D. Gesbert, “Coordination on the MISO interference channel using the virtual SINR framework,” in Proc. International ITG Workshop on Smart Antennas, Berlin, Germany, Feb. 2009.
  14. E. Björnson, R. Zakhour, D. Gesbert, and B. Ottersten, “Cooperative multicell precoding: Rate region characterization and distributed strategies with instantaneous and statistical CSI,” IEEE Trans. Signal Process., vol. 58, no. 8, pp. 4298–4310, Aug. 2010.
  15. G. Dulac-Arnold, R. Evans, H. van Hasselt, P. Sunehag, T. Lillicrap, J. Hunt, T. Mann, T. Weber, T. Degris, and B. Coppin, “Deep reinforcement learning in large discrete action spaces,” arXiv, 2016. [Online]. Available: https://arxiv.org/abs/1512.07679
  16. “Spatial channel model for multiple input multiple output (MIMO) simulations,” 3GPP TR 25.996, Release 15, Jun. 2018.
  17. “Further advancements for E-UTRA physical layer aspects,” 3GPP TR 36.814, Release 9, Mar. 2017.
  18. “RF requirements for LTE pico node B,” 3GPP TR 36.931, Release 13, Jun. 2018.

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

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

Open Problems

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

Continue Learning

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

Collections

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