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

arXiv 2404.02477 ↗First posted 3 Apr 2024eess.SP cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 19 references that are in the map. First posted on arXiv 3 Apr 2024.

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4 of 19 references.

References · 4

  1. 2015Prioritized Experience Replaycited by 1,099
  2. 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 161
  3. 2017Learning to Optimize: Training Deep Neural Networks for Wireless Resource Managementcited by 96
  4. 2019Sum-Rate Maximization of Multicell MISO Networks with Limited Information Exchangecited by 2

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