Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems

Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Power consumption is one of the major issues in massive MIMO (multiple input multiple output) systems, causing increased long-term operational cost and overheating issues. In this paper, we consider per-antenna power allocation with a given finite set of power levels towards maximizing the long-term energy efficiency of the multi-user systems, while satisfying the QoS (quality of service) constraints at the end users in terms of required SINRs (signal-to-interference-plus-noise ratio), which depends on channel information. Assuming channel states to vary as a Markov process, the constraint problem is modeled as an unconstraint problem, followed by the power allocation based on Q-learning algorithm. Simulation results are presented to demonstrate the successful minimization of power consumption while achieving the SINR threshold at users.

Original languageEnglish
Title of host publication54th Asilomar Conference on Signals, Systems, and Computers 2020
EditorsMichael B. Matthews
PublisherIEEE
Pages1028-1032
Number of pages5
ISBN (Electronic)9780738131269
DOIs
Publication statusPublished - 3 Jun 2021
Event54th Asilomar Conference on Signals, Systems and Computers 2020 - Pacific Grove, United States
Duration: 1 Nov 20205 Nov 2020

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers 2020
Abbreviated titleACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period1/11/205/11/20

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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