Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems
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.
Cite
@article{arxiv.2101.12154,
title = {Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems},
author = {Navneet Garg and Mathini Sellathurai and Tharmalingam Ratnarajah},
journal= {arXiv preprint arXiv:2101.12154},
year = {2021}
}