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

TL;DR

This paper proposes a reinforcement learning approach for per-antenna power control in massive MIMO systems, aiming to optimize energy efficiency and meet user QoS constraints amid channel variability.

Contribution

It introduces a Q-learning based method for dynamic power allocation in massive MIMO, addressing energy efficiency and QoS under Markov channel conditions.

Findings

Power consumption is minimized while maintaining SINR thresholds.

The method adapts to channel variations modeled as a Markov process.

Simulation results validate the effectiveness of the proposed approach.

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.