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

arXiv: https://arxiv.org/abs/2101.12154 · HTML: https://mlsanity.com/paper/2101.12154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding](https://mlsanity.com/paper/1901.06537/) (2019)

## Abstract (arXiv)

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.