# Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks

arXiv: https://arxiv.org/abs/2103.11782 · HTML: https://mlsanity.com/paper/2103.11782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [Learning Based Frequency- and Time-Domain Inter-Cell Interference Coordination in HetNets](https://mlsanity.com/paper/1411.5548/) (2014)

## Abstract (arXiv)

Beamforming is an essential technology in the 5G massive multiple-input-multiple-output (MMIMO) communications, which are subject to many impairments due to the nature of wireless transmission channel, i.e. the air. The inter-cell interference (ICI) is one of the main impairments faced by 5G communications due to frequency-reuse technologies. In this paper, we propose a reinforcement learning (RL) assisted full dynamic beamforming for ICI mitigation in 5G downlink. The proposed algorithm is a joint of beamforming and full dynamic Q-learning technology to minimize the ICI, and results in a low-complexity method without channel estimation. Performance analysis shows the quality of service improvement in terms of signal-to-interference-plus-noise-ratio (SINR) and computational complexity compared to other algorithms.