# AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2103.13791 · HTML: https://mlsanity.com/paper/2103.13791/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Pilot Contamination in Massive MIMO Systems: Challenges and Future Prospects](https://mlsanity.com/paper/2404.19238/) (2024)

## Abstract (arXiv)

In this paper, the problem of pilot contamination in a multi-cell massive multiple input multiple output (M-MIMO) system is addressed using deep reinforcement learning (DRL). To this end, a pilot assignment strategy is designed that adapts to the channel variations while maintaining a tolerable pilot contamination effect. Using the angle of arrival (AoA) information of the users, a cost function, portraying the reward, is presented, defining the pilot contamination effects in the system. Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.