# Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning

## TL;DR

This paper introduces a two-phase deep reinforcement learning algorithm for optimizing antenna parameters in heterogeneous cellular networks, balancing performance and scalability.

## Contribution

It proposes a practical two-step RL approach combining multi-agent mean field and single-agent deep RL for adaptive antenna tuning.

## Key findings

- Approaches multi-agent RL performance with fewer trials.
- Outperforms single-agent RL in dynamic environments.
- Maintains robustness regardless of environmental changes.

## Abstract

We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The interactions between the cells, most notably due to their coupled interference render this optimization prohibitively complex. Utilizing a single agent reinforcement learning (RL) algorithm for this optimization becomes quite suboptimum despite its scalability, whereas multi-agent RL algorithms yield better solutions at the expense of scalability. Hence, we propose a compromise algorithm between these two. Specifically, a multi-agent mean field RL algorithm is first utilized in the offline phase so as to transfer information as features for the second (online) phase single agent RL algorithm, which employs a deep neural network to learn users locations. This two-step approach is a practical solution for real deployments, which should automatically adapt to environmental changes in the network. Our results illustrate that the proposed algorithm approaches the performance of the multi-agent RL, which requires millions of trials, with hundreds of online trials, assuming relatively low environmental dynamics, and performs much better than a single agent RL. Furthermore, the proposed algorithm is compact and implementable, and empirically appears to provide a performance guarantee regardless of the amount of environmental dynamics.