# Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations

## TL;DR

This paper proposes a reinforcement learning approach to optimize the trajectories of aerial base stations in a communication network, enhancing user data rates by jointly optimizing trajectory, power, and sub-channel allocation.

## Contribution

It introduces a distributed Q-learning algorithm for joint trajectory, power, and sub-channel optimization in multi-ABS networks, reducing information exchange needs.

## Key findings

- Q-learning effectively trains ABS trajectories based on network topology.
- The proposed method improves sum-rate performance.
- Distributed approach reduces communication overhead.

## Abstract

In this paper, the trajectory optimization problem for a multi-aerial base station (ABS) communication network is investigated. The objective is to find the trajectory of the ABSs so that the sum-rate of the users served by each ABS is maximized. To reach this goal, along with the optimal trajectory design, optimal power and sub-channel allocation is also of great importance to support the users with the highest possible data rates. To solve this complicated problem, we divide it into two sub-problems: ABS trajectory optimization sub-problem, and joint power and sub-channel assignment sub-problem. Then, based on the Q-learning method, we develop a distributed algorithm which solves these sub-problems efficiently, and does not need significant amount of information exchange between the ABSs and the core network. Simulation results show that although Q-learning is a model-free reinforcement learning technique, it has a remarkable capability to train the ABSs to optimize their trajectories based on the received reward signals, which carry decent information from the topology of the network.