A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks

TL;DR

This paper introduces a deep Q-learning approach for downlink power allocation in multi-cell wireless networks, aiming to maximize throughput efficiently without requiring optimal training data.

Contribution

It presents a novel centralized DRL-based power allocation method that outperforms traditional schemes and benchmarks with genetic algorithms in multi-cell scenarios.

Findings

DRL-based scheme achieves higher throughput than conventional methods

Proposed method approaches near-optimal solutions

Simulation confirms improved performance in dense networks

Abstract

Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections. Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation. Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems. Unlike deep learning (DL), DRL does not require any optimal/ near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data. In this paper, we propose a novel centralized DRL based downlink power allocation scheme for a multi-cell system intending to maximize the total network throughput. Specifically, we apply a deep Q-learning (DQL) approach to achieve near-optimal power allocation policy. For benchmarking the proposed approach, we use a Genetic Algorithm (GA) to obtain near-optimal power allocation solution. Simulation results show that the proposed DRL-based power allocation scheme performs better compared to the conventional power allocation schemes in a multi-cell scenario.