Deep Deterministic Policy Gradient for Relay Selection and Power Allocation in Cooperative Communication Network

TL;DR

This paper introduces a deep reinforcement learning framework that optimizes relay selection and power allocation in cooperative communication networks without requiring perfect channel state information, improving success rates.

Contribution

It proposes a prioritized experience replay deep deterministic policy gradient method for relay and power optimization without needing prior CSI knowledge.

Findings

Outperforms existing reinforcement learning methods.

Increases communication success rate by approximately 4%.

Effectively handles continuous action spaces.

Abstract

Perfect channel state information (CSI) is usually required when considering relay selection and power allocation in cooperative communication. However, it is difficult to get an accurate CSI in practical situations. In this letter, we study the outage probability minimizing problem based on optimizing relay selection and transmission power. We propose a prioritized experience replay aided deep deterministic policy gradient learning framework, which can find an optimal solution by dealing with continuous action space, without any prior knowledge of CSI. Simulation results reveal that our approach outperforms reinforcement learning based methods in existing literatures, and improves the communication success rate by about 4%.