Deep Reinforcement Learning for Multi-flow Routing in Heterogeneous Wireless Networks

TL;DR

This paper introduces a deep reinforcement learning framework for multi-flow routing in heterogeneous wireless networks, enabling adaptive, efficient, and scalable route selection amidst dynamic network conditions.

Contribution

It presents a novel DRL-based routing approach that jointly optimizes technology, subband, and relay selection, improving performance over traditional methods in HWNs.

Findings

Significantly improves end-to-end throughput in HWNs.

Enhances scalability and adaptability under dynamic network conditions.

Demonstrates robustness across various mobility and density scenarios.

Abstract

Due to the rapid growth of heterogeneous wireless networks (HWNs), where devices with diverse communication technologies coexist, there is increasing demand for efficient and adaptive multi-hop routing with multiple data flows. Traditional routing methods, designed for homogeneous environments, fail to address the complexity introduced by links consisting of multiple technologies, frequency-dependent fading, and dynamic topology changes. In this paper, we propose a deep reinforcement learning (DRL)-based routing framework using deep Q-networks (DQN) to establish routes between multiple source-destination pairs in HWNs by enabling each node to jointly select a communication technology, a subband, and a next hop relay that maximizes the rate of the route. Our approach incorporates channel and interference-aware neighbor selection approaches to improve decision-making beyond conventional distance-based heuristics. We further evaluate the robustness and generalizability of the proposed method under varying network dynamics, including node mobility, changes in node density, and the number of data flows. Simulation results demonstrate that our DRL-based routing framework significantly enhances scalability, adaptability, and end-to-end throughput in complex HWN scenarios.