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

2025-11-03

AI Summary

Adaptive multi-hop routing for multiple data streams in heterogeneous wireless networks (HWNs) faces significant challenges due to dynamic topologies, frequency-selective fading, and coexistence of diverse radio access technologies. Method: This paper proposes a channel- and interference-aware deep reinforcement learning (DRL) routing framework centered on a Deep Q-Network (DQN). It jointly models action spaces comprising radio technology selection, subband allocation, and next-hop decision-making. Crucially, it introduces a novel neighbor selection mechanism grounded in real-time channel state information and interference assessment, overcoming the limitations of conventional distance-based heuristics. Results: Experiments demonstrate that the framework substantially improves end-to-end throughput, scalability, and environmental adaptability. It exhibits superior robustness over classical routing protocols under dynamic conditions—including node mobility, varying network density, and concurrent multi-flow traffic—while maintaining efficient resource utilization and cross-technology coordination.

Technology Category

• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Optimizing multi-flow routing in heterogeneous wireless networks
• Overcoming limitations of traditional routing in dynamic environments
• Enhancing scalability and throughput with deep reinforcement learning

Innovation

• Deep reinforcement learning optimizes multi-flow routing
• DQN jointly selects technology subband and relay
• Channel interference-aware neighbor selection improves decisions