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.

翻译：随着异构无线网络（HWNs）的快速发展，多种通信技术设备共存，对高效、自适应的多跳多数据流路由的需求日益增长。传统路由方法专为同构环境设计，无法应对由多技术链路、频率相关衰落及动态拓扑变化带来的复杂性。本文提出一种基于深度强化学习（DRL）的路由框架，利用深度Q网络（DQN）在HWN中为多源-目的对建立路由，使每个节点能够联合选择通信技术、子频带及下一跳中继，以最大化路由速率。该方法融合了信道感知与干扰感知的邻居选择策略，以超越传统基于距离的启发式方法，提升决策质量。我们进一步评估了所提方法在不同网络动态（包括节点移动性、节点密度变化及数据流数量）下的鲁棒性与泛化能力。仿真结果表明，该基于DRL的路由框架在复杂HWN场景中显著提升了可扩展性、自适应性与端到端吞吐量。