HybridRL-RNP: A Hybrid Reinforcement Learning Framework for Optimizing Router Nodes Placement in Wireless Mesh Networks Toward 6G

Abstract

In Wireless Mesh Networks (WMNs), router node placement (RNP) is critical for achieving network-wide coverage, connectivity, and reliable communication performance. However, determining the optimal router placement is a nonlinear combinatorial problem with a vast and dynamic search space, which is strongly influenced by user distribution and traffic demand. To address this challenge, this study introduces a Hybrid Reinforcement Learning Framework, called HybridRL-RNP, designed to optimize router placement in WMNs. The proposed approach integrates the REINFORCE algorithm with a heuristic-guided placement strategy, enabling the learning agent to adaptively select router positions based on the current network coverage and the node density. A reward function is formulated using network connectivity (NC), which is the proportion of user nodes connected to at least one gateway as the primary optimization objective, while also considering coverage uniformity and router interconnectivity. Simulation experiments demonstrate that HybridRL-RNP achieves an average NC exceeding 96%, outperforming traditional heuristic-based and pure RL-based schemes. Moreover, the framework ensures stable inter-router topologies and scalable coverage performance for varying network densities. These results highlight the effectiveness and practicality of the proposed HybridRL-RNP framework as an intelligent topology control solution for Wireless Mesh Networks towards 6G, where the synergy between AI-driven optimization and heuristic knowledge plays a pivotal role in achieving globally optimal network connectivity.