QL-6GRP: A Lightweight Q-Learning-Based Routing Protocol for Dynamic MANETs in 6G-Oriented Environments
Mobile Ad Hoc Networks (MANETs) are expected to support highly dynamic and decentralized communication scenarios in future 6G-oriented wireless systems. However, routing remains challenging because of mobility, topology variability, and resource constraints. Reinforcement learning (RL) offers a promising alternative by enabling adaptive routing decisions based on observed network conditions. This paper presents QL-6GRP (Q-Learning for 6G Routing Protocol), a lightweight Q-learning-based routing protocol designed for fully distributed MANET environments. The protocol enables each node to learn next-hop forwarding decisions using local observations, including link quality, residual energy, hop progress, and neighborhood density. A complete implementation of QL-6GRP was developed within the NS-3 simulator, supporting online learning through hop-level feedback signaling and bounded-memory operation. The protocol was evaluated under multiple parameter settings and network sizes using Random Waypoint mobility and UDP constant-bit-rate traffic to examine both routing performance and computational behavior. The experimental results demonstrate the feasibility of adaptive routing with moderate signaling overhead under carefully tuned moderate-scale scenarios while revealing key trade-offs between feedback frequency, routing quality, and computational scalability. Moderate periodic feedback provides the most favorable balance, whereas excessive feedback increases overhead without improving performance. In addition, reinforcement-learning operations incur substantial computational costs, with the wall-clock runtime increasing by approximately 13 times when the network size increases from 50 to 100 nodes. These findings reveal the operating limits and practical design trade-offs of lightweight tabular RL-based MANET routing and provide useful guidelines for future scalable learning-driven protocols in dynamic wireless environments.