Lightweight Adaptive Reinforcement Learning-Based TCP Congestion Control for Multi-Hop Ad Hoc Networks

Ad hoc networks are characterized by flexible deployment and multi-hop communication, which has facilitated their growing prevalence in diverse applications. However, the TCP protocol exhibits substantial performance degradation in multi-hop ad hoc networks with dynamic topologies. To address this issue, this paper proposes TCP-RLA, a lightweight adaptive reinforcement learning-based TCP congestion control algorithm. It predicts network state variations and leverages a deep Q-network (DQN) with a rule-assisted discrete action space to adaptively tune the congestion window. This design boosts convergence speed and reduces computational complexity, making it well-suited for resource-constrained ad hoc nodes. Simulation results demonstrate that, compared with two reinforcement learning-based algorithms (GVegas and Orca), TCP-RLA achieves an average throughput improvement of 36.1% and 43.3%, an average round-trip time (RTT) reduction of 13.1% and 47.9%, and an average packet loss rate (PLR) reduction of 33.3% and 50%, respectively.