Reinforcement Learning-Based Adaptive Data Rate Control in Mobile LoRaWAN with Imperfect CSI

Abstract

Low Power Wide Area Networks (LPWANs), particularly LoRaWAN, are increasingly being deployed in mobile Internet of Things (IoT) applications. In these applications, adaptive data rate (ADR) control is essential for maintaining reliable and energy-efficient communication under dynamic wireless conditions. However, ADR performance remains constrained when channel state information (CSI) is imperfect. Although reinforcement learning (RL)-based ADR methods have demonstrated notable improvement over conventional approaches, their effectiveness under imperfect CSI deployment scenarios remains insufficiently explored. In this paper, we investigate the robustness of RL-based ADR approaches with imperfect CSI for mobile LoRaWAN networks using a mobility-aware simulation framework. Standard ADR, heuristic ADR, Deep Q-Network (DQN)-based ADR, and Proximal Policy Optimisation (PPO)-based ADR are evaluated across varying mobility patterns, network layouts, and channel dynamics. Key performance metrics include packet delivery ratio, latency, runtime efficiency, and retention of baseline capability are evaluated. The Results show that PPO achieves the best performance under imperfect CSI conditions, attaining a mean packet delivery ratio of 0.9954, outperforming standard ADR, heuristic ADR, and DQN by 29.49%, 19.12%, and 1.55%, respectively. In addition, PPO reduced latency by 53.12% and runtime by 81.94% compare to DQN while preserving over 99% of baseline communication reliability under scenario variation. These findings demonstrate that policy-gradient learning provides better transferability, robustness, and deployment readiness compared with conventional and value-based ADR approaches. The study highlights the importance of evaluating intelligent wireless controllers not only for optimisation performance, but also for cross-scenario generalisation before real-world deployment in mobile LPWAN systems.