Generalizable Deep Reinforcement Learning-Based Intelligent Handover in Indoor WiGig Networks
Hamza Kaddour, Eslam Hasan, Mostafa M. Fouda, Muhammad Ismail, Zubair Md Fadlullah, Nei Kato
Abstract
The dynamic nature of user mobility and density in indoor WiGig networks poses a significant challenge to seamless handover, particularly in the 60 GHz band, where small and closely clustered channel gain values hinder effective decision-making. To address this, we propose a generalizable deep reinforcement learning (DRL)-based handover that integrates a novel reward function designed to amplify channel gain differentials, thereby improving the convergence speed and decision accuracy of learning agents. We investigate the performance of state-of-the-art DRL algorithms—Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C)—enhanced through advanced hyperparameter-tuning techniques, including grid search, random search, optuna, and hyperopt. Among these, DQN combined with grid search yields the best overall performance, surpassing A2C by 48% in average rolling reward and achieving 32% faster convergence than PPO.To assess generalization, we evaluate the merged DQN agent trained across varying user density scenarios (1–8 users) against expert agents specialized for individual densities and a high-density-trained agent tested across all scenarios. Our results reveal that the merged agent exhibits robust and consistent performance across all densities, indicating strong generalization capability. In contrast, the high-density agent suffers performance degradation of up to 13% when exposed to unseen scenarios, underscoring its limited adaptability. While expert agents perform optimally within their specific environments, their deployment complexity renders them impractical for real-time systems. These findings highlight the importance of training DRL agents across diverse scenarios to achieve scalable and generalizable handover solutions in dense and dynamic WiGig networks.