Deep Incremental Learning-Driven Human Body Channel Prediction With Adaptive Relay Selection for Enhanced WBAN Performance - Yunlin Pan; Yaxuan Ren; Jiasong Mu (IEEE Access)
With the widespread application of wireless body area networks in medical monitoring and human health management, improving its network performance and reliability has become a key issue. However, the inherent complex spatiotemporal characteristics of WBAN channels pose great challenges to the improvement of network performance. This paper proposes a human body channel prediction model based on deep learning and incremental learning to effectively capture the dynamic changes of human body channels. This model can adaptively predict future channel quality by combining historical channel data and real-time update capabilities. At the same time, this paper also designs an optimal adaptive relay selection algorithm, which achieves intelligent selection of relay nodes by evaluating channel quality and node energy consumption, further enhancing the robustness and transmission efficiency of the network. Simulation results show that the proposed method has significant advantages in channel prediction accuracy, relay selection effect and network performance improvement, providing new technical support for the practical application of WBAN.