Enhancing Indoor THz Multi-AP Joint Transmission With IRS: A Clustering and Kalman Filtering Approach for Mobile User
Yishi Zhu, Yuichi Kawamoto, Nei Kato, Kazuto Yano, Toshikazu Sakano · IEEE Transactions on Cognitive Communications and Networking · 2024
Demands for high-speed wireless connectivity are pushing the boundaries of Wireless Local Area Networks (WLANs), prompting a transition towards Terahertz (THz) technology for 6G networks. THz technology is capable of supporting higher data rates and lower latency, enabling the miniaturization and denser placements of Access Points (APs), particularly in urban environments. Joint transmission strategies, coordinating data from multiple APs, can further improve THz network performance and reliability. Despite its advantages, THz signals face challenges with signal penetration and attenuation. To tackle these issues, Intelligent Reflecting Surfaces (IRS), a planar surface made with reconfigurable meta-material elements, have emerged as a solution with the abilities to create beyond Line-of-Sight (LoS) communication and passive beamforming. In this paper, we study the channel estimation and resource allocation problem with considering the user mobility and increased system complexity. A combined clustering and Kalman Filtering (KF) approach is proposed to continuously estimate user statuses and efficiently organize network elements. The simulation results demonstrate that our approach markedly enhances the performance of joint transmission and reduces the overhead associated with channel estimation, thereby highlighting the potential of integrating THz joint transmission and IRS in future WLANs.