Report on Current Developments in Wireless Communication and Sensing
General Direction of the Field
The recent advancements in wireless communication and sensing (ISAC) systems are pushing the boundaries of both technologies, aiming to integrate them seamlessly while optimizing performance and efficiency. The field is witnessing a significant shift towards leveraging machine learning (ML) and artificial intelligence (AI) to tackle the complexities introduced by distributed and large-scale systems. This approach is particularly evident in the design of beamforming strategies, where graph neural networks (GNNs) are being employed to manage the intricacies of cell-free massive MIMO systems. These ML-driven methods promise scalable solutions that adapt dynamically to changes in network topology without requiring full retraining, thereby enhancing operational efficiency.
Energy efficiency remains a paramount concern, especially in emerging technologies like terahertz (THz) communication. The focus is on developing novel schemes that reduce the overhead associated with beamforming training, thereby minimizing power consumption and latency. These advancements are crucial for realizing the potential of THz communication in next-generation (6G) systems, where ultra-high data rates are expected.
Security and privacy are also gaining prominence, with innovative solutions being proposed for secure communication in the presence of eavesdroppers. The use of reconfigurable intelligent surfaces (RISs) and specifically, simultaneously transmitting and reflecting RISs (STAR-RISs), is emerging as a powerful tool for enhancing secure communication while balancing energy harvesting constraints. These systems are being optimized to dynamically manipulate the wireless environment to thwart eavesdropping attempts, even under imperfect channel state information (CSI).
Fairness and resource allocation in wireless networks are being redefined through cooperative spectrum sharing and novel channel access procedures. The emphasis is on developing protocols that not only improve spectrum utilization but also ensure equitable access for all devices, thereby reducing end-to-end delays and congestion.
Noteworthy Papers
Learning Beamforming in Cell-Free Massive MIMO ISAC Systems: The integration of GNNs for scalable and adaptive beamforming in cell-free ISAC systems is a significant advancement, offering near-optimal performance across various network structures.
Energy Efficient Beamforming Training in Terahertz Communication Systems: The proposed energy-efficient THz beamforming scheme significantly reduces training latency and power consumption, achieving superior effective rate and energy efficiency compared to existing methods.
Outage-Constrained Sum Secrecy Rate Maximization for STAR-RIS with Energy-Harvesting Eavesdroppers: The novel strategy using STAR-RIS to enhance secure communication while managing energy harvesting constraints demonstrates robust performance, even under imperfect CSI.
Enhancing User Fairness in Wireless Powered Communication Networks with STAR-RIS: The proposed STAR-RIS-assisted WPCN strategies significantly improve user fairness and performance, with notable advantages in both single-antenna and multi-antenna scenarios.
