Current Developments in Wireless Power Transfer and Integrated Sensing and Communication Systems
The recent advancements in wireless power transfer (WPT) and integrated sensing and communication (ISAC) systems have shown significant promise in enhancing the efficiency, reliability, and security of wireless networks. This report summarizes the key developments in these areas, highlighting the innovative approaches and results that are pushing the boundaries of current technology.
General Direction of the Field
Wireless Power Transfer (WPT): The field of WPT is evolving towards more adaptive and efficient methods for powering energy-neutral devices (ENDs) in large-scale networks. Recent research has focused on optimizing the initial power delivery to devices with unknown locations and depleted energy levels. Adaptive single-tone and multi-tone signals have been experimentally evaluated, showing that single-tone excitation can provide faster response times and better performance at lower transmit power levels. This suggests a shift towards more dynamic and responsive power delivery mechanisms that can adapt to varying network conditions.
Integrated Sensing and Communication (ISAC): ISAC systems are becoming increasingly sophisticated, with advancements in both sensing and communication capabilities. The integration of massive multiple-input multiple-output (MIMO) architectures with intelligent reflecting surfaces (IRSs) and reconfigurable intelligent surfaces (RISs) is enabling more robust and efficient monitoring and communication. Techniques such as distributed beamforming, spatial multiplexing, and proactive monitoring are being explored to enhance the performance of ISAC systems, particularly in scenarios involving malicious activities or large-scale surveillance.
Optimization and Fairness in Networks: Efforts to optimize network performance while ensuring user fairness are also gaining traction. Strategies such as energy splitting non-orthogonal multiple access (ES-NOMA) and time switching time division multiple access (TS-TDMA) are being proposed to eliminate the doubly-near-far effect in wireless powered communication networks (WPCNs). These strategies involve joint optimization of time allocation, user transmit power, and beamforming, leading to improved throughput and fairness.
Machine Learning and AI Integration: The integration of machine learning (ML) and artificial intelligence (AI) techniques is becoming a key enabler in optimizing network operations. For instance, parametric channel state information (CSI) feedback techniques using deep learning (DL) are being developed to reduce CSI feedback overhead in mmWave massive MIMO systems. These techniques leverage geometric channel parameters to compress the CSI, thereby improving system efficiency and performance.
Noteworthy Papers
Enhancing User Fairness in Wireless Powered Communication Networks with STAR-RIS:
- Proposes innovative STAR-RIS protocols to eliminate the doubly-near-far effect, significantly improving network fairness and throughput.
CSI Acquisition in Cell-Free Massive MIMO Surveillance Systems:
- Introduces a robust CSI acquisition scheme that enables accurate monitoring of untrusted links, enhancing security and performance in surveillance systems.
Transformer-assisted Parametric CSI Feedback for mmWave Massive MIMO Systems:
- Demonstrates a novel DL-based approach to reduce CSI feedback overhead, significantly improving the efficiency and performance of mmWave massive MIMO systems.
These papers represent some of the most innovative and impactful contributions to the field, offering promising directions for future research and development.
