Report on Current Developments in Integrated Sensing, Communications, and Edge Computing
General Direction of the Field
The recent advancements in the research area of integrated sensing, communications, and edge computing are significantly shaping the future of wireless technologies. The field is moving towards more efficient, intelligent, and adaptive systems that leverage novel hardware and algorithmic innovations to enhance performance and energy efficiency. Key developments include the integration of reconfigurable intelligent surfaces (RISs) and multi-access edge computing (MEC) to address challenges in spectrum efficiency, latency, and energy consumption.
Intelligent Metasurfaces and Integrated Systems: There is a growing trend towards using stacked intelligent metasurfaces (SIM) for integrated sensing and communications. These systems are designed to optimize spectrum efficiency and power allocation by jointly controlling phase shifts and power distribution. The innovation here lies in the wave-domain precoding techniques that mitigate inter-user interference and enhance sensing capabilities.
Optimization of Quality of Service (QoS) in Autonomous Systems: Researchers are focusing on dynamic allocation strategies for network parameters in autonomous vehicle networks to improve latency and prioritize services. Cross-layer solutions and multi-agent techniques are being employed to optimize parameters like contention windows and inter-frame spaces, significantly reducing latency for various services.
Energy-Efficient Reconfigurable Surfaces: The introduction of rotatable block-controlled RIS (BC-RIS) is a notable advancement, aiming to reduce power consumption while maintaining spectral efficiency. This approach contrasts with traditional element-controlled RIS by using block-level phase control, thereby lowering power requirements and enhancing energy efficiency.
Enhanced Edge Computing Systems: The integration of active STAR-RIS with MEC systems is a promising direction for improving energy efficiency and task management. These systems jointly optimize task offloading, phase shifts, and power control, demonstrating superior performance in handling data-intensive applications.
Collaborative Computing and Multi-Objective Optimization: In satellite-terrestrial networks, collaborative computing schemes are being developed to optimize task offloading and resource allocation. These schemes use multi-agent algorithms to adaptively manage network resources and enhance service performance, particularly in video streaming and IoT services.
Online Low-Latency and Fresh Service Provisioning: Edge computing networks are being optimized for low-latency and fresh service provisioning through joint optimization of service caching, task offloading, and resource allocation. Lyapunov-based online frameworks and deep reinforcement learning techniques are being employed to achieve near-optimal solutions.
Noteworthy Papers
- Stacked Intelligent Metasurfaces for Integrated Sensing and Communications: This paper introduces an innovative approach to wave-domain precoding that significantly mitigates inter-user interference and enhances sensing capabilities.
- Active STAR-RIS Empowered Edge System for Enhanced Energy Efficiency and Task Management: The proposed system demonstrates superior performance in energy efficiency and task management, outperforming conventional RIS-assisted systems by a significant margin.
These developments highlight the transformative potential of integrating advanced hardware with intelligent algorithms to create more efficient, reliable, and sustainable communication and computing systems. The field is poised for further innovations as these technologies continue to evolve and converge.
