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Reconfigurable Intelligent Surface for Sensing, Communication, and Computation: Perspectives, Challenges, and Opportunities

Bin Li, Wancheng Xie, Zesong Fei

TL;DR

This paper introduces a reconfigurable intelligent surface (RIS)-enabled framework for Integrated Sensing, Communication, and Computation (ISCC) to address spectrum scarcity and latency in 6G. It articulates how RIS can simultaneously enhance radar sensing, data transmission, and edge computing by enabling cross-layer optimization and intelligent environmental shaping, supported by a DRL-based design. The authors outline a unified RIS-enabled ISCC architecture, discuss fundamental challenges, and present two application scenarios (UAV and IoV) with a DRL-driven case study demonstrating energy efficiency improvements over non-RIS setups. The work highlights future directions including distributed RIS collaboration, digital twins, THz-band ISCC, and sustainable network design, underscoring RIS’s potential to significantly augment ISCC performance in future wireless ecosystems.

Abstract

Forthcoming 6G networks have two predominant features of wide coverage and sufficient computation capability. To support the promising applications, Integrated Sensing, Communication, and Computation (ISCC) has been considered as a vital enabler by completing the computation of raw data to achieve accurate environmental sensing. To help the ISCC networks better support the comprehensive services of radar detection, data transmission and edge computing, Reconfigurable Intelligent Surface (RIS) can be employed to boost the transmission rate and the wireless coverage by smartly tuning the electromagnetic characteristics of the environment. In this article, we propose an RIS-assisted ISCC framework and exploit the RIS benefits for improving radar sensing, communication and computing functionalities via cross-layer design, while discussing the key challenges. Then, two generic application scenarios are presented, i.e., unmanned aerial vehicles and Internet of vehicles. Finally, numerical results demonstrate a superiority of RIS-assisted ISCC, followed by a range of future research directions.

Reconfigurable Intelligent Surface for Sensing, Communication, and Computation: Perspectives, Challenges, and Opportunities

TL;DR

This paper introduces a reconfigurable intelligent surface (RIS)-enabled framework for Integrated Sensing, Communication, and Computation (ISCC) to address spectrum scarcity and latency in 6G. It articulates how RIS can simultaneously enhance radar sensing, data transmission, and edge computing by enabling cross-layer optimization and intelligent environmental shaping, supported by a DRL-based design. The authors outline a unified RIS-enabled ISCC architecture, discuss fundamental challenges, and present two application scenarios (UAV and IoV) with a DRL-driven case study demonstrating energy efficiency improvements over non-RIS setups. The work highlights future directions including distributed RIS collaboration, digital twins, THz-band ISCC, and sustainable network design, underscoring RIS’s potential to significantly augment ISCC performance in future wireless ecosystems.

Abstract

Forthcoming 6G networks have two predominant features of wide coverage and sufficient computation capability. To support the promising applications, Integrated Sensing, Communication, and Computation (ISCC) has been considered as a vital enabler by completing the computation of raw data to achieve accurate environmental sensing. To help the ISCC networks better support the comprehensive services of radar detection, data transmission and edge computing, Reconfigurable Intelligent Surface (RIS) can be employed to boost the transmission rate and the wireless coverage by smartly tuning the electromagnetic characteristics of the environment. In this article, we propose an RIS-assisted ISCC framework and exploit the RIS benefits for improving radar sensing, communication and computing functionalities via cross-layer design, while discussing the key challenges. Then, two generic application scenarios are presented, i.e., unmanned aerial vehicles and Internet of vehicles. Finally, numerical results demonstrate a superiority of RIS-assisted ISCC, followed by a range of future research directions.
Paper Structure (18 sections, 5 figures, 1 table)

This paper contains 18 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. RIS-Enabled Multi-Functional Networks
  3. RIS for Sensing and Communication
  4. RIS for Communication and Computation
  5. RIS for Sensing, Communication, and Computation
  6. Key Challenges
  7. Applications of Multi-Functional Networks
  8. ISCC in UAV Networks
  9. ISCC in IoV
  10. Case Study: Exploiting RIS for ISCC
  11. Scenario Setting
  12. Results Evaluation
  13. Future Research Opportunities
  14. Distributed and Collaborative ISCC
  15. Digital Twin for ISCC
  16. ...and 3 more sections

Figures (5)

  • Figure 1: An illustrative scenario of the considered ISCC network, where some IoT devices may reach the edge nodes (i.e., BS, RSU, and UAV) through an indirect path supported by RIS with the configurable phase shifts.
  • Figure 2: Typical RIS-aided sensing situations.
  • Figure 3: The performance region of ISCC.
  • Figure 4: Radar beampatterns obtained with and without the aid of RIS.
  • Figure 5: Energy consumption versus the number of users.