Robust and Resilient Networks with Integrated Sensing, Communication and Computation

TL;DR

This work defines robust and resilient ISCC (R$^2$-ISCC) networks that jointly optimize sensing, communication, and computing under limited resources to support critical 6G applications. It frames a common RM architecture and introduces five interlinked enablers—robust/proactive RM, efficient information exchange, digital twins, distributed multi-tier/multi-modal architectures, and resilience-by-design—to realize robust and resilient operation. A distributed radar case study contrasts Baseline, Robust, Resilient, and Robust & Resilient strategies, illustrating how location uncertainty and blockages shape end-to-end sensing performance, and highlighting the trade-offs between efficiency and reliability. The paper also outlines open research problems spanning scalable RM, task-centric designs, network coding for resilience, architectural diversification, and self-organizing ISCC networks, setting a roadmap for future R$^2$-ISCC developments.

Abstract

Emerging applications such as networked robotics, intelligent transportation, smart factories, and virtual and augmented reality demand integrated perception and connectivity enabled by wireless communication. This has driven growing interests in integrated sensing, communication, and computation (ISCC) systems, with a primary focus on their efficient co-designs. However, as ISCC systems increasingly support critical applications, they must not only deliver high performance but also demonstrate robustness and resilience. In this context, robustness refers to a system's ability to maintain performance under uncertainties, while resilience denotes its capacity to sustain a minimum level of service in the face of major disruptions. To address this gap, this article presents an overview of ISCC systems from the perspectives of robustness and resilience under limited resources. First, key concepts related to these properties are introduced in the ISCC context. Subsequently, design approaches for realizing robust and resilient ISCC networks are discussed. Finally, the article concludes with the discussions of a case study and open research problems in this area.

Figures (6)

• Figure 1: Key enablers of R$^2$-ISCC networks: (1) robust and proactive resource management, (2) efficient information exchange, both supported by (3) digital twins, as well as (4) architectural enablers and (5) holistic robust-and-resilient-by-design approaches.
• Figure 2: Illustration of the continuous spectrum from robust to resilient designs, based on the rareness of disruption events and the associated system states.
• Figure 3: Illustration of different phases in an ISCC network with regard to robustness and resilience. This figure underlines that robustness and resilience are intertwined and characterize the system's capability to withstand a spectrum of potential uncertainties and disruptions.
• Figure 4: Three classes of concepts for realizing R$^2$-ISCC systems. Concrete examples of these strategies are presented in Subsections III-B to III-F.
• Figure 5: Illustration of a distributed sensor setup for cooperative target tracking. The true target location is uncertain (within blue circles); the estimated location (flag) includes an uncertainty region (green circles). Moreover, sensor views may be obstructed illustrated by walls.