Interference Mitigation for Network-Level ISAC: An Optimization Perspective

TL;DR

The paper addresses the interference challenge in network-level ISAC, where SI, MI, clutter, crosstalk, and MUI arise in joint sensing and communication. It links interference mitigation techniques to network-wide optimization by classifying interferences and proposing five techniques (CMT, IA, HD-BF, TS, SA) with corresponding problem formulations and solution methods, validated via two case studies. The authors provide a systematic taxonomy, optimization-guided design, and case-study validation showing improvements over baselines in sensing accuracy and communication QoS. They outline future directions including IRS-enhanced ISAC, robust designs under CSI uncertainty, and DL-based low-complexity mitigation to unlock the full potential of network-level ISAC.

Abstract

Future wireless networks are envisioned to simultaneously provide high data-rate communication and ubiquitous environment-aware services for numerous users. One promising approach to meet this demand is to employ network-level integrated sensing and communications (ISAC) by jointly designing the signal processing and resource allocation over the entire network. However, to unleash the full potential of network-level ISAC, some critical challenges must be tackled. Among them, interference management is one of the most significant ones. In this article, we build up a bridge between interference mitigation techniques and the corresponding optimization methods, which facilitates efficient interference mitigation in network-level ISAC systems. In particular, we first identify several types of interference in network-level ISAC systems, including self-interference, mutual interference, crosstalk, clutter, and multiuser interference. Then, we present several promising techniques that can be utilized to suppress specific types of interference. For each type of interference, we discuss the corresponding problem formulation and identify the associated optimization methods. Moreover, to illustrate the effectiveness of the proposed interference mitigation techniques, two concrete network-level ISAC systems, namely coordinated cellular network-based and distributed antenna-based ISAC systems, are investigated from interference management perspective. Experiment results indicate that it is beneficial to collaboratively employ different interference mitigation techniques and leverage the network structure to achieve the full potential of network-level ISAC. Finally, we highlight several promising future research directions for the design of ISAC systems.

Figures (4)

• Figure 1: Illustration of a network-level ISAC system where two DFRC BSs serve three CUs, two STs, and one ISAC user. The colored ellipses represent the transmit signals while the dashed-line arrows denote the interference in the ISAC system. The communication signal carries the information symbols for the CUs and the sensing signal is employed to sense the desired target. The ISAC signal refers to a signal that can provide sensing and communication services simultaneously.
• Figure 2: Illustration of the considered ISAC system with four BSs, five CUs, and one ST.
• Figure 3: Infeasibility rate versus minimum SINR requirement.
• Figure 4: Average total energy consumption during an ISAC frame (dBm) versus the total number of transmit antennas for different schemes.