Toward Multiple Integrated Sensing and Communication Base Station Systems: Collaborative Precoding Design with Power Constraint

TL;DR

This paper proposes a joint optimization algorithm for collaborative precoding in multiple ISAC base stations, effectively eliminating mutual interference while balancing sensing and communication performance under power constraints.

Contribution

It introduces a mutual interference model and a novel joint optimization algorithm for precoding in multi-ISAC systems with power constraints, enhancing collaborative sensing capabilities.

Findings

JOA effectively balances sensing and communication performance.

The algorithm reduces mutual interference among ISAC base stations.

Complexity analysis shows efficient implementation of JOA.

Abstract

The collaborative sensing of multiple Integrated sensing and communication (ISAC) base stations is one of the important technologies to achieve intelligent transportation. Interference elimination between ISAC base stations is the prerequisite for realizing collaborative sensing. In this paper, we focus on the mutual interference elimination problem in collaborative sensing of multiple ISAC base stations that can communicate and radar sense simultaneously by transmitting ISAC signals. We establish a mutual interference model of multiple ISAC base stations, which consists of communication and radar sensing related interference. Moreover, we propose a joint optimization algorithm (JOA) to solve the collaborative precoding problem with total power constraint (TPC) and perantenna power constraint (PPC). The optimal precoding design can be obtained by using JOA to set appropriate tradeoff coefficient between sensing and communication performance. The proposed collaborative precoding design algorithm is evaluated by considering sensing and communication performance via numerical results. The complexity of JOA for collaborative precoding under TPC and PPC is also compared and simulated in this paper.