Multi-Cell Coordinated Beamforming for Integrate Communication and Multi-TMT Localization

TL;DR

This paper presents a novel coordinated beamforming algorithm for multi-cell systems that improves target localization accuracy in integrated sensing and communication systems while maintaining communication quality, using a new CRLB derivation and SDP solution.

Contribution

It introduces a closed-form CRLB expression for TOA-based localization and a semi-definite programming approach for joint localization and communication optimization.

Findings

The proposed algorithm achieves rank-one global optimal solutions.

It outperforms benchmark algorithms in localization accuracy.

There is a trade-off between localization precision and communication SINR.

Abstract

This paper investigates integrated localization and communication in a multi-cell system and proposes a coordinated beamforming algorithm to enhance target localization accuracy while preserving communication performance. Within this integrated sensing and communication (ISAC) system, the Cramer-Rao lower bound (CRLB) is adopted to quantify the accuracy of target localization, with its closed-form expression derived for the first time. It is shown that the nuisance parameters can be disregarded without impacting the CRLB of time of arrival (TOA)-based target localization. Capitalizing on the derived CRLB, we formulate a nonconvex coordinated beamforming problem to minimize the CRLB while satisfying signal-to-interference-plus-noise ratio (SINR) constraints in communication. To facilitate the development of solution, we reformulate the original problem into a more tractable form and solve it through semi-definite programming (SDP). Notably, we show that the proposed algorithm can always obtain rank-one global optimal solutions under mild conditions. Finally, numerical results demonstrate the superiority of the proposed algorithm over benchmark algorithms and reveal the performance trade-off between localization accuracy and communication SINR.