Low-Complexity Cramér-Rao Lower Bound and Sum Rate Optimization in ISAC Systems

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
• Main Authors: Fang, Tianyu, Nguyen, Nhan Thanh, Juntti, Markku
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.04.2025
• Subjects: Approximation algorithms; Array signal processing; Cramér-Rao lower bound; Integrated sensing and communication; Integrated sensing and communications; Linear programming; Lower bound; Measurement; Optimization; Signal processing algorithms; Simulation; Speech processing; successive convex approximation
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP49660.2025.10888351

While Cramér-Rao lower bound is an important metric in sensing functions in integrated sensing and communications (ISAC) designs, its optimization usually involves a computationally expensive solution such as semidefinite relaxation. In this paper, we aim to develop a low-complexity yet efficient algorithm for CRLB optimization. We focus on a beamforming design that maximizes the weighted sum between the communications sum rate and the sensing CRLB, subject to a transmit power constraint. Given the non-convexity of this problem, we propose a novel method that combines successive convex approximation (SCA) with a shifted generalized power iteration (SGPI) approach, termed SCA-SGPI. The SCA technique is utilized to approximate the non-convex objective function with convex surrogates, while the SGPI efficiently solves the resulting quadratic subproblems. Simulation results demonstrate that the proposed SCA-SGPI algorithm not only achieves superior tradeoff performance compared to existing method but also significantly reduces computational time, making it a promising solution for practical ISAC applications.