Multiobjective-Optimization-Based Transmit Beamforming for Multitarget and Multiuser MIMO-ISAC Systems

• Published in: IEEE internet of things journal 2024-09, Vol.11 (18), p.29260-29274
• Main Authors: Meng, Chunwei, Wei, Zhiqing, Ma, Dingyou, Ni, Wanli, Su, Liyan, Feng, Zhiyong
• Format: Article
• Language: English
• Subjects: Array signal processing; Beamforming; Communication; Communication networks; Communications systems; Cross correlation; Integrated sensing and communications (ISAC); Measurement; MIMO communication; Multiple objective analysis; multiple-input and multiple-output (MIMO); mutiobjective optimization; mutual information (MI); Optimization; Pareto optimization; Pareto optimum; Radar; Radar targets; Sensors; Signal to noise ratio; transmit beamforming; Upper bound; Upper bounds; Wireless communications
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3413687

Integrated sensing and communication integrated sensing and communications (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this article, we investigate transmit beamforming design for the multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and communication users. A general form of multitarget sensing mutual information (MI) is derived, along with its upper bound, which can be interpreted as the sum of individual single-target sensing MI. Additionally, this upper bound can be achieved by suppressing the cross-correlation among the reflected signals from different targets, which aligns with the principles of adaptive MIMO radar. Then, we propose a multiobjective optimization framework based on the signal-to-interference-plus-noise ratio of each user and the tight upper bound of sensing MI, introducing the Pareto boundary to characterize the achievable communication-sensing performance boundary of the proposed ISAC system. To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between the communication users and radar targets. Subsequently, the bisection search method is employed to find a specific Pareto optimal solution by solving a series of convex feasible problems. Finally, the simulation results validate that the proposed method achieves a better tradeoff between the multiuser communication and multitarget sensing performance. Additionally, utilizing the tight upper bound of sensing MI as a performance metric can enhance the multitarget resolution capability and angle estimation accuracy.