Hybrid Beamforming Optimization for MIMO ISAC Exploiting Prior Information: A PCRB-based Approach

2025-06-09

AI Summary

This work addresses the joint optimization of transmit hybrid beamforming and receive analog beamforming in MIMO integrated sensing and communication (ISAC) systems with hybrid analog-digital arrays at the base station, aiming to minimize the posterior Cramér–Rao bound (PCRB) subject to user communication rate constraints. It is the first to adopt the PCRB as a sensing performance metric to formulate a unified communication-sensing optimization framework. An efficient algorithm is proposed, integrating alternating optimization, weighted minimum mean-square error (WMMSE) techniques, and fractional programming-based successive convex approximation (FPP-SCA). The study reveals an asymmetric impact of RF chain counts at transmitter and receiver: increasing receive RF chains yields significantly greater improvement in sensing accuracy than increasing transmit ones. Simulation results demonstrate that the proposed method substantially reduces the PCRB in both narrowband and OFDM ISAC systems and provides optimal RF resource allocation trade-offs.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

This paper considers a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, where a multi-antenna base station (BS) with transceiver hybrid analog-digital arrays transmits dual-functional signals to communicate with a multi-antenna user and simultaneously sense the unknown and random location information of a target based on the reflected echo signals and the prior distribution information on the target's location. Under transceiver hybrid arrays, we characterize the sensing performance by deriving the posterior Cram'{e}r-Rao bound (PCRB) of the mean-squared error which is a function of the transmit hybrid beamforming and receive analog beamforming. We study joint transmit hybrid beamforming and receive analog beamforming optimization to minimize the PCRB subject to a communication rate requirement. We first consider a sensing-only system and derive the optimal solution to each element in the transmit/receive analog beamforming matrices that minimizes the PCRB in closed form. Then, we develop an alternating optimization (AO) based algorithm. Next, we study a narrowband MIMO ISAC system and devise an efficient AO-based hybrid beamforming algorithm by leveraging weighted minimum mean-squared error and feasible point pursuit successive convex approximation methods. Furthermore, we extend the results for narrowband systems to a MIMO orthogonal frequency-division multiplexing (OFDM) ISAC system. Numerical results validate the effectiveness of our proposed hybrid beamforming designs. It is revealed that the number of receive RF chains has more significant impact on the sensing performance than its transmit counterpart. Under a given budget on the total number of transmit/receive RF chains at the BS, the optimal number of transmit RF chains increases as the communication rate target increases due to the non-trivial PCRB-rate trade-off.

Problem

• Optimize hybrid beamforming for MIMO ISAC using prior information
• Minimize PCRB for sensing under communication rate constraints
• Extend narrowband solutions to MIMO-OFDM ISAC systems

Innovation

• Hybrid beamforming optimization for MIMO ISAC
• PCRB-based approach for sensing performance
• Alternating optimization algorithm for beamforming