Joint Transceiver Beamforming for MU-MIMO ISAC: A DSP-Driven Approach

Abstract

This paper introduces a flexible transceiver beamforming framework for integrated sensing and communications (ISAC) systems. To rigorously quantify the trade-off between sensing and communication (S&C) performance, we propose a unified mean squared error (MSE) evaluation framework building upon practical physical-layer digital signal processing (DSP) algorithms. Unlike traditional information-theoretic bounds, our approach characterizes sensing precision through the statistical behavior of the MUSIC-based angular estimation error and evaluates communication reliability via the minimum mean squared error (MMSE) of the receiver. This formulation enables a joint performance assessment based on actual parameter estimation and channel equalization in real-world. To minimize the weighted S&C MSEs under a total transmit power constraint. Moreover, an efficient iterative algorithm incorporating alternating optimization, successive convex approximation (SCA), and Karush-Kuhn-Tucker (KKT) conditions is developed, enabling the derivation of exact or semi-closed-form solutions. Numerical results demonstrate the effectiveness and advantages of the proposed method.