MI-based beamforming optimization framework for integrated sensing and communication

This article proposes a novel mutual information (MI)-based beamforming framework for integrated sensing and communication (ISAC) systems in the Internet of Vehicles (IoV). The framework addresses the challenges posed by diverse optimization criteria and the suboptimal performance degradation often resulting from normalization methods. We first analyze a time-division multiplexing (TDM) signal model that facilitates both target detection and communication. Subsequently, we introduce a general signal model with integrated beamforming, where communication users simultaneously function as sensing targets. For each model, we formulate an optimization problem to maximize the system MI under a total power constraint. For the TDM model, we propose a Joint Optimization Dual Gradient Ascent algorithm. This method involves constructing an augmented Lagrangian function, computing the gradients for sensing and communication MI separately, and iteratively updating the beamforming vectors using gradient ascent. For the more complex general model, which presents an NP-hard problem, we tackle the non-convex objective function via the Minorization–Maximization (MM) algorithm, obtaining a solution through numerical optimization. Numerical results demonstrate that the proposed framework effectively evaluates the system’s sensing-communication performance trade-off and outperforms classical water-filling algorithms. This work thus provides a new and effective paradigm for ISAC system optimization.