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ORIGINAL RESEARCH article

Front. Signal Process.

Sec. Signal Processing for Communications

Volume 5 - 2025 | doi: 10.3389/frsip.2025.1700979

This article is part of the Research TopicEmerging Optimization, Learning and Signal Processing for Next Generation Wireless Communications and NetworkingView all 5 articles

MI Based Beamforming Optimization Framework for Integrated Sensing and Communication

Provisionally accepted
Zhiwei  PuZhiwei Pu1*Fan  ChenFan Chen2
  • 1School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing, China
  • 2Chongqing Industry Polytechnic College, Chongqing, China

The final, formatted version of the article will be published soon.

This paper 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.

Keywords: Integrated of Sensing and Communication(ISAC), Mutual Information(MI), beamforming, Adaptive weight, optimization

Received: 08 Sep 2025; Accepted: 07 Oct 2025.

Copyright: © 2025 Pu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Zhiwei Pu, puzhiwei@cqjtu.edu.cn

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