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- 1School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing, China
- 2Chongqing Industry Polytechnic College, Chongqing, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.