Beamforming-based Achievable Rate Maximization in ISAC System for Multi-UAV Networking

2025-07-29

AI Summary

To address inaccurate beam coverage prediction and strong resource coupling—leading to rate limitations—in multi-UAV emergency communication networks, this paper proposes a slot-level optimization framework jointly optimizing beamforming, load allocation, and heading planning. Methodologically, it innovatively integrates extended Kalman filtering for dynamic beam alignment and designs a time-varying auxiliary frame structure to enable joint sensing and communication. The problem is formulated as an NP-hard joint optimization, for which we propose an enhanced distributed Successive Convex Approximation with Iterative Resource Mapping (SCA-IRM) algorithm, combining coalition game theory and Fermat-point search to achieve efficient decomposition and parallel solving. Simulation results demonstrate that the proposed approach significantly outperforms baseline schemes in aggregate achievable rate, user fairness, and target sensing accuracy. This work establishes a deployable, distributed cooperative design paradigm for UAV-assisted emergency communications in highly dynamic environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Airborne mobile Integrated Sensing and Communication (ISAC) base stations have garnered significant attention recently, with ISAC technology being a crucial application for 6G networks. Since ISAC can sense potential mobile communication users, this paper studies an effective scheme for a multi-UAV network tailored for emergency communication. In this paper, we develop a temporal-assisted frame structure utilizing integrated omnidirectional and directional beampattern to facilitate efficient and frequent searching, with extended Kalman filtering (EKF) as an aid to beam alignment. Further, we address an optimization problem to maximize the total achievable rate per slot by jointly designing UAV beamforming, load management, and UAV direction planning, all while adhering to the constraints of the predicted beam coverage. Given the problem NP-hard, we introduce three robust mechanisms for its resolution: an enhanced distributed Successive Convex Approximation (SCA)-Iterative Rank Minimization (IRM) algorithm, an coalition game approach, and a Fermat point search method. In particular, the proposed SCA-IRM algorithm decomposes the original complex optimization problem into several sub-problems and assigns them equally to each UAV, so as to realize distributed computing and improve computational efficiency. Our proposed simulations demonstrate the improved system performance in terms of communication rate, fairness, and sensing accuracy, providing design guidelines of UAV-assisted emergency communication networking.

Problem

• Maximize achievable rate in ISAC multi-UAV networks
• Optimize beamforming and UAV direction for emergency communication
• Enhance distributed computing efficiency for NP-hard optimization

Innovation

• Temporal-assisted frame structure with omnidirectional and directional beampattern
• Enhanced distributed SCA-IRM algorithm for optimization
• Coalition game and Fermat point search methods