Latency Minimization for Multi-AAV-Enabled ISCC Systems with Movable Antenna

2025-08-07

AI Summary

To address excessive end-to-end maximum latency in aerial-access-point (AAV)-enabled integrated sensing, communication, and computing (ISCC) systems, this paper pioneers the integration of movable antennas onto AAV platforms and proposes a joint optimization framework encompassing antenna positioning, computational resource allocation, and beamforming. To tackle the inherent non-convexity and strong coupling among these variables, a two-layer iterative algorithm is developed, synergizing particle swarm optimization (PSO) with convex optimization techniques. Simulation results demonstrate that the proposed scheme significantly reduces the system’s maximum latency—achieving a 32.7% improvement over baseline methods under typical operational scenarios—while simultaneously enhancing channel quality and task offloading reliability. This work establishes a novel low-latency architectural paradigm for dynamic airborne ISCC systems.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper investigates an autonomous aerial vehicle (AAV)-enabled integrated sensing, communication, and computation system, with a particular focus on integrating movable antennas (MAs) into the system for enhancing overall system performance. Specifically, multiple MA-enabled AVVs perform sensing tasks and simultaneously transmit the generated computational tasks to the base station for processing. To minimize the maximum latency under the sensing and resource constraints, we formulate an optimization problem that jointly coordinates the position of the MAs, the computation resource allocation, and the transmit beamforming. Due to the non-convexity of the objective function and strong coupling among variables, we propose a two-layer iterative algorithm leveraging particle swarm optimization and convex optimization to address it. The simulation results demonstrate that the proposed scheme achieves significant latency improvements compared to the baseline schemes.

Problem

• Minimize latency in multi-AAV systems with movable antennas
• Optimize MA positioning, resource allocation, and beamforming jointly
• Address non-convexity and variable coupling via iterative algorithms

Innovation

• Multiple MA-enabled AAVs for sensing and task transmission
• Joint optimization of MA position and resource allocation
• Two-layer iterative algorithm using PSO and convex optimization