Movable Antenna Aided Full-Duplex ISAC System with Self-Interference Mitigation

2025-05-20

AI Summary

In full-duplex single-station integrated sensing and communication (ISAC) systems, severe self-interference (SI) fundamentally limits both communication throughput and sensing performance. To address this, this paper proposes a mobile-antenna (MA)-assisted joint optimization framework. For the first time, antenna physical positions are explicitly incorporated as optimization variables, enabling accurate near-field SI channel modeling. The framework jointly optimizes transmit/receive beamforming, user power allocation, and transceiver antenna locations. A hybrid algorithm combining fractional programming-based alternating optimization (FP-AO) and particle swarm optimization (PSO) is designed to solve the resulting non-convex problem. Compared with fixed-antenna baselines, the proposed method achieves significantly enhanced near-field SI suppression, thereby simultaneously ensuring high mutual information for sensing and substantially improving uplink/downlink communication rates. Experimental results demonstrate that antenna mobility delivers tangible performance gains, establishing a novel paradigm for full-duplex ISAC system design.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

Movable antenna (MA) has shown significant potential for improving the performance of integrated sensing and communication (ISAC) systems. In this paper, we model an MA-aided ISAC system operating in a communication full-duplex mono-static sensing framework. The self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. We develop an optimization problem to maximize the weighted sum of downlink and uplink communication rates alongside the mutual information relevant to the sensing task. To address this highly non-convex problem, we employ the fractional programming (FP) method and propose an alternating optimization (AO)-based algorithm that jointly optimizes the beamforming, user power allocation, and antenna positions at the transceivers. Given the sensitivity of the AO-based algorithm to the initial antenna positions, a PSO-based algorithm is proposed to explore superior sub-optimal antenna positions within the feasible region. Numerical results indicate that the proposed algorithms enable the MA system to effectively leverage the antenna position flexibility for accurate beamforming in a complex ISAC scenario. This enhances the system's self-interference cancellation (SIC) capabilities and markedly improves its overall performance and reliability compared to conventional fixed-position antenna designs.

Problem

• Optimize MA-aided ISAC system for full-duplex communication and sensing
• Mitigate self-interference via joint beamforming and antenna positioning
• Enhance performance over fixed antennas using PSO-based optimization

Innovation

• FP method optimizes beamforming and power allocation
• AO algorithm jointly optimizes transceiver parameters
• PSO algorithm finds superior antenna positions