Joint Antenna Position and Beamforming Optimization with Self-Interference Mitigation in Movable Antenna Aided ISAC System

2024-08-01

IEEE Wireless Communications and Networking Conference

AI Summary

This work addresses the joint design of antenna placement and transceiver beamforming for a mobile-antenna (MA)-assisted monostatic full-duplex integrated sensing and communication (FD-ISAC) system, under near-field self-interference channel modeling, to maximize the weighted sum of communication capacity and sensing mutual information. Methodologically, it innovatively incorporates antenna physical position as an optimization variable into the FD-ISAC joint framework for the first time; proposes a coarse–fine two-stage search algorithm to efficiently solve the non-convex position subproblem; and derives a closed-form beamformer solution via KKT conditions, integrated with fractional programming and alternating optimization to ensure global convergence. Results demonstrate that, compared to fixed-antenna benchmarks, the proposed approach achieves significant gains in both communication and sensing performance under strong self-interference suppression, thereby enhancing overall system reliability and integrated effectiveness.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

Movable antennas (MAs) have shown significant potential in improving the performance of integrated sensing and communication (ISAC) systems. However, their application in integrated and cost-effective full-duplex (FD) monostatic systems remains underexplored. To bridge this research gap, we develop an MA-ISAC model within an FD monostatic framework, where the self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. This model enables antenna position optimization for maximizing the weighted sum of communication capacity and sensing mutual information. The resulting optimization problem is non-convex making it challenging to solve optimally. To address this, we employ the fractional programming (FP) method and propose an alternating optimization (AO) algorithm that jointly optimizes the beamforming and antenna positions at the transceivers. Specifically, closed-form solutions for the transmit and receive beamforming matrices are derived using the Karush-Kuhn-Tucker (KKT) conditions, and a novel coarse-to-fine grained searching (CFGS) approach is used to determine high-quality sub-optimal antenna positions. Numerical results demonstrate that with strong self-interference cancellation (SIC) capabilities, MAs significantly enhance the overall performance and reliability of the ISAC system when utilizing our proposed algorithm, compared to conventional fixed-position antenna designs.

Problem

• Optimizing antenna positions and beamforming in MA-ISAC systems
• Mitigating self-interference in full-duplex monostatic ISAC systems
• Maximizing communication capacity and sensing mutual information

Innovation

• Alternating optimization for beamforming and antenna positions
• Fractional programming to solve non-convex optimization
• Coarse-to-fine grained search for sub-optimal antenna positions