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

Size Peng, Yin Xu, Guanli Yi, Cixiao Zhang, Dazhi He, Wenjun Zhang

TL;DR

This paper addresses joint optimization in a movable-antenna, full-duplex ISAC system operating in a mono-static configuration, modeling the near-field self-interference as a function of MA positions. It introduces a fractional programming-based framework with an alternating optimization algorithm to jointly optimize beamforming, uplink/downlink power, and antenna positions, and complements it with a PSO-based search to overcome sensitivity to initial MA placements. The proposed methods yield non-decreasing, convergent performance with complexities dominated by matrix inversions, and numerical results show significant gains over fixed-antenna designs, especially when employing PSO to explore the movable region. Overall, MA-enabled FD ISAC can achieve markedly improved self-interference cancellation and system reliability across varied power, region size, and user scenarios.

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.

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

TL;DR

This paper addresses joint optimization in a movable-antenna, full-duplex ISAC system operating in a mono-static configuration, modeling the near-field self-interference as a function of MA positions. It introduces a fractional programming-based framework with an alternating optimization algorithm to jointly optimize beamforming, uplink/downlink power, and antenna positions, and complements it with a PSO-based search to overcome sensitivity to initial MA placements. The proposed methods yield non-decreasing, convergent performance with complexities dominated by matrix inversions, and numerical results show significant gains over fixed-antenna designs, especially when employing PSO to explore the movable region. Overall, MA-enabled FD ISAC can achieve markedly improved self-interference cancellation and system reliability across varied power, region size, and user scenarios.

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.

Paper Structure

This paper contains 13 sections, 65 equations, 11 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Signal Model
  5. Problem Formulation
  6. Proposed Solution
  7. Beamforming Optimization
  8. Auxiliary Variables Optimization
  9. Antenna Position Optimization
  10. PSO-based algorithm for updating antenna position
  11. Convergence and Complexity Analysis
  12. Simulation Result
  13. Conclusion

Figures (11)

  • Figure 1: Illustration of the proposed ISAC system aided with MA.
  • Figure 2: Convergence of Algorithm \ref{['AObased_algorithm']} with different number of users.
  • Figure 3: Total antenna moving distance in each iteration of Algorithm \ref{['AObased_algorithm']}.
  • Figure 4: Objective function with different number of particles and iterations in Algorithm \ref{['PSObased_algorithm']}.
  • Figure 5: ISAC performance with different downlink transmit power.
  • ...and 6 more figures