A Covariance-Surrogate Framework for Movable-Antenna Enabled Anti-Jamming with Unknown Jammers
Lebin Chen, Ming-Min Zhao, Qingqing Wu, Min-Jian Zhao, and Rui Zhang

TL;DR
This paper introduces a surrogate-based optimization framework for movable-antenna anti-jamming systems with unknown jammers, improving SINR through joint antenna position and beamformer design.
Contribution
It proposes a novel surrogate objective and a low-complexity trust-region algorithm for joint antenna placement and beamforming in unknown jamming scenarios.
Findings
The surrogate objective closely approximates the true interference covariance.
The PTRSO algorithm converges to a stationary point near the anchor.
Numerical results show superior SINR performance over baselines.
Abstract
In this paper, we investigate a movable antenna (MA) enabled anti-jamming optimization problem, where a legitimate uplink system is exposed to multiple jammers with unknown jamming channels. To enhance the anti-jamming capability of the considered system, an MA array is deployed at the receiver, and the antenna positions and the minimum-variance distortionless-response (MVDR) receive beamformer are jointly optimized to maximize the output signal-to-interference-plus-noise ratio (SINR). The main challenge arises from the fact that the interference covariance matrix is unknown and nonlinearly dependent on the antenna positions. To overcome these issues, we propose a surrogate objective by replacing the unknown covariance with the sample covariance evaluated at the current antenna position anchor. Under a two-timescale framework, the surrogate objective is updated once per block (contains…
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Taxonomy
TopicsRadar Systems and Signal Processing · Direction-of-Arrival Estimation Techniques · Indoor and Outdoor Localization Technologies
