Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array

2024-10-27

arXiv.org

AI Summary

This work addresses the robustness of legitimate communication links against multiple interferers by proposing a cooperative optimization framework for reconfigurable antenna arrays. Unlike conventional fixed arrays, it pioneers the use of physical antenna displacement as an additional design degree of freedom, jointly optimizing receive beamforming weights and array element spatial positions to maximize the signal-to-interference-plus-noise ratio (SINR). To reduce computational complexity and enable real-time deployment, an end-to-end deep learning architecture is developed: a beamforming subnetwork solves the Rayleigh quotient analytically, while a position control subnetwork employs a multilayer perceptron (MLP) for regression-based spatial configuration. Experiments demonstrate that the method achieves near-optimal interference mitigation performance with significantly reduced policy generation complexity, enabling low-overhead online inference. Consequently, interference suppression efficiency and communication reliability are substantially improved.

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Abstract

This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly optimizing the receive beamforming and antenna element positioning. Due to the non-convexity and multi-fold difficulties from an optimization perspective, we develop a deep learning-based framework where beamforming is tackled as a Rayleigh quotient problem, while antenna positioning is addressed through multi-layer perceptron training. The neural network parameters are optimized using stochastic gradient descent to achieve effective jamming mitigation strategy, featuring offline training with marginal complexity for online inference. Numerical results demonstrate that the proposed approach achieves near-optimal anti-jamming performance thereby significantly improving the efficiency in strategy determination.

Problem

• Enhance anti-jamming communication using movable antenna arrays
• Maximize signal-to-interference-plus-noise ratio via joint optimization
• Develop deep learning framework for jamming mitigation strategy

Innovation

• Deep learning optimizes beamforming and positioning
• Movable antenna array combats jamming attacks
• Offline training reduces online inference complexity