Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array

TL;DR

This paper introduces a deep learning framework utilizing movable antennas to enhance anti-jamming communication, jointly optimizing beamforming and antenna positioning for improved signal resilience.

Contribution

It presents a novel deep learning-based approach for jointly optimizing antenna placement and beamforming to mitigate jamming, leveraging movable antennas and offline training.

Findings

Achieves near-optimal anti-jamming performance

Significantly improves strategy determination efficiency

Demonstrates effectiveness through numerical results

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.