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arXiv 2410.20344eess.SPcs.ITmath.IT
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基于深度学习辅助的可移动天线阵列抗干扰抑制

Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array

Xiao Tang, Yudan Jiang, Jinxin Liu, Qinghe Du, Dusit Niyato, Zhu Han

AI总结:

本文提出在接收端部署可移动天线阵列对抗多干扰机攻击,通过联合优化波束成形与天线定位,构建基于深度学习的框架实现SINR最大化,取得近最优抗干扰性能并显著提升策略确定效率。

摘要:

本文揭示了可移动天线在增强抗干扰通信方面的潜力。我们考虑了在多个干扰机存在情况下的合法通信链路,并提出在接收端部署可移动天线阵列来对抗干扰攻击。通过联合优化接收波束成形和天线单元定位,我们将该问题建模为信号与干扰加噪声比最大化问题。由于从优化角度来看存在非凸性和多重困难,我们开发了一个基于深度学习的框架,其中波束成形作为瑞利商问题求解,而天线定位则通过多层感知机训练来解决。使用随机梯度下降优化神经网络参数以实现有效的抗干扰抑制策略,其特点为离线训练且在线推理的复杂度极低。数值结果表明,所提方法实现了近乎最优的抗干扰性能,从而显著提高了策略确定的效率。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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