用轻量神经网络高效消除多天线系统中的互调干扰
Neural Network Based Framework for Passive Intermodulation Cancellation in MIMO Systems
- 采用深度可分离与空洞卷积捕捉天线和子载波间非线性关系
- 仅11000参数即实现29dB平均功率误差抑制
- 适合5G及以上系统中可扩展的干扰消除场景
被动互调（PIM）已成为现代MIMO-OFDM系统中自干扰的关键来源，尤其在5G及更高级别通信需求下更为突出。传统抑制方法依赖复杂的非线性模型，存在可扩展性差、计算开销高的问题。本文提出一种基于深度学习的轻量级PIM抑制框架，利用深度可分离卷积与空洞卷积高效建模天线与子载波间的非线性依赖关系，并结合循环学习率调度与梯度裁剪以提升收敛性能。在受控的MIMO实验环境中，该方法有效抑制了三阶被动互调失真，平均功率误差（APE）最高达29dB，且仅有11,000个可训练参数。结果表明，紧凑型神经架构在未来的无线通信系统中具备良好的可扩展性与干扰抑制潜力。
原文摘要 · Abstract (English)
Passive intermodulation (PIM) has emerged as a critical source of self-interference in modern MIMO-OFDM systems, especially under the stringent requirements of 5G and beyond. Conventional cancellation methods often rely on complex nonlinear models with limited scalability and high computational cost. In this work, we propose a lightweight deep learning framework for PIM cancellation that leverages depthwise separable convolutions and dilated convolutions to efficiently capture nonlinear dependencies across antennas and subcarriers. To further enhance convergence, we adopt a cyclic learning rate schedule and gradient clipping. In a controlled MIMO experimental setup, the method effectively suppresses third-order passive intermodulation (PIM) distortion, achieving up to 29dB of average power error (APE) with only 11k trainable parameters. These results highlight the potential of compact neural architectures for scalable interference mitigation in future wireless communication systems.
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