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.

翻译：无源互调已成为现代MIMO-OFDM系统中自干扰的关键来源，尤其是在5G及后续技术严格要求的背景下。传统的消除方法通常依赖于复杂的非线性模型，其可扩展性有限且计算成本高昂。本研究提出了一种轻量级深度学习框架用于无源互调消除，该框架利用深度可分离卷积和扩张卷积，以高效捕获天线与子载波间的非线性依赖关系。为进一步提升收敛性能，我们采用了循环学习率调度与梯度裁剪技术。在受控的MIMO实验环境中，该方法有效抑制了三阶无源互调失真，仅使用11k可训练参数即实现了高达29dB的平均功率误差。这些结果凸显了紧凑型神经架构在未来无线通信系统中实现可扩展干扰抑制的潜力。