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A complex-valued neural network approach to passive intermodulation suppression
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DOI：10.1016/j.phycom.2026.103050.png)
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Abstract
En 中文
Artificial neural networks have recently emerged as a promising tool for digital suppression of passive inter-modulation, yet most studies remain restricted to a small number of transceiver chains and do not address realistic multi-channel deployments. This paper proposes a neural PIM suppression framework for multi-carrier and multi-channel transceivers and studies multilayer perceptron (MLP) and Kolmogorov-Arnold network (KAN) architectures within a compact three-layer model. To balance the limited flexibility of standard MLPs with the computational overhead of KANs, we introduce a streamlined KAN design called SKAN by pruning the edge function to keep only the trainable spline basis expansion. We further tailor SKAN to complex-valued baseband signals through an amplitude-phase structure that preserves phase while applying the nonlinear mapping to the amplitude, which improves feature extraction efficiency and reduces inference cost. We validate the proposed approach on a commercial mobile base station equipped with eight transceiver channels under dual carrier operation. Across this setup, SKAN achieves stronger PIM suppression and faster convergence with fewer trainable parameters and fewer floating-point operations than neural baselines, indicating that it is an effective and scalable solution for practical multi-channel PIM mitigation.
Keywords:
PIM suppression
Universal approximation theorem
Kolmogorov-Arnold network
Multi-channel systems
Base station
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