Neural Network Based Framework for Passive Intermodulation Cancellation in MIMO Systems

• URL: http://arxiv.org/abs/2509.19382v2
• Date: Sat, 01 Nov 2025 08:37:29 GMT
• Title: Neural Network Based Framework for Passive Intermodulation Cancellation in MIMO Systems
• Authors: Xiaolong Li, Zhi-Qin John Xu, Peiting You, Yifei Zhu,
• Abstract summary: 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.<n>Results show that the method effectively suppresses third-order passive intermodulation (PIM) distortion, achieving up to 29dB of average power error (APE) with only 11k trainable parameters.
• Score: 15.303553586865826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA [61.12136997430116]
  Decentralized federated learning (DFL) based on low-rank adaptation (LoRA) enables mobile devices with multi-task datasets to collaboratively fine-tune a large language model (LLM) by exchanging locally updated parameters with a subset of neighboring devices via wireless connections for knowledge integration.<n> directly aggregating parameters fine-tuned on heterogeneous datasets induces three primary issues across the DFL life-cycle: (i) catastrophic knowledge forgetting during fine-tuning process, arising from conflicting update directions caused by data heterogeneity; (ii) textitinefficient communication and convergence during model aggregation process,
  arXiv  Detail & Related papers  (2026-02-24T02:45:32Z)
• PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming [67.78883135636657]
  pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated.<n>PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage.<n>We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading.
  arXiv  Detail & Related papers  (2025-10-27T03:04:46Z)
• Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
  Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
  arXiv  Detail & Related papers  (2025-07-12T17:11:06Z)
• Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based? [89.05848771674773]
  A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed.<n>It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs)<n>The positions of PAs can be reconfigured to both spanning large-scale path and space.
  arXiv  Detail & Related papers  (2025-02-12T18:54:10Z)
• Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks [0.0]
  We propose FedVaccine, a novel AMC model aimed at improving generalizability across signals with varying noise levels. FedVaccine overcomes the limitations of existing FL-based AMC models' linear aggregation by employing a split-learning strategy. These findings highlight FedVaccine's potential to enhance the reliability and performance of AMC systems in practical wireless network environments.
  arXiv  Detail & Related papers  (2024-10-16T17:48:47Z)
• On latent dynamics learning in nonlinear reduced order modeling [0.6249768559720122]
  We present the novel mathematical framework of latent dynamics models (LDMs) for reduced order modeling of parameterized nonlinear time-dependent PDEs.<n>A time-continuous setting is employed to derive error and stability estimates for the LDM approximation of the full order model (FOM) solution.<n>Deep neural networks approximate the discrete LDM components, while providing a bounded approximation error with respect to the FOM.
  arXiv  Detail & Related papers  (2024-08-27T16:35:06Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI [29.11998008894847]
  We propose a data-driven deep learning-based unified hybrid beamforming framework for time division duplex and frequency division duplex systems. For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network.
  arXiv  Detail & Related papers  (2022-01-18T07:21:00Z)
• Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization [64.76619508293966]
  Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a wireless FL framework, which uploads local model parameters and computes global model parameters via wireless communications.
  arXiv  Detail & Related papers  (2021-08-31T08:19:54Z)
• An Ode to an ODE [78.97367880223254]
  We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the group O(d) This nested system of two flows provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem.
  arXiv  Detail & Related papers  (2020-06-19T22:05:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.