Non-Identical Diffusion Models in MIMO-OFDM Channel Generation

• URL: http://arxiv.org/abs/2509.01641v1
• Date: Mon, 01 Sep 2025 17:33:39 GMT
• Title: Non-Identical Diffusion Models in MIMO-OFDM Channel Generation
• Authors: Yuzhi Yang, Omar Alhussein, Mérouane Debbah,
• Abstract summary: We propose a novel diffusion model, termed the non-identical diffusion model, and investigate its application to wireless OFDM channel generation.<n>Non-identical diffusion enables us to characterize the reliability of each element within the noisy input.<n>We show the correctness and effectiveness of the proposed non-identical diffusion scheme both theoretically and numerically.
• Score: 33.33163164222617
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a novel diffusion model, termed the non-identical diffusion model, and investigate its application to wireless orthogonal frequency division multiplexing (OFDM) channel generation. Unlike the standard diffusion model that uses a scalar-valued time index to represent the global noise level, we extend this notion to an element-wise time indicator to capture local error variations more accurately. Non-identical diffusion enables us to characterize the reliability of each element (e.g., subcarriers in OFDM) within the noisy input, leading to improved generation results when the initialization is biased. Specifically, we focus on the recovery of wireless multi-input multi-output (MIMO) OFDM channel matrices, where the initial channel estimates exhibit highly uneven reliability across elements due to the pilot scheme. Conventional time embeddings, which assume uniform noise progression, fail to capture such variability across pilot schemes and noise levels. We introduce a matrix that matches the input size to control element-wise noise progression. Following a similar diffusion procedure to existing methods, we show the correctness and effectiveness of the proposed non-identical diffusion scheme both theoretically and numerically. For MIMO-OFDM channel generation, we propose a dimension-wise time embedding strategy. We also develop and evaluate multiple training and generation methods and compare them through numerical experiments.

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