In this letter, denoising diffusion implicit models (DDIM), a computation-efficient class of probabilistic diffusion models, are proposed for improving the reconstruction performance of end-users in cell-free massive MIMO (mMIMO) downlink. The idea is to leverage the “denoising” characteristic of diffusion models to remove the hardware and channel imperfections, as well as the interference signals, and finally reconstruct the downlink signals. First, it is shown that the data transmission in cell-free mMIMO downlink can be modeled as a forward diffusion process, assuming the aggregated effect of residual impairments and multi-user interference as Gaussian-distributed signals. Then the data reconstruction is carried out via a reverse diffusion process within the DDIM framework. Numerical results in terms of both wireless-specific and learning-specific hyperparameters are provided to highlight the improvement in the reconstruction performance and post-processed SINR. We also trade-off computation complexity against data reconstruction quality by adjusting the hyperparameters of our denoising model without the need for re-training.

中文翻译：

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无单元大规模 MIMO 下行链路中的扩散模型辅助数据重建：一种计算感知方法


在这封信中，提出了去噪扩散隐式模型 （DDIM），这是一类计算高效的概率扩散模型，用于提高最终用户在无单元大规模 MIMO （mMIMO） 下行链路中的重建性能。这个想法是利用扩散模型的 “去噪 ”特性来消除硬件和信道的缺陷，以及干扰信号，最后重建下行链路信号。首先，结果表明，无小区 mMIMO 下行链路中的数据传输可以建模为前向扩散过程，假设残余损伤和多用户干扰的聚合效应为高斯分布信号。然后，在 DDIM 框架内通过反向扩散过程进行数据重建。提供了无线特异性和学习特异性超参数方面的数值结果，以突出重建性能和后处理 SINR 的改进。我们还通过调整降噪模型的超参数来权衡计算复杂性与数据重建质量，而无需重新训练。