Diffusion Model-Aided Data Reconstruction in Cell-Free Massive MIMO Downlink: A Computation-Aware Approach

Abstract

Abstract

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.