This paper proposes a novel joint channel-estimation and source-detection algorithm using successive interference cancellation (SIC)-aided generative score-based diffusion models. Prior work in this area focuses on massive MIMO scenarios, which are typically characterized by full-rank channels, and fail in low-rank channel scenarios. The proposed algorithm outperforms existing methods in joint source-channel estimation, especially in low-rank scenarios where the number of users exceeds the number of antennas at the access point (AP). The proposed score-based iterative diffusion process estimates the gradient of the prior distribution on partial channels, and recursively updates the estimated channel parts as well as the source. Extensive simulation results show that the proposed method outperforms the baseline methods in terms of normalized mean squared error (NMSE) and symbol error rate (SER) in both full-rank and low-rank channel scenarios, while having a more dominant effect in the latter, at various signal-to-noise ratios (SNR).

翻译：本文提出了一种新颖的联合信道估计与信源检测算法，该算法采用基于连续干扰消除（SIC）辅助的生成式分数扩散模型。该领域的先前工作主要集中于大规模MIMO场景，此类场景通常以满秩信道为特征，在低秩信道场景中会失效。所提算法在联合信源-信道估计方面优于现有方法，尤其是在用户数量超过接入点（AP）天线数量的低秩场景中。所提出的基于分数的迭代扩散过程估计了部分信道先验分布的梯度，并递归地更新估计的信道部分以及信源。大量的仿真结果表明，所提方法在满秩和低秩信道场景下，在归一化均方误差（NMSE）和符号错误率（SER）方面均优于基线方法，并且在各种信噪比（SNR）下，在低秩场景中具有更显著的优势。