用迭代干扰消除提升低秩信道下的信道与数据联合估计性能
Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarios
- 基于梯度的扩散模型递归更新部分信道和信号源估计
- 在用户数超天线数时，NMSE与SER均优于现有方法
- 特别适合低秩信道场景，适用于大规模接入系统
本文提出一种新型联合信道估计与信号检测算法，采用逐次干扰消除（SIC）辅助的生成式得分扩散模型。现有方法多针对满秩信道的大型MIMO场景，难以应对低秩信道。所提算法通过得分迭代扩散过程估计部分信道先验分布的梯度，递归更新信道与源信号。大量仿真表明，该方法在满秩与低秩信道下均显著优于基线方法，尤其在低秩场景中优势更明显，且在不同信噪比（SNR）条件下表现稳健。
原文摘要 · Abstract (English)
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).
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