Title: Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models Show Chinese title

Title: 基于条件先验的非平稳信道估计使用加速扩散模型

Abstract: Wireless channels in motion-rich urban microcell (UMi) settings are non-stationary; mobility and scatterer dynamics shift the distribution over time, degrading classical and deep estimators. This work proposes conditional prior diffusion for channel estimation, which learns a history-conditioned score to denoise noisy channel snapshots. A temporal encoder with cross-time attention compresses a short observation window into a context vector, which captures the channel's instantaneous coherence and steers the denoiser via feature-wise modulation. In inference, an SNR-matched initialization selects the diffusion step whose marginal aligns with the measured input SNR, and the process follows a shortened, geometrically spaced schedule, preserving the signal-to-noise trajectory with far fewer iterations. Temporal self-conditioning with the previous channel estimate and a training-only smoothness penalty further stabilizes evolution without biasing the test-time estimator. Evaluations on a 3GPP benchmark show lower NMSE across all SNRs than LMMSE, GMM, LSTM, and LDAMP baselines, demonstrating stable performance and strong high SNR fidelity. Show Chinese abstract

Abstract: 运动丰富的城市微小区（UMi）环境中的无线信道是非平稳的；移动性和散射体动态随时间改变分布，导致经典和深度估计器性能下降。 本工作提出了用于信道估计的条件先验扩散方法，该方法学习一个与历史相关的得分函数以去除噪声信道快照的噪声。 一个带有跨时间注意力的时序编码器将一个短观察窗口压缩成一个上下文向量，该向量捕捉信道的瞬时相干性，并通过特征级调制引导去噪器。 在推理过程中，一个信噪比匹配的初始化选择与测量输入信噪比对应的扩散步骤，然后按照缩短的、几何间隔的调度进行，以较少的迭代次数保留信噪比轨迹。 利用前一信道估计的时序自条件和仅在训练时使用的平滑惩罚进一步稳定演化过程，而不会对测试时的估计器产生偏差。 在3GPP基准上的评估显示，在所有信噪比下均比LMMSE、GMM、LSTM和LDAMP基线方法具有更低的NMSE，证明了其稳定的性能和强大的高信噪比保真度。