发表机构
Worcester Polytechnic Institute（伍斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SNR门控LSTM条件扩散模型，在角度域去噪，利用时间动态和SNR自适应推理，实现低延迟高精度MIMO信道估计。
AI 中文摘要
准确且低延迟的信道估计对于现代MIMO系统至关重要，尤其是在移动场景下，信道表现出结构化稀疏性和强时间相关性。本文提出了一种基于时间序列条件的扩散框架用于信道估计，在角度域进行去噪。从最小二乘（LS）观测出发，我们训练了一个扩散去噪器，其条件信息由长短期记忆（LSTM）网络在短观测序列上编码，使模型能够利用超出单快照估计的时间动态。为了在宽信噪比（SNR）范围内稳健地平衡观测保真度和学习到的生成先验，我们引入了一个可学习的SNR门控晚期融合捷径，通过具有可训练中心和尺度的sigmoid门将网络输入注入最终解码阶段。为了降低推理延迟，我们采用确定性去噪扩散隐式模型（DDIM）风格的反向更新，并带有SNR自适应截断和步数分配，这在高SNR下显著减少了反向扩散步数，同时在低SNR区域保持强性能。在时变标准化信道模型上的仿真表明，所提方法相比现有基于扩散的信道估计基线取得了持续的性能提升，同时通过SNR自适应推理保持了低延迟。
英文摘要
Accurate and low latency channel estimation is critical for modern MIMO systems, particularly under mobility, where channels exhibit structured sparsity and strong temporal correlation. This paper proposes a time-series conditioned diffusion framework for channel estimation that performs denoising in the angular domain. Starting from least squares (LS) observations, we train a diffusion denoiser whose conditioning information is encoded by a long short-term memory (LSTM) network over a short observation sequence, enabling the model to exploit temporal dynamics beyond per-snapshot estimation. To robustly balance observation fidelity and learned generative priors across a wide signal-to-noise ratio (SNR) range, we introduce a learnable SNR-gated late-fusion shortcut that injects the network input into the final decoding stage through a sigmoid gate with trainable center and scale. To reduce inference latency, we adopt deterministic denoising diffusion implicit model (DDIM) style reverse updates with SNR-adaptive truncation and step allocation, which significantly reduces the number of reverse diffusion steps at high SNR while maintaining strong performance in low SNR regimes. Simulations on time-evolving standardized channel models demonstrate that the proposed method achieves consistent performance gains over existing diffusion-based channel estimation baselines, while retaining low latency through SNR-adaptive inference.
Comments5 pages, 5 figures. Accepted by and presented at the 2026 IEEE 104th Vehicular Technology Conference (VTC2026-Fall)
