SNR-Gated LSTM-Conditioned Diffusion Model for MIMO Channel Estimation

Abstract: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.

5 pages, 5 figures. Accepted by and presented at the 2026 IEEE 104th Vehicular Technology Conference (VTC2026-Fall)

Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2610.08977 [cs.LG]

(or arXiv:2610.08977v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.08977 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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