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

A new arXiv paper (2610.08977v1) proposes an SNR-gated LSTM-conditioned diffusion framework for MIMO channel estimation that denoises in the angular domain, using an LSTM to encode conditioning information from a short observation sequence. The method introduces a learnable SNR-gated late-fusion shortcut with trainable sigmoid center and scale, plus deterministic DDIM-style reverse updates with SNR-adaptive truncation and step allocation to cut inference latency at high SNR. Simulations on time-evolving standardized channel models show consistent gains over existing diffusion-based channel estimation baselines while retaining low latency.

arXiv:2610.08977v1 Announce Type: new 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.