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

Key points

• This paper proposes a time-series conditioned diffusion framework for channel estimation that performs denoising in the angular domain.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]SNR-Gated LSTM-Conditioned Diffusion Model for MIMO Channel Estimation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 06:39 PM

  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.

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