Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by this trend, we investigate the application of the advanced diffusion models (DMs), a representative class of generative AI models, to high dimensional wireless channel estimation. By capturing the structure of multiple-input multiple-output (MIMO) wireless channels via a deep generative prior encoded by DMs, we develop a novel posterior inference method for channel reconstruction. We further adapt the proposed method to recover channel information from low-resolution quantized measurements. Additionally, to enhance the over-the-air viability, we integrate the DM with the unsupervised Stein's unbiased risk estimator to enable learning from noisy observations and circumvent the requirements for ground truth channel data that is hardly available in practice. Results reveal that the proposed estimator achieves high-fidelity channel recovery while reducing estimation latency by a factor of 10 compared to state-of-the-art schemes, facilitating real-time implementation. Moreover, our method outperforms existing estimators while reducing the pilot overhead by half, showcasing its scalability to ultra-massive antenna arrays.

翻译：随着生成式人工智能（AI）的蓬勃发展，其在解决无线通信领域传统挑战方面的潜力也逐渐显现。受此趋势启发，本研究探讨了先进的扩散模型——一类代表性的生成式AI模型——在高维无线信道估计中的应用。通过利用扩散模型编码的深度生成先验来捕获多输入多输出无线信道的结构，我们开发了一种新颖的后验推断方法用于信道重建。我们进一步调整所提方法，以从低分辨率量化测量中恢复信道信息。此外，为增强空中传输的可行性，我们将扩散模型与无监督的斯坦因无偏风险估计器相结合，使其能够从含噪观测中学习，并规避了对实践中难以获取的真实信道数据的需求。结果表明，所提出的估计器能够实现高保真度的信道恢复，同时与最先进方案相比，将估计延迟降低了10倍，有利于实时实现。此外，我们的方法在将导频开销减半的同时，性能优于现有估计器，展示了其对超大规模天线阵列的可扩展性。