用扩散模型提升毫米波太赫兹超大规模天线的信道估计精度与效率。
DiffPace: Diffusion-based Plug-and-play Augmented Channel Estimation in mmWave and Terahertz Ultra-Massive MIMO Systems
- 基于扩散模型构建信道先验，捕捉近远场混合波形特性。
- 在10 dB信噪比下实现-15 dB NMSE，推理步数减少90%。
- 适合高维信道估计场景，尤其适用于硬件受限的无线系统。
毫米波（mmWave）和太赫兹（THz）通信有望满足下一代无线网络日益增长的数据速率需求，提供丰富的带宽。为缓解高频段严重的路径损耗并降低硬件成本，采用混合波束成形架构的超大规模多输入多输出（UM-MIMO）系统可实现显著的波束成形增益和更高的频谱效率。然而，由于信道维度高且射频链路数量有限导致观测数据压缩，毫米波与太赫兹频段的超大规模MIMO系统中准确信道估计（CE）面临挑战。同时，大阵列孔径和高载波频率引发的混合近场与远场辐射模式进一步增加了估计难度。传统基于压缩感知的框架依赖预定义的稀疏化矩阵，无法准确刻画混合近远场信道结构，导致性能下降。本文提出DiffPace，一种基于扩散模型（DM）的即插即用式信道估计方法。DiffPace利用扩散模型捕获基于混合球面和平面波（HPSM）模型的信道分布，并通过即插即用机制将扩散模型作为先验知识引入，提升估计精度。此外，扩散模型通过求解常微分方程进行推理，相比随机采样方法所需推理步数显著减少。实验结果表明，DiffPace在10 dB信噪比下达到-15 dB的归一化均方误差（NMSE），相比最先进方案推理步数减少90%，兼具高精度与高效计算优势。
原文摘要 · Abstract (English)
Millimeter-wave (mmWave) and Terahertz (THz)-band communications hold great promise in meeting the growing data-rate demands of next-generation wireless networks, offering abundant bandwidth. To mitigate the severe path loss inherent to these high frequencies and reduce hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) systems with hybrid beamforming architectures can deliver substantial beamforming gains and enhanced spectral efficiency. However, accurate channel estimation (CE) in mmWave and THz UM-MIMO systems is challenging due to high channel dimensionality and compressed observations from a limited number of RF chains, while the hybrid near- and far-field radiation patterns, arising from large array apertures and high carrier frequencies, further complicate CE. Conventional compressive sensing based frameworks rely on predefined sparsifying matrices, which cannot faithfully capture the hybrid near-field and far-field channel structures, leading to degraded estimation performance. This paper introduces DiffPace, a diffusion-based plug-and-play method for channel estimation. DiffPace uses a diffusion model (DM) to capture the channel distribution based on the hybrid spherical and planar-wave (HPSM) model. By applying the plug-and-play approach, it leverages the DM as prior knowledge, improving CE accuracy. Moreover, DM performs inference by solving an ordinary differential equation, minimizing the number of required inference steps compared with stochastic sampling method. Experimental results show that DiffPace achieves competitive CE performance, attaining -15 dB normalized mean square error (NMSE) at a signal-to-noise ratio (SNR) of 10 dB, with 90\% fewer inference steps compared to state-of-the-art schemes, simultaneously providing high estimation precision and enhanced computational efficiency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。