发表机构
Fudan University; Technische Universitat, Berlin; The Hong Kong University of Science and Technology (Guangzhou)（复旦大学; 柏林工业大学; 香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模MIMO-OFDM系统中高维信道与有限导频带来的估计难题，本文提出环境感知扩散模型，学习位置条件信道先验并结合导频观测进行贝叶斯估计，在多种信噪比和导频配置下优于传统及现有学习方法。
AI 中文摘要
本文提出了一种在大规模多输入多输出正交频分复用（MIMO-OFDM）系统中基于环境感知扩散的信道估计方法。大规模MIMO信道的高维度加上有限的导频资源，使得准确估计具有挑战性。为解决这一问题，我们利用无线信道的空间变异性，训练一个扩散模型来学习信道状态信息的位置条件分布，从而为信道估计提供环境感知的先验。基于该学习到的先验，我们开发了一种后验推断算法，将导频观测纳入反向扩散过程，通过结合接收信号似然与学习到的信道先验实现贝叶斯信道估计。通过联合利用位置信息和测量数据，所提方法在有限导频资源下提高了估计精度。基于射线追踪信道数据集的仿真结果表明，所提方法在各种信噪比和导频配置下始终优于传统估计器和现有基于学习的方法。
英文摘要
This paper proposes an environment-aware diffusion based channel estimation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The high dimensionality of massive MIMO channels combined with limited pilot resources makes accurate estimation challenging. To address this issue, we exploit the spatial variability of wireless channels by training a diffusion model to learn the location-conditioned distribution of channel state information, which provides an environment-aware prior for channel estimation. Based on this learned prior, a posterior inference algorithm is developed to incorporate pilot observations into the reverse diffusion process, enabling Bayesian channel estimation by combining the received-signal likelihood with the learned channel prior. By jointly leveraging location information and measurement data, the proposed approach improves estimation accuracy under limited pilot resources. Simulation results based on ray-tracing channel datasets demonstrate that the proposed method consistently outperforms conventional estimators and existing learning-based approaches across various signal-to-noise ratios and pilot configurations.
