发表机构
TUM School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对高速移动无线信道估计问题，采用压缩感知高斯混合模型（CSGMM），显著提升了正交时频空间（OTFS）调制下信道估计的归一化均方误差，确立了该框架作为有前景解决方案的地位。
AI 中文摘要
未来无线通信系统的关键挑战之一是在高速移动场景下确保可靠性，而准确恢复信道状态信息（CSI）是必不可少的。近期多项研究表明，正交时频空间（OTFS）调制是应对这一挑战的有前景技术；此外，基于机器学习（ML）的方法相比传统估计技术，能更有效地利用环境信息，有望提升信道估计性能。本文针对OTFS的信道估计问题，采用基于压缩感知（CS）的稀疏贝叶斯生成模型（SBGM），即最新提出的压缩感知高斯混合模型（CSGMM）。研究表明，所提方法在归一化均方误差（NMSE）上较次优基线有显著提升；同时揭示该模型在多普勒-延迟（DD）域内可任意精度最优近似复杂信道分布的理论潜力。综上，本研究确立OTFS-CSGMM框架作为高速移动无线信道估计的有前景解决方案。
英文摘要
One of the key challenges of future wireless communication systems is ensuring reliability in high-speed mobile scenarios, where accurate recovery of channel state information (CSI) is essential. Many recent studies have concluded that orthogonal time-frequency space (OTFS) modulation is a promising technology for addressing this challenge. Additionally, machine learning (ML)-based methods have the potential to improve channel estimation performance by leveraging ambient information more effectively than classical estimation techniques. This paper particularly addresses channel estimation for OTFS by employing a compressive sensing (CS)-based sparse Bayesian generative model (SBGM), namely the recently introduced compressive sensing Gaussian mixture model (CSGMM). We show that our proposed approach yields significant improvement in normalized mean squared error (NMSE) over the next-best-performing baseline. We additionally provide insights into the theoretical potential of the model to optimally approximate complex channel distributions with arbitrary precision within the Doppler-delay (DD) domain. To summarize, this work establishes the OTFS-CSGMM framework as a promising solution for high mobility wireless channel estimation.
Comments5 pages, 2 figures, conference
