基于神经网络的延迟-多普勒辅助OFDM信道估计
Neural Network-Based Delay-Doppler-Assisted Channel Estimation for OFDM
- The University of Sydney（悉尼大学）
- Technische Universität Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对高移动性信道下传统OFDM信道估计因载波间干扰失效的问题，提出基于神经网络的延迟-多普勒辅助信道估计框架，仿真显示其归一化均方误差和误码率更优。
AI中文摘要：
传统正交频分复用（OFDM）信道估计依赖单抽头估计和时频（TF）插值，在高移动性信道中会变得不可靠，因为多普勒效应引起的载波间干扰（ICI）会使底层逐元素时频模型失效。本文针对 doubly selective 信道下的OFDM，提出一种基于神经网络的延迟-多普勒（DD）辅助信道估计框架。我们首先推导了感知ICI的时频域输入输出关系，并将信道估计建模为DD域恢复问题。与传统稀疏恢复方法不同，所提框架不要求等效DD域信道向量严格稀疏，从而可容纳分数延迟和多普勒频移引起的泄漏。由于信道估计过程中数据符号未知，感知矩阵仅利用已知导频符号构建，因此未显式建模数据引起的干扰，导致导频观测存在结构化失配。为应对这一挑战，所采用的网络通过感知矩阵迭代交换观测域和信道域特征，学习从这些受污染观测到等效DD域信道的映射，随后用该映射重构时频域信道。仿真结果表明，与传统OFDM估计器相比，所提方法实现了更低的归一化均方误差和误码率。
英文摘要：
Conventional orthogonal frequency division multiplexing (OFDM) channel estimation relies on single-tap estimation and time-frequency (TF) interpolation, which becomes unreliable in high-mobility channels because Doppler-induced inter-carrier interference (ICI) invalidates the underlying element-wise TF model. This paper proposes a neural-network-based delay-Doppler (DD)-assisted channel estimation framework for OFDM over doubly selective channels. We first derive an ICI-aware TF domain input-output relation and formulate channel estimation as a DD recovery problem. Unlike conventional sparse recovery approaches, the proposed framework does not require the equivalent DD domain channel vector to be strictly sparse, thereby accommodating the leakage induced by fractional delay and Doppler shifts. Since the data symbols are unknown during channel estimation, the sensing matrix is constructed using only the known pilot symbols. As a result, data-induced interference is not explicitly modeled, leading to a structured mismatch in the pilot observations. To tackle this challenge, the adopted network iteratively exchanges observation- and channel-domain features through the sensing matrix to learn the mapping from these contaminated observations to the equivalent DD domain channel, which is subsequently used to reconstruct the TF-domain channel. Simulation results show that the proposed method achieves lower normalized mean-square error and bit-error rate than conventional OFDM estimators.
