发表机构
Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对AFDM系统在分数时延和多普勒及未知路径数信道下的CSI获取难题，提出一种基于改进SAGE框架的模型阶自适应信道估计方法，实现低NMSE和BER，支持高达600 km/h的移动速度。
AI 中文摘要
仿射频分复用（AFDM）作为一种有前景的波形，因其能够利用多径分集而出现于高移动性通信中。然而，在具有分数时延和多普勒以及未知传播路径数量的信道中，信道状态信息（CSI）的获取仍然具有挑战性。本文研究了AFDM系统的信道估计问题。我们首先分析了在分数时延引起的频率折叠存在下AFDM的响应。分析表明，分数时延可能使主导极值发生位移并产生信息丰富的次级极值。然后，我们在改进的空间交替广义期望最大化（SAGE）框架内开发了一种模型阶自适应信道估计器。采用双导频参考符号进行路径和时延初始化，而跨多个AFDM符号的导频观测为多普勒估计提供时间信息。通过统计控制的残差检验识别新路径，而通过条件支持剪枝去除不支持或冗余的路径。随后通过从粗到细的过程估计每条保留路径的时延、多普勒频率和复增益，并使用精确的AFDM似然进行细化。仿真结果表明，所提出的方法在归一化均方误差（NMSE）和误码率（BER）方面优于代表性的SAGE、稀疏恢复和贝叶斯基准方法。它还提供了可靠的路径检测和模型阶估计，并在高达600 km/h的终端速度下保持稳健性能。
英文摘要
Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for high-mobility communications owing to its ability to exploit multipath diversity. However, channel state information (CSI) acquisition remains challenging in channels with fractional delay and Doppler and an unknown number of propagation paths. In this paper, we investigate the channel estimation for AFDM systems. We first analyze the AFDM response in the presence of fractional-delay-induced frequency wrapping. The analysis reveals that fractional delay may displace the dominant extremum and generate informative secondary extrema. Then, we develop a model-order-adaptive channel estimator within an improved space-alternating generalized expectation-maximization (SAGE) framework. A dual-pilot reference symbol is employed for path and delay initialization, while pilot observations across multiple AFDM symbols provide temporal information for Doppler estimation. New paths are identified through statistically controlled residual tests, whereas unsupported or redundant paths are removed by conditional support pruning. The delay, Doppler frequency, and complex gain of each retained path are subsequently estimated through a coarse-to-fine procedure and refined using the exact AFDM likelihood. Simulation results demonstrate that the proposed method achieves lower normalized mean squared error (NMSE) and bit error rate (BER) than representative SAGE, sparse-recovery, and Bayesian benchmarks. It also provides reliable path detection and model-order estimation and maintains robust performance at terminal velocities of up to 600~km/h.
