发表机构
University of Victoria（维多利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对亚太赫兹OFDM系统中联合信道与相位噪声估计的高复杂度问题，提出复数域残差FFT卷积注意力网络CRFCAN，通过物理启发的跨域结构实现端到端联合恢复，显著优于现有算法并具备良好泛化性。
AI 中文摘要
在亚太赫兹（sub-THz）通信中，超宽带带宽与严重相位噪声（PN）损伤的耦合使得传统的联合信道与相位噪声估计变得高度复杂且计算上难以承受。为解决这一问题，我们提出了CRFCAN，一种用于联合信道与相位噪声估计的复数域残差FFT卷积注意力网络。与现有的依赖级联网络或结合神经网络与传统迭代估计器的混合框架的深度学习方案不同，CRFCAN通过物理启发的跨域结构以真正端到端的方式执行联合恢复。具体而言，快速傅里叶变换（FFT）和逆FFT模块被嵌入残差组中，以实现在时域和频域之间的迭代特征交互，从而同时捕获频率选择性衰落和时变相位失真。此外，引入了两个专用残差块，分别用于复数特征提取和乘法相位失真建模。还采用了具有软归一化的物理感知PN输出尾部，以提高估计稳定性，同时保留有效PN过程的物理特性。仿真结果表明，CRFCAN在归一化均方误差（NMSE）和误码率（BER）方面显著优于传统算法和最先进的深度学习模型。值得注意的是，CRFCAN以单次、固定复杂度的推理实现了优越性能，并且无需微调即可泛化到未见过的PN模型，突显了其对于亚太赫兹接收机的鲁棒性和实用性。
英文摘要
In sub-terahertz (sub-THz) communications, the coupling of ultra-wide bandwidth and severe phase noise (PN) impairments renders conventional joint channel and PN estimation highly complex and computationally prohibitive. To address this, we propose CRFCAN, a complex-valued residual FFT convolutional attention network designed for joint channel and PN estimation. Unlike existing deep learning schemes that rely on cascaded networks or hybrid frameworks combining neural networks with conventional iterative estimators, CRFCAN performs joint recovery in a truly end-to-end fashion through a physics-inspired cross-domain structure. Specifically, Fast Fourier Transform (FFT) and inverse FFT modules are embedded within residual groups to enable iterative feature interaction across the time and frequency domains, thereby capturing both frequency-selective fading and time-varying phase distortions. In addition, two dedicated residual blocks are introduced for complex feature extraction and multiplicative phase-distortion modeling, respectively. A physics-aware PN output tail with soft normalization is further employed to improve estimation stability while preserving the physical characteristics of the effective PN process. Simulation results demonstrate that CRFCAN significantly outperforms conventional algorithms and state-of-the-art deep learning models in terms of normalized mean square error (NMSE) and bit error rate (BER). Notably, CRFCAN achieves superior performance with single-shot, fixed-complexity inference and generalizes well to unseen PN models without fine-tuning, highlighting its robustness and practicality for sub-THz receivers.
