通过多项式重构实现太赫兹正交频分复用的连续符号内相位噪声跟踪
Continuous Intra-Symbol Phase Noise Tracking for THz OFDM via Polynomial Reconstruction
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中文总结 AI 辅助
针对6G网络中THz通信受WPN严重影响的问题，提出连续相位轨迹重构方法CPTR，通过最小二乘多项式拟合从导频观测重构相位轨迹，刻画逼近误差与CRB，仿真表明该方法复杂度低，在误码率上优于多种方法。
中文摘要 AI 辅助
用于第六代（6G）网络的太赫兹（THz）通信系统受到维纳相位噪声（WPN）的严重影响，其在亚太赫兹载波上的创新方差远大于毫米波5G系统。传统的公共相位误差（CPE）补偿在每个正交频分复用（OFDM）符号上应用单个相位旋转，当相位轨迹在符号持续时间内变化显著时变得不足。本文提出了连续相位轨迹重构（CPTR），一种闭式符号内相位噪声跟踪方法，通过具有\(\mathcal{O}(N_p + N)\)复杂度的最小二乘多项式拟合从导频观测中重构样本级相位轨迹。我们刻画了WPN下的多项式逼近误差，并推导了多项式相位系数估计的克拉美 - 罗下界（CRB），表明CPTR在多项式替代模型内是最小方差无偏估计。在300GHz、\(N = 1024\)和16 - QAM的仿真表明，CPTR在信噪比为10 - 45dB时保持在CRB的0.2dB以内，同时实现了比基于卡尔曼的跟踪更低的复杂度，并在误码率上比CPE、线性插值和三次样条方法有显著提升。
英文摘要
Terahertz (THz) communication systems for sixth-generation (6G) networks are severely impaired by Wiener phase noise (WPN), whose innovation variance at sub-THz carriers is substantially larger than in millimeter-wave 5G systems. Conventional common-phase-error (CPE) compensation applies a single phase rotation per OFDM symbol and becomes inadequate when the phase trajectory varies significantly within the symbol duration. This letter proposes continuous phase trajectory reconstruction (CPTR), a closed-form intra-symbol phase noise tracking method that reconstructs the sample-level phase trajectory from pilot observations via least-squares polynomial fitting with $\mathcal{O}(N_p+N)$ complexity. We characterize the polynomial approximation error under WPN and derive the Cramér--Rao bound (CRB) for polynomial phase coefficient estimation, showing that CPTR is minimum-variance unbiased within the polynomial surrogate model. Simulations at 300~GHz with \textit{N}~=~1024 and 16-QAM show that CPTR remains within 0.2~dB of the CRB across SNR~=~10--45~dB while achieving significantly lower complexity than Kalman-based tracking and substantial BER gains over CPE, linear interpolation, and cubic spline methods.
