Generalized frequency division multiplexing (GFDM), an enabler of beyond-5G wireless networks, can be critically impaired due to radio frequency (RF) phase noise. However, joint channel estimation and phase noise compensation for GFDM systems have not been addressed before. Hence, we tackle this problem. To this end, we propose an iterative algorithm for joint channel and phase noise estimation and two algorithms for joint data detection and phase noise compensation. These algorithms use linear and non-linear least-squares (NLS) methods and employ block-type and comb-type pilots. The complexity of these algorithms is also analyzed. Moreover, to reduce their complexity, interpolation techniques are deployed to decrease the number of unknowns. We also analyze the signal-to-interference-plus noise ratio (SINR) and sum-rate of GFDM contaminated with phase noise. Furthermore, the accuracy of the channel and phase noise estimates is established via Cramér-Rao lower bounds (CRLBs). The simulation results illustrate that the mean-squared error (MSE) performance of the proposed joint channel and phase noise estimator reaches the CRLB. Moreover, the proposed joint data symbol detection and phase noise compensation algorithms nearly eliminate the impacts of phase noise in GFDM systems.

中文翻译：

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GFDM的联合通道和相位噪声估计和数据检测

通用频分复用（GFDM）是5G以上无线网络的促成因素，由于射频（RF）相位噪声会严重受损。但是，之前尚未解决GFDM系统的联合信道估计和相位噪声补偿的问题。因此，我们解决了这个问题。为此，我们提出了一种用于信道和相位噪声联合估计的迭代算法，以及用于联合数据检测和相位噪声补偿的两种算法。这些算法使用线性和非线性最小二乘（NLS）方法，并采用块型和梳型导频。还分析了这些算法的复杂性。此外，为了降低它们的复杂性，部署了插值技术以减少未知数。我们还分析了被相位噪声污染的GFDM的信号干扰加噪声比（SINR）和总和率。此外，信道和相位噪声估计的准确性是通过Cramér-Rao下界（CRLB）建立的。仿真结果表明，所提出的联合信道和相位噪声估计器的均方误差（MSE）性能达到了CRLB。此外，所提出的联合数据符号检测和相位噪声补偿算法几乎消除了GFDM系统中相位噪声的影响。