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基于时间相关性辅助的成形QAM信号盲偏振解复用

Blind Polarization Demultiplexing of Shaped QAM Signals Assisted by Temporal Correlations.

作者信息

Bajaj Vinod, Van de Plas Raf, Wahls Sander

机构信息

Delft Center for Systems and Control, Delft University of Technology, 2628 CD, Delft, The Netherlands.

V. Bajaj, R. Van de Plas, and S. Wahls are with the Delft Center for Systems and Control, Delft University of Technology, 2628 CD, Delft, The Netherlands.

出版信息

J Lightwave Technol. 2024 Jan 15;42(2):560-571. doi: 10.1109/jlt.2023.3315370. Epub 2023 Sep 14.

Abstract

While probabilistic constellation shaping (PCS) enables rate and reach adaption with finer granularity [1], it imposes signal processing challenges at the receiver. Since the distribution of PCS-quadrature amplitude modulation (QAM) signals tends to be Gaussian, conventional blind polarization demultiplexing algorithms are not suitable for them [2]. It is known that independently and identically distributed (iid) Gaussian signals, when mixed, cannot be recovered/separated from their mixture. For PCS-QAM signals, there are algorithms such as [3], [4] which are designed by extending conventional blind algorithms used for uniform QAM signals. In these algorithms, an initialization point is obtained by processing only a part of the mixed signal, which have non-Gaussian statistics. In this paper, we propose an alternative method wherein we add temporal correlations at the transmitter, which are subsequently exploited at the receiver in order to separate the polarizations. We will refer to the proposed method as frequency domain (FD) joint diagonalization (JD) probability aware-multi modulus algorithm (pr-MMA), and it is suited to channels with moderate polarization mode dispersion (PMD) effects. Furthermore, we extend our previously proposed JD-MMA [5] by replacing the standard MMA with a pr-MMA, improving its performance. Both FDJD-pr-MMA and JD-pr-MMA are evaluated for a diverse range of PCS (entropy ) over a first-order PMD channel that is simulated in a proof-of-concept setup. A MMA initialized with a memoryless constant modulus algorithm (CMA) is used as a benchmark. We show that at a differential group delay (DGD) of 10% of symbol period and 18 dB SNR/pol., JD-pr-MMA successfully demultiplexes the PCS signals, while CMA-MMA fails drastically. Furthermore, we demonstrate that the newly proposed FDJD-pr-MMA is robust against moderate PMD effects by evaluating it over a DGD of up to 40% of . Our results show that the proposed FDJD-pr-MMA successfully equalizes PMD channels with a DGD up to 20% of .

摘要

虽然概率星座整形(PCS)能够以更精细的粒度实现速率和传输距离适配[1],但它在接收机处带来了信号处理挑战。由于PCS - 正交幅度调制(QAM)信号的分布趋于高斯分布,传统的盲极化解复用算法不适用于它们[2]。众所周知,独立同分布(iid)的高斯信号混合后无法从其混合信号中恢复/分离。对于PCS - QAM信号,有诸如[3]、[4]中所设计的算法,这些算法是通过扩展用于均匀QAM信号的传统盲算法得到的。在这些算法中,通过仅处理具有非高斯统计特性的混合信号的一部分来获得初始化点。在本文中,我们提出了一种替代方法,即在发射机处添加时间相关性,随后在接收机处利用这些相关性来分离极化。我们将所提出的方法称为频域(FD)联合对角化(JD)概率感知多模算法(pr - MMA),它适用于具有中等偏振模色散(PMD)效应的信道。此外,我们通过用pr - MMA替换标准MMA来扩展我们先前提出的JD - MMA[5],从而提高其性能。在概念验证设置中模拟的一阶PMD信道上,针对各种PCS(熵)对FD - JD - pr - MMA和JD - pr - MMA进行了评估。以无记忆常数模算法(CMA)初始化的MMA用作基准。我们表明,在符号周期的10%的差分群延迟(DGD)和18 dB SNR/偏振的情况下,JD - pr - MMA成功地对PCS信号进行了解复用,而CMA - MMA则严重失败。此外,通过在高达符号周期40%的DGD上对其进行评估,我们证明了新提出的FD - JD - pr - MMA对中等PMD效应具有鲁棒性。我们的结果表明,所提出的FD - JD - pr - MMA成功地均衡了DGD高达符号周期20%的PMD信道。

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