Modulus mean square-based blind hybrid modulation format recognition for orthogonal frequency division multiplexing-based elastic optical networking

Modulus mean square-based blind hybrid modulation format recognition for orthogonal frequency division multiplexing-based elastic optical networking
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基于模均方的盲混合调制格式识别用于基于正交频分复用的弹性光网络

DOI:
10.1016/j.optcom.2019.04.038
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发表时间:
2019-08
影响因子:
2.4
通讯作者:
Bai Chenglin(白成林)
Bai Chenglin(白成林)
中科院分区:
物理与天体物理3区
文献类型:
--
作者:
Zhao Lei;Xu Hengying;Bai Shunchang;Bai Chenglin(白成林)

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针对基于正交频分复用的弹性光网络(OFDM-EON),提出了一种基于信号模均方(MMS)的简单盲混合调制格式识别(MFR)方案,并进行了实验验证。该方案根据MMS值不同的调制格式的特点,通过设置适当的阈值来识别子载波的调制格式。此外,所提出的盲混合MFR方案具有相位噪声和频偏无关性,并且计算量小,不需要导频。当OFDM-EON系统的子载波的调制格式与(QPSK,8QAM),(QPSK,16QAM),(QPSK,32QAM),(QPSK,64QAM),(8QAM,64QAM),(16QAM,64QAM),(32QAM,64QAM),(QPSK、8QAM、64QAM),(QPSK,16 QAM,64 QAM)和(QPSK、32 QAM、64 QAM),仿真结果表明,要实现对系统子载波调制格式的100%识别,系统需要的光信噪比(OSNR)分别为20 dB、26 dB、23 dB、18 dB、23 dB、27 dB、24 dB、27 dB、26 dB、27 dB。最后,通过一个实验系统验证了该识别算法的有效性.
This paper proposes and experimentally validates a simple and blind hybrid modulation format recognition (MFR) scheme based on the modulus mean square (MMS) of signal for orthogonal frequency division multiplexing-based elastic optical networking (OFDM-EON). According to features of various modulation format with different MMS values, this scheme is designed to recognize the modulation formats of sub-carriers by setting appropriate threshold values. Moreover, the proposed blind hybrid MFR scheme demonstrates independency of phase noise and frequency offset, and has a small amount of computation and does not require pilots. When the sub-carrier’s modulation format of the OFDM-EON system is selected separately from (QPSK, 8QAM), (QPSK, 16QAM), (QPSK, 32QAM), (QPSK, 64QAM), (8QAM, 64QAM), (16QAM, 64QAM), (32QAM, 64QAM), (QPSK, 8QAM, 64QAM), (QPSK, 16QAM, 64QAM) and (QPSK, 32QAM, 64QAM), the simulation results show that in order to achieve 100% recognition of the system sub-carrier’s modulation format, the system needs optical signal to noise ratio (OSNR) are 20dB, 26dB, 23dB, 18dB, 23dB, 27dB, 24dB, 27dB, 26dB, 27dB, respectively. Finally, we also verify the effectiveness of the recognition algorithm by an experimental system.
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