Machine Learning Enabled Detection for QPSK-PD-NOMA System Employing Single Mode Fiber

D. P. Jana, Sumanta Gupta
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引用次数: 1

Abstract

In this paper, we propose a Successive Interference Cancellation (SIC) technique which uses k-means clustering for a standard single mode fiber based non return to zero (NRZ)-quadrature phase shift keying (QPSK) transmission system that employs power division non-orthogonal multiple access (PD-NOMA) scheme to enhance the capacity. Results obtained from the numerical simulation confirm that for an aggregated transmission of 20 Gbps signal for two users, each transmitting a data rate of 10 Gbps over standard single mode fiber (SSMF), a BER value of 10−5 is achievable with an OSNR penalty of 2 dB for a far user, which is situated 100 km away from the transmitter and utilizes NOMA. The proposed system doesn't include any compensation technique for mitigating fiber induced impairments. Simulation results also reveal that, a 1:7 power division ratio is required between the two users for optimum performance.
单模光纤QPSK-PD-NOMA系统的机器学习检测
本文提出了一种基于k-均值聚类的连续干扰消除(SIC)技术,用于标准单模光纤的不归零(NRZ)正交相移键控(QPSK)传输系统,该系统采用功率分非正交多址(PD-NOMA)方案来提高传输容量。数值模拟结果证实,对于两个用户的20 Gbps信号的聚合传输,每个用户在标准单模光纤(SSMF)上传输10 Gbps的数据速率,对于距离发射机100公里并使用NOMA的远端用户,可以实现10−5的误码率,并且OSNR损失为2 dB。提出的系统不包括任何补偿技术来减轻纤维引起的损伤。仿真结果还表明,两个用户之间的功率分配比为1:7才能获得最佳性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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