Abstract
Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM) system with Pilot-free phase noise compensator was introduced in order to accomplish the need of high spectral efficiency in the optical communication. In CO-OFDM system Discrete wavelet packet transforms (DWPTs) in place of Fast Fourier Transforms (FFTs) had attracted more attention since it has removed the need of cyclic prefix used to compensate fiber dispersion. In this paper, a DWPT based CO-OFDM system with Wilcoxon Robust Extreme Learning Machine based pilot-free phase noise compensator using multi-level QPSK partitioning of 16-QAM has been proposed. From the results of this work it has been seen that the percentage improvement in performance (in terms of Q-Factor) and spectral efficiency over traditional pilot-aided techniques is approximately 6 and 21 respectively. Moreover, this proposed work will comparatively reduce the overall system complexity.
Highlights
High-speed processing of signals is increasingly becoming a vital factor for several applications such as 5G Internet and cloud computing
Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM) system was introduced in order to achieve high spectral efficiency both in flexible passive optical networks and long-haul optical communication systems [1,2]
Laser phase noise caused due to longer symbol duration has become a critical issue in CO-OFDM systems since it results in a degradation in overall system performance [3]
Summary
High-speed processing of signals is increasingly becoming a vital factor for several applications such as 5G Internet and cloud computing. Laser phase noise caused due to longer symbol duration has become a critical issue in CO-OFDM systems since it results in a degradation in overall system performance [3] Various compensation techniques such as Pilot-aided (PA) and RF-pilot (RFP) was introduced for the mitigation of phase noise in a CO-OFDM system and verified for the compensation capacity [4,5,6,7]. CP- and pilot-free Wilcoxon Robust Extreme Learning Machine (WRELM) based Phase noise compensator has been proposed In this technique, prior knowledge of channel statistics in the form of pilot subcarriers is not needed, since the received training symbols will be used in the compensator network. Low pass filter has been used to avoid aliasing
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