College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
Department of Electronic and Electrical Engineering, Faculty of Engineering, University College London, London WC1E 7JE, UK
Networking and Transmission Laboratory, Acreo Swedish ICT AB, Stockholm SE-16425, Sweden
School of Information and Communication Technology, Royal Institute of Technology, Stockholm SE-16440, Sweden
: Received: 22 August 2016 / Approved: 23 August 2016 / Online: 23 August 2016 (10:40:19 CEST)
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Xu, T.; Jacobsen, G.; Popov, S.; Li, J.; Liu, T.; Zhang, Y.; Bayvel, P. Analytical Investigations on Carrier Phase Recovery in Dispersion-Unmanaged n-PSK Coherent Optical Communication Systems. Preprints2016, 2016080194 (doi: 10.20944/preprints201608.0194.v1).
Xu, T.; Jacobsen, G.; Popov, S.; Li, J.; Liu, T.; Zhang, Y.; Bayvel, P. Analytical Investigations on Carrier Phase Recovery in Dispersion-Unmanaged n-PSK Coherent Optical Communication Systems. Preprints 2016, 2016080194 (doi: 10.20944/preprints201608.0194.v1).
Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the n-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.