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On Models, Bounds, and Estimation Algorithms for Time-Varying Phase Noise

Mohammad Reza Khanzadi (Institutionen för mikroteknologi och nanovetenskap ; Institutionen för signaler och system, Kommunikationssystem ; GigaHertz Centrum) ; Hani Mehrpouyan (Institutionen för signaler och system, Kommunikationssystem) ; Erik Alpman ; Tommy Svensson (Institutionen för signaler och system, Kommunikationssystem) ; Dan Kuylenstierna (Institutionen för mikroteknologi och nanovetenskap, Mikrovågselektronik ; Extern ; GigaHertz Centrum) ; Thomas Eriksson (Institutionen för signaler och system, Kommunikationssystem ; Institutionen för signaler och system, Informationsteori ; GigaHertz Centrum)
5th International Conference on Signal Processing and Communication Systems (ICSPCS2011) p. Art Nr. 6140897. (2011)
[Konferensbidrag, refereegranskat]

In this paper, first, a new discrete-time model of phase noise for digital communication systems, which is a more accurate model compared to the classical Wiener model, is proposed based on a comprehensive continuous-time representation of time-varying phase noise, and statistical characteristics of this model are derived. Next, the non-data-aided (NDA) and decision-directed (DD) maximum-likelihood (ML) estimators of time-varying phase noise, using the proposed discrete-time model are derived. To evaluate the performance of the proposed estimators, the Cramer-Rao lower bound (CRLB) for each estimation approach is derived and by using Monte-Carlo simulations it is shown that the mean-square error (MSE) of the proposed estimators converges to the CRLB at moderate signal-to-noise ratios (SNR). Finally, simulation results show that the proposed estimators outperform existing estimation methods as the variance of the phase noise process increases.

Nyckelord: Time-varying phase noise, Wiener model, non-data-aided (NDA), decision-directed (DD), maximum-likelihood (ML), Cramer-Rao lower bound (CRLB) , phase noise estimation

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Denna post skapades 2011-09-16. Senast ändrad 2016-07-21.
CPL Pubid: 146301


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