We carry out a comprehensive analysis to examine the performance of our recently proposed correlation-based and pilot-tone-assisted frequency offset compensation method in coherent optical OFDM system. The frequency offset is divided into two parts: fraction part and integer part relative to the channel spacing. Our frequency offset scheme includes the correlation-based Schmidl algorithm for fraction part estimation as well as pilot-tone-assisted method for integer part estimation. In this paper, we analytically derive the error variance of fraction part estimation methods in the presence of laser phase noise using different correlation-based algorithms: Schmidl, Cox and Cyclic Prefix based. This analytical expression is given for the first time in the literature. Furthermore, we give a full derivation for the pilot-tone-assisted integer part estimation method using the OFDM model.
© 2013 OSA
Orthogonal frequency-division multiplexing (OFDM) has attracted much research interest due to its dispersion tolerance, ease of frequency domain equalization and high spectral efficiency. To the contrary, OFDM is more sensitive to frequency offset (FO), due to its longer symbol duration, which is N (number of subcarriers) times that of a single carrier system. This calls for accuracy in frequency recovery hundreds or thousands of times greater than that in a single carrier system with the same bit rate . The presence of FO would cause loss of orthogonality between subcarriers and thus degrade the system performance. Frequency offset compensation (FOC), therefore, is one of the most critical functions to implement in OFDM systems.
Various methods have been employed for FO estimation and compensation in both wireless [2–4] and optical domain [5–8]. The frequency offset can be divided into a fraction and an integer part of the carrier spacing (f0) and estimated separately. In , we proposed to use a correlation-based method for estimating the fraction part and a pilot-tone-assisted method for the integer part. The fraction part estimation methods are either based on repeated training symbols including Schmidl  (which is the method employed in ) and Moose , or cyclic prefic (CP) . Here, we will analytically derive the estimation variance in the presence of laser phase noise for these methods. To our knowledge, there is no other work on this topic in the literature. The analytical expressions are confirmed through simulation. CP method is found to be the most robust to laser phase noise among the three. Furthermore, we investigate the performance of correlation-based methods under different amount of chromatic dispersion. Schmidl and Moose estimators are robust to dispersion while the CP estimator degrades severely in the presence of dispersion. The pilot-tone assisted integer part estimation method is given in  without solid proof. In this paper, we will give a full derivation of the method based the OFDM model. Its performance will be further investigated through both analysis and simulation.
The reminder of the paper is organized as follows. In Section 2, a model incorporating frequency offset, laser phase noise, channel distortion and additive white Gaussian noise is derived for CO-OFDM systems. Subsequently, correlation-based fraction part estimation methods are investigated in the presence of chromatic dispersion and laser phase noise in section 3, where analytical expressions and simulation results of error variances are given. In section 4, the pilot-tone assisted integer part estimation method is presented with a more detailed derivation from the system model. In Section 5 we draw the conclusions.
2. System Model
The discrete time domain samples of an OFDM signal are obtained by taking DFT transform from Xki, the frequency-domain complex modulation symbol associated to the k-th subcarrier and i-th OFDM symbol:
3. Correlation-based fraction part estimation
In this section, we investigate the performance of correlation-based fraction part estimators in the presence of chromatic dispersion and laser phase noise. Three methods (Schmidl , Moose  and CP ) are included, and the derivation can be easily extended to other estimators. For fair comparison, we modify the Schmidl  method to include two identical training symbols instead of one. Ignoring phase noise and additive noise, there is 2πε phase shift between the first and second training symbol, both in time and frequency domain. Thus, the maximum likelihood estimation is obtained from cross-correlation between two received symbols:2] and Moose  estimators, respectively. Taking the effect of CP into consideration, the actual frequency offset is Δf = εf0N/(N + CP). Similarly, the CP estimator  takes cross-correlation between the cyclic prefix and the data from which cyclic prefix is generated. A better estimation can be achieved by taking average over D consecutive symbols:
We rewrite the relationship between the two training symbols of Schmidl estimator as follows:Eq. (2). Following a similar approximation in , we can derive the estimation variance for Schmidl estimator from the tangent of the phase error:
For , Eq. (7) can be approximated as:
With high signal-to-noise ratio, Eq. (8) may be further approximated by:Eq. (8-10) but comes with a different variance for phase difference :
Assuming the performance of CP and Schmidl (or Moose) estimators are identical for zero dispersion and zero phase noise case (D = N/CP), we expect CP to be performing better than Schmidl (or Moose) under nonzero laser phase noise case. This is because CP estimator has a smaller variance of phase noise difference between the two signals taken for cross-correlation.
To verify the derived variance expressions, we built a CO-OFDM system using MATLAB. The transmitter and receiver block diagram of our CO-OFDM is shown in Fig. 1. The system employs QPSK modulation with a DFT/IDFT size of 256 and a cyclic prefix of 32 samples. The signal is sampled at 10 Gsample/s. To match the performance of CP with Schmidl/Moose for the zero phase noise case, we set D = N/CP = 8. Figure 2 and 3 shows the simulation result in a back-to-back transmission. Figure 2 shows the estimation accuracy in terms of variance (Var[ε]) versus signal to noise ratio (SNR, Es/N0) with different laser linewidths. All the simulation results (black symbol) match perfectly with the analytical curves (red line). At higher laser linewidth, say 100 kHz, the variance curves are no longer sensitive to SNR for all three methods, as σ2>>N0/(NEs). CP estimator is more tolerant to laser phase noise than Schmidl (or Moose) estimator, e.g., it has nearly 10 times smaller variance than the other two methods at 100 kHz. Figure 3(a) compares the laser linewidth tolerance of the three estimators at 15-dB SNR. In addition to the fact that no training symbol is required for CP estimator, it performs the best in the presence of laser phase noise. We can easily prove that for any values of N, CP and D as long as we hold D = N/CP and CP≤N. Figure 3(b) depicts the variance versus relative FO ε for different methods and different laser linewidths . All the methods have the same estimation range and CP method has the most accurate estimation at nonzero laser linewidth.
In Fig. 4, we incorporate linear channel distortion (chromatic dispersion) with different dispersion values (0, 1700, 17000 ps/nm) into the system. As predicted, Schmidl (or Moose) estimator performs almost the same under different amount of dispersion. The small deviation from the ideal curve is due to the small nonzero components hl for l≥CP generated by fiber chromatic dispersion. However, the degradation is almost negligible for as large as 17000-ps/nm dispersion (1000 km of standard single mode fiber with 17 ps/nm/km dispersion parameter). To the contrary, the accuracy of the CP estimator is severely degraded by dispersion as small as 1700 ps/nm, especially for smaller laser phase noise case. The conclusions are expected to be the same for polarization mode dispersion (PMD). We can transmit identical training symbols in different polarizations and thus PMD will affect the received signals in a similar way as CD in the single polarization case.
4. Pilot-tone-assisted integer part estimation
In this section, we will fully derive the pilot-tone-assisted integer part estimation method from its OFDM model in frequency domain (Eq. (3). A pilot-tone with larger energy at DC is inserted at the center of the spectrum. Assuming zero laser phase noise () and that εf has been compensated for, can be calculated as:
With (m + εi) being an integer, we can conclude that when m + εi = 0 and otherwise. The resulted received symbol would be:
From Eq. (14), we observe that εi will shift the pilot-tone (peak in the received spectrum) εi positions away. We can thus calculate εi by:Eq. (17), we can conclude that the error probability is only dependent on the pilot to average signal power ratio (Ep/Es, Ep = |Xp|2, Es = E[|Xs|2]), SNR (Es/N0) and DFT size (N). In Fig. 5 (a) we plot Pc versus pilot to average signal power ratio at different SNR in a back to back transmission with QPSK format. As predicted, the probability curve depends on SNR value and DFT size, but it is unaffected by f0, εi or dispersion. For constant modulus format, we can further reduce Eq. (17) to:
As indicated by Eq. (18), a smaller SNR or larger DFT size requires higher pilot to average signal power ratio to achieve error free detection, which is verified by simulation. In Fig. 5(b), Pc is ploted versus Ep/Es for different laser linewidths, where degradation is hardly noticeable under 100 kHz. Larger laser linewidth (500 kHz, 1 MHz) affects the curves to a small extend but different curves still converge to 0 at almost the same speed. Laser phase noise affects the received signal through ICI, which will corrupt the peak in a similar way as AWGN noise.
In this paper, we carried out a comprehensive analysis to examine the performance of our recently proposed correlation-based and pilot-tone-assisted frequency offset compensation method in coherent optical OFDM system. We have analytically derived the fraction part estimation accuracy in the presence of laser phase noise for various correlation-based methods. Furthermore, we re-propose our pilot-tone-assisted integer part estimation method with a full derivation based on the OFDM model. Its estimation accuracy is proved to be independent of f0 and dispersion, dependent of DFT size, pilot to average signal power ratio, SNR and laser phase noise. In the future we will further investigate the performance in the presence of nonlinear phase noise.
The authors would like to thank the supports of AcRF Tier 1 Grant R-263-000-631-112 and NUSRI Grant R-2012-N-009.
References and links
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