Carrier Phase Estimation in Multi-Subcarrier Coherent Optical Systems

Bilal, Syed Muhammad; Fludger, C.R.S.; Bosco, Gabriella

doi:10.1109/lpt.2016.2585500

Cited by 18 publications

(5 citation statements)

References 11 publications

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“…In addition, the performance of standard carrier phase estimation (CPE) is known to be dependent on the characteristic linewidth times symbol-duration product. As a consequence, advanced CPE schemes may be required to avoid MSC implementation penalties at low symbol-rate transmission [10]. The excess B2B penalty coming from MSC implementation at low symbol-rates can be a key limiting factor, reducing the observed SRO or even resulting in degraded performance [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear mitigation on subcarrier-multiplexed PM-16QAM optical systems

Guiomar¹,

Carena²,

Bosco³

et al. 2017

Opt. Express

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We report a comprehensive set of experimental, simulation and analytical results on the benefit of nonlinear mitigation strategies for multi-subcarrier (MSC) PM-16QAM transmission systems. First, we demonstrate ∼9% maximum reach gain enabled by symbol-rate optimization (SRO) of MSC-PM-16QAM in a 31 channels WDM transmission experiment. Then, we demonstrate that, in the considered experimental scenario, the gain provided by digital backpropagation (DBP) over single-carrier (SC) transmission is similar to that achieved by SRO over MSC transmission. Furthermore, we show that the SRO phenomenon can be weakened after self-channel interference (SCI) removal through DBP. As a result, and due to DBP performance limitations in the experiment, the combined effect of SRO and DBP was found to enable only an additional 4% gain in maximum reach. Finally, we address the impact and symbol-rate dependence of nonlinear phase noise (NLPN) in MSC-PM-16QAM transmission, discussing on the NLPN mitigation capability of standard carrier phase estimation (CPE) and on respective gains that could be achieved through its enhanced mitigation.

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Section: Introductionmentioning

confidence: 99%

Nonlinear mitigation on subcarrier-multiplexed PM-16QAM optical systems

Guiomar¹,

Carena²,

Bosco³

et al. 2017

Opt. Express

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“…Before discussing the principle of TCN, we briefly introduce the model of phase drifts, which is the input of TCN. First, the fast phase drifts between two free-running lasers can be modeled as a Wiener process [21,22]. Assuming that the phase drift is zero at the first moment, the measurements of phase drifts (based on pilot pulse) at the following i moment can be expressed as [14,21]…”

Section: The Model Of Phase Driftsmentioning

confidence: 99%

Phase compensation of a continuous-variable quantum key distribution via temporal convolutional neural network

Jiang,

Zuo,

Luo

et al. 2024

J. Phys. A: Math. Theor.

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Although the continuous-variable quantum key distribution (CV-QKD) protocol based on a local local oscillator (LLO) can close all the security loopholes from the transmitted local oscillator (TLO), the phase noise caused by the inaccurate phase reference information limits the performance of the protocol. To reduce the residual phase noise, in this work, we propose a phase estimation and compensation method based on the temporal convolutional neural (TCN) model, where a part of phase information obtained by measuring pilot pulses is employed as the training data and input into the TCN module. With a trained TCN module, the subsequent phase drifts can be more accurately estimated, allowing for better phase compensation and lower phase noise. Numerical analysis shows that the proposed scheme can improve the transmission distance and the secret key rate of the LLO protocol.

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“…Due to the lower baudrate of each of the contributor subcarriers, we are again posed with the challenge that legacy single-carrier systems initially had, even if equipped with low-linewidth lasers. Typically, this problem is tackled by employing joint CPR [15]- [17], which enables to increase the effective baudrate of the CPR subsystem. In Fig.…”

Section: Advanced Cpr Algorithms For Multi-carrier Systemsmentioning

confidence: 99%