Highly-Sensitive Phase and Frequency Noise Measurement Technique Using Bayesian Filtering

Zibar, Darko; Chin, Hou-Man; Tong, Y. L.; Jain, Nitin; Guo, Joel; Chang, Lin; Gehring, Tobias; Bowers, John E.; Andersen, Ulrik L.

doi:10.1109/lpt.2019.2945051

Cited by 9 publications

(11 citation statements)

References 8 publications

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“…2a. In coherent detection systems the additive noise caused by the beating of the LO laser with vacuum fluctuations within the measurement bandwidth limit the efficacy of this method 21 , in addition to electronic noise. In principle, this can be solved by increasing the pilot signal power, however, as previously mentioned, this may be undesirable in a practical CV-QKD system.…”

Section: Machine Learning Aided Phase Tracking Algorithm For Carrier mentioning

confidence: 99%

Machine learning aided carrier recovery in continuous-variable quantum key distribution

et al. 2021

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The secret key rate of a continuous-variable quantum key distribution (CV-QKD) system is limited by excess noise. A key issue typical to all modern CV-QKD systems implemented with a reference or pilot signal and an independent local oscillator is controlling the excess noise generated from the frequency and phase noise accrued by the transmitter and receiver. Therefore accurate phase estimation and compensation, so-called carrier recovery, is a critical subsystem of CV-QKD. Here, we explore the implementation of a machine learning framework based on Bayesian inference, namely an unscented Kalman filter (UKF), for estimation of phase noise and compare it to a standard reference method and a previously demonstrated machine learning method. Experimental results obtained over a 20-km fibre-optic link indicate that the UKF can ensure very low excess noise even at low pilot powers. The measurements exhibited low variance and high stability in excess noise over a wide range of pilot signal to noise ratios. This may enable CV-QKD systems with low hardware implementation complexity which can seamlessly work on diverse transmission lines.

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Section: Machine Learning Aided Phase Tracking Algorithm For Carrier mentioning

confidence: 99%

Machine learning aided carrier recovery in continuous-variable quantum key distribution

et al. 2021

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“…A more practical and thereby implementable approach, as well as equal in performance to Eq. ( 11), is obtained using [17]:…”

Section: A Amplifier Induced Amplitude and Phase Fluctuationsmentioning

confidence: 99%

Approaching optimum phase measurement in the presence of amplifier noise

Zibar

Pedersen

Varming

et al. 2021

Optica

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In fundamental papers from 1962 [1, 2], Heffener and Haus showed that it is not possible to construct a linear noiseless amplifier. The implies that the amplifier intrinsic noise sources induce random perturbations on the phase of the incoming optical signal which translates into spectral broadening. To achieve the minimum (quantum noise limited) induced phase fluctuation, and the corresponding minimum spectral broadening, an optimum phase measurement method is needed. We demonstrate that a measurement method based on the heterodyne detection and the extended Kalman filtering approaches an optimum phase measurement in the presence of amplifier noise. A penalty of 5 dB (numerical) and 15 dB (experimental) compared to the quantum limited spectral broadening is achieved. For comparison, the conventional phase measurement method's penalty exceeds 30 dB for the measurements. Our results reveal new scientific insights by demonstrating that the impact of amplifier noise can be significantly reduced by using the proposed phase measurement method. An impact is envisioned for the phase-based optical sensing system, as optical amplification could increase sensing distance with the minimum impact on the phase.

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“…The input signal power is varied from 32 pW to 2.1 nW (-75 dBm to -59 dBm), corresponding to SNR levels in the range from -11 dB to -7 dB in 1.1 GHz bandwidth. In reference [10], we have shown that quantum noise limited BER performance is achievable using the employed phase noise tracking technique.…”

Section: Optical Phase Tracking At the Quantum Limitmentioning

confidence: 99%

“…Lasers used for quantum communication have low phase noise making the linewidth measurement using RF analyzers challenging. A solution is to make a conversion from FN spectra or phase noise (PN) spectra to linewidth as highly accurate large bandwidth phase noise measurement can be obtained using Bayesian filtering [10].…”

Section: Optical Phase Tracking At the Quantum Limitmentioning

confidence: 99%

“…The field of machine learning (ML) can provide useful tools to address the aforementioned challenges. This is because ML techniques excel at: 1) learning highly-complex input-output mappings which allows for system optimization [4][5][6], 2) learning signaling and detection schemes for complex channels or for channels where analytical models are not available [7,8], and 3) performing ultra-sensitive signal detection [9,10]. In this paper, it will be demonstrated how multi-layer neural networks, which are one of the most popular machine learning technique nowadays, can enable an inverse system design (ISD).…”

Section: Introductionmentioning

confidence: 99%

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