Kalman filter-enabled parameter estimation for simultaneous quantum key distribution and classical communication scheme over a satellite-mediated link

Abstract

An accurate estimation of system parameters is of significance for the practical implementation of the simultaneous quantum key distribution and classical communication (SQCC) over a satellite-mediated link when considering the finite-size effect. In this paper, we propose a Kalman filter (KF)-enabled parameter estimation method for the SQCC over a satellite-mediated link. The fast and slow phase drift can be both estimated by using the improved vector KF carrier phase estimation algorithm, and thus the phase estimation error can be tracked in real time and be almost approximate to the theoretical mean square error limit. Taking advantage of the achieved phase estimation and the dual modulation of the SQCC scheme, the excess noise can be estimated with not only a higher precise but also a lower sacrificing rate of raw keys. Numerical simulations demonstrate the feasibility of the SQCC in both the downlink and uplink in terms of the finite-size effect. As a comparison of the Mth-power algorithm, we find that the secret key rate and achievable zenith angle perform better by using the vector KF algorithm. It paves the way of practical implementations for the SQCC system.

Unscented Kalman Filter with Joint Decision Scheme for Phase Estimation in Probabilistically Shaped QAM Systems

“…As an effective compensation method for linear impairments, the Kalman filter has received interest for applications in coherent optical communication systems [9][10][11]. Because of the fast−tracking capability and high accuracy for parameter estimation, several Kalman−filter−based algorithms have been suggested and implemented such as carrier recovery [12][13][14] and polarization state tracking [15,16]. As an alternative to the BPS algorithm, the CPR algorithm based on the Kalman filter can also function as a blind method for carrier recovery.…”

Machine Learning assisted excess noise suppression for continuous-variable quantum key distribution

“…One is to improve the tolerance of the system, such as the two-way protocol [20] and the phase noise model proposed in [21]. The other is to suppress excess noise at a lower level which is mainly realized by tracking and compensating for phase noise [22,23,24,25,26]. A phase estimation protocol based on the theoretical security and Bayes' theorem is studied in [22], which can achieve a well-motivated confidence interval of the estimated eigenphase without the strong reference pulse propagation.…”

“…A phase estimation protocol based on the theoretical security and Bayes' theorem is studied in [22], which can achieve a well-motivated confidence interval of the estimated eigenphase without the strong reference pulse propagation. In the work [23,24], the fast and slow phase drift can be both estimated by using the improved vector Kalman filter carrier phase estimation algorithm, and thus the phase estimation error can be tracked in real-time and be almost approximate to the theoretical mean square error limit. An implementation of a machine learning framework based on an unscented Kalman filter is explored in [26] for estimation of phase noise, enabling CVQKD systems with low hardware complexity which can work on diverse transmission lines.…”

Adaptive optical phase estimation for real-time sensing of fast-varying signals

“…Optical phase tracking occupies a unique application position because of its use in the measurement of dynamic targets or signals 1 – 6 , including gravitational wave detection and biological measurements 7 , 8 . However, in classical optical measurement, each measurement has an upper precision limit, which is the quantum noise limit determined by quantum mechanics 9 – 16 . For constant phase measurements, the optical measurement accuracy limit is determined based on the number of photons N to be 10 .…”