Carrier-phase recovery system and method

Patent metadata

Field	Value
Publication number	US-10873493-B2
Application number	US-201916412104-A
Country	US
Kind code	B2
Filing date	May 14, 2019
Priority date	May 15, 2018
Publication date	Dec 22, 2020
Grant date	Dec 22, 2020

Abstract

A carrier-phase recovery method includes: (i) applying a first carrier-phase recovery algorithm to complex-valued symbols of a signal received by a product detector, yielding coarse phase-estimates, the signal being modulated per an M-QAM scheme; (ii) modelling the coarse phase-estimates as a weighted sum of M probability-density functions of an M-component mixture model; (iii) optimizing the M probability-density functions with an expectation-maximization algorithm to yield M optimized probability-density functions; (iv) mapping, based on the M optimized probability-density functions, the coarse phase-estimates to one of M symbols corresponding to the QAM scheme, each coarse phase-estimate mapped to a same symbol belonging to a same one of M clusters; (v) applying a second carrier-phase recovery algorithm to each of the M clusters to generate refined phase-estimates each corresponding to a respective coarse phase-estimate; and (vi) mapping, based on the M optimized probability-density functions, each refined phase-estimate to one of the M symbols.

First claim

What is claimed is:

1. A carrier-phase recovery method comprising:
applying a first carrier-phase recovery algorithm to a plurality of complex-valued symbols of a signal received by a product detector to yield a plurality of coarse phase-estimates, the signal being modulated per an order-M quadrature-amplitude modulation (QAM) scheme;
modelling the plurality of coarse phase-estimates as a weighted sum of M probability density functions of an M-component mixture model;
optimizing parameters of each of the M probability density functions with an expectation-maximization algorithm to yield M optimized probability density functions;
mapping, based on the M optimized probability density functions, each of the plurality of coarse phase-estimates to one of M constellation symbols corresponding to the QAM scheme, each of the plurality of coarse phase-estimates mapped to a same constellation symbol belonging to a same one of M clusters;
applying a second carrier-phase recovery algorithm to each of the M clusters to generate a plurality of refined phase-estimates each corresponding to a respective one of the plurality of coarse phase-estimates; and
mapping, based on the M optimized probability density functions, each of the plurality of refined phase-estimates to one of the M constellation symbols.

2. The method of claim 1 , each of the plurality of coarse phase-estimates including a coordinate pair in a complex plane of a constellation diagram representing the signal, the method further comprising:
defining a plurality of regions in the complex plane based on intersections of adjacent optimized probability density functions of the M optimized probability density functions, each of the plurality of regions including a respective one of the M constellation symbols; and,
for each of the plurality of coarse phase-estimates, mapping the coarse phase-estimate to the one of the M constellation symbols corresponding to a region of the plurality of regions occupied by the coarse phase-estimate.

3. The method of claim 1 , further comprising, after applying a second carrier-phase recovery algorithm:
repeating steps of modelling, optimizing, mapping each of the plurality of coarse phase-estimates, and
applying the second carrier-phase recovery algorithm, wherein in the step of modelling, the refined phase-estimates generated by the second-carrier-phase recovery algorithm replace the coarse phase-estimates.

4. The method of claim 1 ,
in the step of modeling, the mixture model being a Gaussian mixture model, each of the M probability density functions being a respective Gaussian distribution, the parameters of the mixture model including a mean, a covariance, and a weight of each of the Gaussian distributions; and
optimizing including employing an iterative expectation-maximization algorithm to obtain optimal values for each of the M means, covariances and weights.

5. The method of claim 1 , in the step of applying the first carrier-phase recovery algorithm, the first carrier-phase recovery algorithm using fewer than M symbols.

6. The method of claim 1 , in the step of applying, plurality of complex-valued symbols being less than ten thousand in number.

7. The method of claim 1 , in the steps of applying, the carrier-phase recovery algorithm being a Viterbi-Viterbi Fourth-Power estimator.

8. The method of claim 1 , the quadrature-amplitude modulation scheme being a dual-polarization QAM scheme.

9. A carrier-phase recovery system:
a processor; and
memory adapted to store a plurality of complex-valued symbols of a signal and storing non-transitory computer-readable instructions that, when executed by the processor, control the processor to:
model the plurality of coarse phase-estimates as a weighted sum of M probability density functions of an M-component mixture model;
optimize parameters of each of the M probability density functions with an expectation-maximization algorithm to yield M optimized probability density functions;
map, based on the M optimized probability density functions, each of the plurality of coarse phase-estimates to one of M constellation symbols corresponding to the QAM scheme, each of the plurality of coarse phase-estimates mapped to a same constellation symbol belonging to a same one of M clusters;
apply a second carrier-phase recovery algorithm to each of the M clusters to generate a plurality of refined phase-estimates each corresponding to a respective one of the plurality of coarse phase-estimates; and
map, based on the M optimized probability density functions, each of the plurality of refined phase-estimates to one of the M constellation symbols.

10. The carrier-phase recovery system of claim 9 , each of the plurality of coarse phase-estimates including a coordinate pair in a complex plane of a constellation diagram representing the signal, the memory further storing non-transitory computer-readable instructions that, when executed by the processor, control the processor to:
define a plurality of regions in the complex plane based on intersections of adjacent optimized probability density functions of the M optimized probability density functions, each of the plurality of regions including a respective one of the M constellation symbols; and,
for each of the plurality of coarse phase-estimates, map the coarse phase-estimate to the one of the M constellation symbols corresponding to a region of the plurality of regions occupied by the coarse phase-estimate.

11. The carrier-phase recovery system of claim 9 , the memory further storing non-transitory computer-readable instructions that, when executed by the processor, control the processor to, after applying a second carrier-phase recovery algorithm:
repeat steps of modelling, optimizing, mapping each of the plurality of coarse phase-estimates, and
apply the second carrier-phase recovery algorithm, wherein in the step of modelling, the refined phase-estimates generated by the second-carrier-phase recovery algorithm replace the coarse phase-estimates.

12. The carrier-phase recovery system of claim 9 , the mixture model being a Gaussian mixture model, each of the M probability density functions being a respective Gaussian distribution, the parameters of the mixture model including a mean, a covariance, and a weight of each of the Gaussian distributions, and the memory further storing non-transitory computer-readable instructions that, when executed by the processor, control the processor to, when optimizing parameters:
employ an iterative expectation-maximization algorithm to obtain optimal values for each of the M means, covariances and weights.

13. The carrier-phase recovery system of claim 9 , the first carrier-phase recovery algorithm using fewer than M symbols.

14. The carrier-phase recovery system of claim 9 , plurality of complex-valued symbols being less than ten thousand in number.

15. The carrier-phase recovery system of claim 9 , the carrier-phase recovery algorithm being a Viterbi-Viterbi Fourth-Power estimator.

16. The carrier-phase recovery system of claim 9 , the quadrature-amplitude modulation scheme being a dual-polarization QAM scheme.

Assignees

• Cable Television Laboratories Inc

Inventors

• Xu Mu
• Jia Zhensheng

Classifications

• H04L27/34 Primary

  Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems · CPC title

• H04B14/008

  Polarisation modulation · CPC title

• H04B14/004

  Amplitude modulation · CPC title

• H04L27/361

  Modulation using a single or unspecified number of carriers, e.g. with separate stages of phase and amplitude modulation · CPC title

• H04L1/0054

  Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms · CPC title