Adaptively coding and modulating signals transmitted via nonlinear channels

• US 9,077,508 B2
• Filed: 11/15/2012
• Issued: 07/07/2015
• Est. Priority Date: 11/15/2012
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Koike-Akino, Toshiaki, Parsons, Kieran, Duan, Chunjie, Kojima, Keisuke
• Primary Examiner(s): Vanderpuye, Ken
• Assistant Examiner(s): Alagheband, Abbas H
• Application Number:
  US13/677,490
• Publication Number:
  US 20140133848A1
• Time in Patent Office: 964 Days
• Field of Search: 398/25, 398/162
• US Class Current: 1/1
• CPC Class Codes: H03M 13/033 Theoretical methods to calculate these checking codes
• CPC Class Codes: H03M 13/1102 Codes on graphs and decoding on graphs, e.g. low-density parity check [LDPC] codes
• CPC Class Codes: H03M 13/2957 Turbo codes and decoding
• CPC Class Codes: H03M 13/353 Adaptation to the channel
• CPC Class Codes: H03M 13/6331 Error control coding in combination with equalisation
• CPC Class Codes: H04B 10/0799 Monitoring line transmitter or line receiver equipment
• CPC Class Codes: H04L 1/0003 by switching between different modulation schemes
• CPC Class Codes: H04L 1/0009 by adapting the channel coding H04L1/1812 takes precedence
• CPC Class Codes: H04L 1/0026 Transmission of channel quality indication
• CPC Class Codes: H04L 1/0057 Block codes H04L1/0061, H04L1/0064 take precedence
• CPC Class Codes: H04L 1/0076 Distributed coding, e.g. network coding, involving channel coding coding in both space and time H04L1/0618; cooperative diversity H04B7/022
• CPC Class Codes: H04L 1/1671 the supervisory signal being transmitted together with control information
• CPC Class Codes: H04L 1/20 using signal quality detector

Abstract

An adaptive coding scheme for nonlinear channels improves reliability and an efficiency in digital communication networks. The method monitors channel statistics to analyze an extrinsic information transfer chart of the channels. The channel statistics are fed back to the transmitter to adapt forward error correction coding. A parametric analysis method uses a Gaussian mixture model. The statistical information feedback can adapt an ARQ sheme by adding a weighted received signal to the original coded signals to reduce nonlinear distortion. Trellis shaping can make the transmitting signal preferable for nonlinear channels.

Claims

1. An optical communications network, comprising:

an optical transmitter, further comprising;

a forward error correction (FEC) encoder;

a precoder; and

a modulator;

an optical receiver, further comprising;

a detector;

a FEC decoder;

a decision block; and

an error check block;

an optical fiber connecting the optical transmitter and the optical receiver;

a first analyzer, connected between the optical transmitter and the optical receiver, configured to generate an extrinsic information transfer (EXIT) chart, and adapting the FEC encoder according to the EXIT chart; and

a second analyzer, connected between the optical transmitter and the optical receiver, configured to generate nonlinear statistics of an optical channel in the optical fiber between the optical transmitter and the optical receiver, and adapting, the precoder and the modulator according to the nonlinear statistics.

2. The network of claim 1, wherein the encoder is a forward error correcting encoder.

3. The network of claim 1, further comprising:

measuring a histogram of an output of the detector given feedback information from the FEC decoder to generate the (EXIT) chart.

4. The network of claim 1, further comprising:

generating an automatic-repeat request (ARQ) to initiate retransmission of data with an improved modulation format and adaptive Schalkwijk-Kailath coding.

5. The network of claim 3, wherein the histogram is based on nonparametric and parametric statistical models, and the parametric model is a Gaussian mixture model (GMM) estimated by an expectation-maximization (EM) process.

6. The network of claim 5, wherein the GMM is estimated by locally maximizing likelihoods during the maximization.

7. The network of claim 1, wherein receiver has exponentially decaying nonlinear distortion.

8. The network of claim 1, wherein the precoder is based on trellis shaping, which intentionally adapts the data so that a transmitting signal is preferable for nonlinear channels to reduce distortion.