Method and System for Reliable Data Communications with Adaptive Multi-Dimensional Modulations for Variable-Iteration Decoding

Patent metadata

Field	Value
Publication number	US-2016233979-A1
Application number	US-201514619392-A
Country	US
Kind code	A1
Filing date	Feb 11, 2015
Priority date	Feb 11, 2015
Publication date	Aug 11, 2016
Grant date	—

Abstract

In an advanced adaptive modulation and coding (AMC) scheme, the code rate and the parity-check matrix (PCM) for low-density parity-check (LDPC) codes are adapted according to modulation formats and variable-iteration receivers. The degree distribution for the PCM adaptation is designed by heuristic optimization to minimize the required SNR via an extrinsic information transfer (EXIT) trajectory analysis for finite-iteration decoding. The method uses dynamic window decoding by generating spatially coupled PCM for quasi-cyclic LDPC convolutional coding. The method also provides a way to jointly optimize labeling and decoding complexity for high-order and high-dimensional modulations. The problem to use a large number of different LDPC codes for various modulation formats and variable-iteration decoding is also dealt with by linearly dependent PCM adaptation across iteration count to keep using a common generator matrix. This PCM adaptation can improve a convergence speed of belief propagation decoding and mitigate an error floor issue.

First claim

We claim:

1 . A method for adaptive modulation and coding (AMC) in a communications network, comprising steps:
coding and decoding data using a set of codes having variable rate and variable parity-check matrix (PCM); and modulating and demodulating the data using a set of modulation formats having variable order and variable dimension, wherein the coding and the modulating are performed in a transmitter and the demodulating and the decoding are performed in a receiver.

2 . The method of claim 1 , further comprising a step:
selecting a best combination of one of the codes and one of the modulation formats, depending on a signal-to-noise ratio (SNR) and iteration counts for the demodulating and the decoding, according to a network requirement of power consumption, latency, bit error rate (BER), data rate, and complexity of the coding, modulating, demodulating and decoding.

3 . The method of claim 1 , further comprising steps:
encoding the data using a selected generator matrix to produce encoded data; modulating the encoded data using the selected modulation format to produce modulated data; transmitting the modulated data from the transmitter to the receiver over a channel; receiving output of the channel as noisy data; demodulating the noisy data using a soft-input soft-output maximum a posteriori probability (MAP) algorithm to calculate a log-likelihood ratio (LLR) to produce demodulated data; and decoding the demodulated data using a selected PCM, wherein a number of decoding iterations and a number of demodulating iterations are variable.

4 . The method of claim 1 , wherein the decoding uses a set of low-density parity-check (LDPC) codes having variable rate and variable PCM, wherein multiple PCMs have different degree distributions to achieve a minimum required SNR designed for different modulation parameters, different receiver parameters, and different coding parameters.

5 . The method of claim 1 , wherein the decoding uses a series of multiple PCMs for different iteration counts, wherein the i-th PCM has a different degree distribution designed for the i-th decoding iteration, wherein one or more of the multiple PCMs are linearly dependent and orthogonal to one common generator matrix used at the transmitter.

6 . The method of claim 4 , wherein designing the multiple PCMs comprises steps;
optimizing a degree distribution for variable nodes and check nodes in a bipartite graph for a given combination of the coding parameters, the modulation parameters, and the receiver parameters; optimizing a girth according to the optimized degree distribution; and adapting the optimized PCM to refine for different combinations of the modulation parameters and the receiver parameters.

7 . The method of claim 6 , wherein optimizing the degree distribution comprises steps;
setting up the degree distribution with average and maximum degree constraints; modifying a bit labeling for a high-order and high-dimensional modulation; analyzing mutual information updates across decoding iterations given the degree distribution by using an extrinsic information transfer (EXIT) trajectory analysis; searching for a required SNR achieving the mutual information of one by using a line search algorithm; iterating from the setting to the searching steps using heuristic optimization methods until a maximum iteration of optimizations reaches a pre-defined number; and outputting the best degree distribution and the corresponding required SNR.

8 . The method of claim 6 , wherein the degree distribution is obtained by a protograph base matrix, in which non-zero elements are confined in a band diagonal matrix so that quasi-cyclic (QC) LDPC convolutional code is designed for low-complexity encoding and low-latency decoding with a dynamic window decoding, wherein QC-LDPC codes use a Galois field or a Lie ring.

9 . The method of claim 7 , wherein heuristic optimization methods use a multi-objective function to search for a set of Pareto optimal solutions, wherein the multi-objective function comprises joint minimizations of the required SNR, BER, encoding complexity, decoding complexity, average degree, maximum degree, decoding latency, and circuit size, by using a multi-objective variant of differential evolution, evolutionary strategy, simulated annealing, genetic algorithm, or swarm optimization.

10 . The method of claim 6 , wherein optimizing the girth comprises steps;
generating the PCM having an optimized degree distribution by using a progressive edge growth (PEG) or a greedy linear transform; calculating a corresponding generator matrix by using a Gaussian elimination of the optimized PCM; and outputting the optimized PCM and generator matrix.

11 . The method of claim 7 , wherein the EXIT trajectory analysis comprises steps:
emulating a communications network for a target SNR via Monte-Carlo runs; calculating LLR via the MAP algorithm, wherein the LLR is algebraically inverted by a precoded coset leader; and analyzing mutual information updates across iterations for finite-iteration decoding and demodulating.

12 . The method of claim 11 , wherein the mutual information updates account for a standard deviation loss due to finite-precision computations and finite-length codes, wherein the deviation loss is empirically modeled as a function of input mutual information, the variable node degree, the check node degree, the code length, and precision digits through Monte-Carlo runs.

13 . The method of claim 1 , wherein the PCM is variable depending on the modulation format.

14 . The method of claim 1 , wherein the set of modulation formats have different orders and different dimensions, further comprising;
block-coded high-dimensional modulations, comprising 4-dimensional (4D) simplex modulation, 8D modulation based on an extended Hamming code, 16D modulation based on a Nordstrom-Robinson nonlinear code, and 24D modulation based on an extended Golay code; sphere-cut lattice-packed modulations, comprising 4D checker board lattice, 6D diamond lattice, 12D Coxeter-Todd lattice, 16D Barnes-Wall lattice, and 24D Leech lattice; space-time modulations, comprising Cayley-transformed unitary modulation, Alamouti modulation, and Reed-Muller operator modulation; and quadrature-amplitude modulations (QAM), comprising phase shift keying (PSK), and pulse-amplitude modulation (PAM).

15 . An adaptive modulation and coding (AMC) controller, comprising:
a memory storing
a set of codes having variable rate and variable PCM for encoding and decoding data; and
a set of modulation formats having variable order and variable dimension for modulating and demodulating the data; and
an AMC selector for selecting a best combination of one of the codes and one of the modulation formats for coding and modulating in a transmitter, and demodulation and decoding in a receiver.

Assignees

• Mitsubishi Electric Res Laboratories Inc

Inventors

• Koike-Akino Toshiaki

Classifications

• H03M13/1111

  Soft-decision decoding, e.g. by means of message passing or belief propagation algorithms · CPC title

• H04L1/0045

  Arrangements at the receiver end · CPC title

• H03M13/3927

  Log-Likelihood Ratio [LLR] computation by combination of forward and backward metrics into LLRs · CPC title

• H04L1/007

  Unequal error protection (for format H04L1/0078; for codes per se H03M13/35) · CPC title

• H03M13/116 Primary

  Quasi-cyclic LDPC [QC-LDPC] codes, i.e. the parity-check matrix being composed of permutation or circulant sub-matrices · CPC title