WO2021110054A1 - Procédé de décodage de canal de décomposition double de pénalité assisté par réseau neuronal multicouche - Google Patents

Procédé de décodage de canal de décomposition double de pénalité assisté par réseau neuronal multicouche Download PDF

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WO2021110054A1
WO2021110054A1 PCT/CN2020/133414 CN2020133414W WO2021110054A1 WO 2021110054 A1 WO2021110054 A1 WO 2021110054A1 CN 2020133414 W CN2020133414 W CN 2020133414W WO 2021110054 A1 WO2021110054 A1 WO 2021110054A1
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penalty
neural network
polyhedron
dual decomposition
decoding
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PCT/CN2020/133414
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English (en)
Chinese (zh)
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韦逸
赵明敏
赵民建
雷鸣
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浙江大学
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    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/01—Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/03—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words
    • H03M13/05—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
    • H03M13/11—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits using multiple parity bits
    • H03M13/1102—Codes on graphs and decoding on graphs, e.g. low-density parity check [LDPC] codes
    • H03M13/1105—Decoding
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
    • H03M13/03—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words
    • H03M13/05—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits
    • H03M13/11—Error detection or forward error correction by redundancy in data representation, i.e. code words containing more digits than the source words using block codes, i.e. a predetermined number of check bits joined to a predetermined number of information bits using multiple parity bits
    • H03M13/1102—Codes on graphs and decoding on graphs, e.g. low-density parity check [LDPC] codes
    • H03M13/1148—Structural properties of the code parity-check or generator matrix
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0054—Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms

Definitions

  • This application belongs to the field of wireless communication channel coding and decoding, and relates to a penalty-dual decomposition channel decoding method assisted by a multilayer neural network.
  • Channel decoding is a question of how to make a decision on the received symbol message.
  • the message received by the sink is not necessarily the same as the message sent by the source, and the sink needs to know which source message is sent by the source at this time, so the message received by the sink needs to be based on a certain This kind of rule decides to correspond to a certain one of the source symbol message set.
  • the linear programming (LP) decoder is based on the linear relaxation of the original maximum likelihood decoding problem, and is a popular decoding technique for binary linear codes. Because the linear programming decoder has a strong guarantee for the decoding performance in theory, it has received extensive attention from the academic and industrial circles, especially the decoding of low-density parity-check (LDPC) codes. However, compared with the classic Belief Propagation (BP) decoder, the LP decoder has higher computational complexity and lower error correction performance in the low signal-to-noise ratio (SNR) area.
  • SNR signal-to-noise ratio
  • the deep learning method since the deep learning method has been successfully applied to many other fields, such as image processing, natural language processing, etc., it has also begun to be applied to the field of wireless communication as a potential technology, such as signal detection, channel estimation, and channel coding. Wait.
  • the purpose of this application is to improve the decoding performance in the channel decoding process, and propose a penalty dual decomposition channel decoding method assisted by a multilayer neural network.
  • This application first proposes to use the penalty dual decomposition method to solve the maximum likelihood channel decoding problem to further improve the decoding performance.
  • the neural network is introduced into the iterative polyhedron mapping in this method to reduce the number of iterations to reduce decoding delay.
  • a multi-layer neural network-assisted penalty-dual decomposition channel decoding method includes:
  • step 2 2. Introduce the basic polyhedron into the parity check constraint condition in step 1, and transform the maximum likelihood decoding problem into a decoding optimization problem based on the parity check polyhedron;
  • step 4 Use the multi-layer neural network-assisted penalty dual decomposition channel decoder obtained in step 4 to perform online real-time channel decoding.
  • the penalty-dual decomposition channel decoder obtained in step 3 has a check polyhedron mapping, and a check polyhedron mapping operation It is the most time-consuming part. Iterative verification of polyhedral mapping causes decoding delays due to iterations.
  • the polyhedral mapping based on the multilayer neural network proposed in this application reduces the decoding delay by reducing the number of iterations.
  • the multilayer neural network is composed of three layers: an input layer, an output layer and a hidden layer; the input layer contains d j neurons, and the hidden layer contains A neuron, the output layer contains 1 neuron, the hidden layer and the output layer are followed by an activation function, which is defined as:
  • the weights and biases in the network are the network parameters that need to be learned; therefore, the mapping between the input and output realized by the multilayer neural network is expressed as:
  • the loss function is:
  • ⁇ W a ,w b ⁇ is quantified as It is a set of natural numbers; the multiplication operation can be cancelled or transformed into a less complex shift operation.
  • the calculation method of the penalty dual decomposition channel decoder assisted by the multilayer neural network is as follows:
  • sgn( ⁇ ) is a step function. If the number of elements with 1 in
  • ⁇ i: ⁇ i 1 ⁇
  • This application makes full use of the penalty function decomposition method to solve the maximum likelihood channel decoding optimization problem, and improves the decoding performance.
  • machine learning methods are used to further optimize the iterative polyhedral mapping method in the channel decoding method based on penalty function decomposition, and a neural network is introduced to reduce the number of iterations to reduce the decoding delay.
  • the network is very easy to train, and the training process requires less training time and hardware platform.
  • Figure 1 is a CPP-net structure diagram based on [96,48]MacKey 96.33.964 code
  • Figure 2 shows the BP decoder, the decoder based on the alternating direction multiplier method (ADMM L2), the penalty dual decomposition channel decoding method (PDD), and the multi-layer neural network assisted penalty dual decomposition channel decoding method (PDD with neural CPP) BLER curve diagram of code group error rate in Rayleigh channel environment.
  • a multi-layer neural network-assisted penalty-dual decomposition channel decoding method proposed for this system includes the following steps:
  • the method specifically includes the following steps:
  • Step 1 For a binary linear code of length N Each codeword is specified by the M ⁇ N parity check matrix H, Represents the transmitted codeword, and y represents the received signal; the maximum likelihood decoding problem is constructed based on channel decoding, expressed as the form described in the following formula (1):
  • Pr( ⁇ ) represents the conditional probability
  • Step 2 Introduce a basic polyhedron into the parity check constraint condition, and relax the maximum likelihood decoding problem (1) into the following linear constraint problem:
  • P j represents a d j ⁇ N selection matrix, which is used to select the elements in the vector x participating in the j-th check equation, Represents the parity check polyhedron with degree d j, the expression is:
  • Step 3 Use the penalty dual decomposition method to introduce a set of auxiliary variables Convert the constraints in (4) into the following equivalent forms:
  • the block continuous upper bound minimization algorithm BSUM is used to process the inner loop, and the dual variable and the penalty parameter ⁇ m are updated in the outer loop value;
  • the BSUM algorithm processing steps are as follows:
  • ⁇ i is a vector The i-th element of
  • ⁇ [0,1] represents the Euclidean mapping on the interval [0,1]
  • the dual variable is updated by the following formula:
  • the decoding method in this step is called a penalty-dual decomposition channel decoder, and the penalty-dual decomposition channel decoder has a check polyhedron map.
  • Step 4 Design the verification polyhedron mapping calculation based on the multilayer neural network It is divided into the following steps:
  • sgn( ⁇ ) is a step function. If the number of elements with 1 in
  • ⁇ i: ⁇ i 1 ⁇
  • the CPP-net consists of three layers: an input layer, an output layer and a hidden layer; the input layer contains d j neurons, and the hidden layer contains A neuron, the output layer contains 1 neuron, the hidden layer and the output layer are followed by an activation function, which is defined as:
  • the weights and biases in the network are the network parameters that need to be learned; therefore, the mapping between input and output realized by CPP-net is expressed as:
  • check polyhedron mapping based on multi-layer neural network The calculation method described in this step is called check polyhedron mapping based on multi-layer neural network, and its implementation code is:
  • Step 5 Use the penalty dual decomposition channel decoder assisted by the multilayer neural network to perform online real-time channel decoding.
  • Figure 2 shows the BP decoder, the decoder based on the alternating direction multiplier method (ADMM L2), the penalty dual decomposition channel decoding method (PDD), and the penalty dual assisted by the multilayer neural network in the Rayleigh channel environment Decompose the code group error rate BLER of the channel decoding method (PDD with neural CPP).
  • the penalty dual decomposition channel decoding method (PDD) and the multi-layer neural network-assisted penalty dual decomposition channel decoding method (PDD with neural CPP) have obtained the best translation. Code performance.
  • Table 1 compares the average iteration times (iterM) of the iterative polyhedron mapping algorithm and the neural network-based polyhedron mapping algorithm. It can be seen that the neural network-based polyhedron mapping algorithm included in this application can effectively reduce the number of iterations.
  • this application proposes a multi-layer neural network-assisted penalty dual decomposition channel decoding method.
  • the above are only specific implementations for specific applications, but the true spirit and scope of this application are not limited to this. Any person skilled in the art can modify, equivalently replace, improve, etc., to achieve channel decoding for different applications. method. These modifications, equivalent replacements and improvements also fall within the protection scope of the claims of this application.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Probability & Statistics with Applications (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Error Detection And Correction (AREA)

Abstract

La présente invention concerne un procédé de décodage de canal de décomposition double de pénalité assisté par réseau neuronal multicouche, qui est principalement orienté vers des codes linéaires binaires. Ledit procédé comprend les étapes consistant à : (1) construire un problème d'optimisation de vraisemblance maximale sur la base d'un décodage de canal ; (2) transformer le problème d'optimisation de vraisemblance maximale dans le décodage de canal en un problème d'optimisation de décodage basé sur un polyèdre de contrôle de parité par l'introduction d'une contrainte dans le concept d'un polyèdre basique ; (3) introduire un procédé de décomposition double de pénalité pour résoudre le problème d'optimisation de décodage, de façon à obtenir un décodeur de canal de décomposition double de pénalité ; et (4) concevoir un mappage de polyèdre de contrôle basé sur un réseau neuronal multicouche, obtenir des paramètres d'apprentissage au moyen d'un apprentissage, et introduire le mappage de polyèdre de contrôle basé sur un réseau neuronal multicouche dans le décodeur de canal de décomposition double de pénalité, pour obtenir un décodeur de canal de décomposition double assisté par réseau neuronal multicouche. La présente invention améliore les performances de décodage et réduit le retard de décodage au moyen d'un apprentissage automatique.
PCT/CN2020/133414 2019-12-06 2020-12-02 Procédé de décodage de canal de décomposition double de pénalité assisté par réseau neuronal multicouche WO2021110054A1 (fr)

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