CN109302269A - A kind of acquisition methods and system of the decoding of bit error rate near-optimization - Google Patents

A kind of acquisition methods and system of the decoding of bit error rate near-optimization Download PDF

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CN109302269A
CN109302269A CN201811328693.1A CN201811328693A CN109302269A CN 109302269 A CN109302269 A CN 109302269A CN 201811328693 A CN201811328693 A CN 201811328693A CN 109302269 A CN109302269 A CN 109302269A
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node
relay
decoding
source
grid structure
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黄冠龙
陆凌
钱彬
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Shenzhen University
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Shenzhen University
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    • 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
    • H04L1/0056—Systems characterized by the type of code used
    • H04L1/0059—Convolutional codes
    • H04L1/006—Trellis-coded modulation
    • 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
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0036—Systems modifying transmission characteristics according to link quality, e.g. power backoff arrangements specific to the receiver
    • 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
    • H04L1/0076—Distributed coding, e.g. network coding, involving channel coding

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

The present invention is suitable for the communication technology, provide a kind of acquisition methods of bit error rate near-optimization decoding, it include: to establish based on decoding retransmission protocol, the junction network model of single source unit/terminal, including single source node, several relay nodes and single target node, each relay node is by single input link and multi output link, and destination node is unique node with multi input link, product network is established in destination node, product network indicates the scene of all possible relay node decoding error, and the correlation between the error bit introduced to relaying node decoder carries out accurate modeling；Branch metric is determined according to product network；The source information that source node transmits is carried out the decoding of bit error rate near-optimization is calculated according to product network and bcjr algorithm.The acquisition methods of the bit error rate near-optimization decoding provided through the embodiment of the present invention can realize performance more preferably than existing decoding algorithm in different trunk channel scenes.

Description

Method and system for obtaining approximately optimal decoding of bit error rate
Technical Field
The invention belongs to the field of communication, and particularly relates to a method and a system for obtaining approximately optimal decoding of bit error rate based on a decoding and forwarding protocol.
Background
As an outstanding transmission strategy, relay-assisted communication has become a research focus, and the basic idea is to deploy one or more relay nodes to extend the coverage of signals sent by source nodes. Among the many known relay protocols, the most widely studied are the Amplify-and-Forward (AF) and Decode-and-Forward (DF) protocols. DF is of significant research interest as one of the most classical and practical relaying protocols to eliminate noise and other channel impairments. In this protocol, the relay node fully decodes, re-encodes and re-transmits the source node's information. However, the implementation of the conventional DF protocol faces two problems.
For one, the traditional maximum ratio combining scheme applied to the target node assumes that the transmission from the source node to the relay node is perfect, while in reality the link from the source node to the relay node is imperfect, which means that the relay node sometimes cannot decode successfully. Secondly, another difficulty in implementing the DF protocol is that it is difficult (impossible) to implement BER optimal decoding algorithm at the target node. First, to achieve optimal decoding, accurate error statistics must be obtained for the link from the source node to the relay node, which is difficult to achieve in practical systems. Secondly, like the Maximum Likelihood Decoding (MLD) algorithm, the BER optimal decoding algorithm at the target node needs to consider all possible symbol detection scenarios not only at the target node but also at the relay node. Furthermore, the complexity of known BER/BLER optimal decoding algorithms is related to the length index of the information block, and the implementation of these algorithms is very complex even for some uncoded systems.
In the more common complex relay network based on single source and single terminal of DF protocol, each relay node has single input link and multiple output links, and the target node is the only node with multiple input links. Further, each relay node physically having multiple input links may be logically represented as multiple relay nodes having a single input link. Although a relay node in an actual network may have multiple incoming links, our network diagram is actually a graphical representation of how packets sent by a source node are received and re-generated by a logical relay node for retransmission to a destination node. During this time, an actual relay node with multiple input links may be represented as multiple logical relay nodes with one input link.
Therefore, in an imperfect source node to relay node link, the existing near-optimal decoding algorithm has a low capability of estimating the probability of different relay decoding scenarios.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method and a system for obtaining the bit error rate approximate optimal decoding based on a decoding and forwarding protocol, and aims to solve the problem that the existing approximate optimal decoding algorithm has low capability of estimating the probability of different relay decoding scenes.
The invention is realized in this way, a method for obtaining the decoding with the approximate optimal bit error rate comprises the following steps:
step A, establishing a relay network model based on a decoding forwarding protocol and a single source single terminal, wherein the relay network model comprises a single source node, a plurality of relay nodes and a single target node, each relay node comprises a single input link and a multi-output link, and the target node is the only node with the multi-input link;
step B, establishing a product grid structure at the target node, wherein the product grid structure represents all possible scenes of decoding errors of the relay node and accurately models the correlation among error bits introduced by the decoding of the relay node;
step C, determining branch metrics according to the product grid structure;
and D, calculating the source information transmitted by the source node according to the product grid structure for determining the branch metric and the BCJR algorithm to obtain the decoding with the bit error rate approximate to the optimal decoding of the source information.
The invention also provides a system for obtaining the approximate optimal decoding of the bit error rate, which comprises the following steps:
the model building unit is used for building a relay network model based on a decoding forwarding protocol and a single source and single terminal, the relay network model comprises a single source node, a plurality of relay nodes and a single target node, each relay node is provided with a single input link and a multiple output link, and the target node is the only node with the multiple input links;
the network construction unit is used for establishing a product grid structure at the target node, wherein the product grid structure represents all possible scenes of decoding errors of the relay node and accurately models the correlation among error bits introduced by the decoding of the relay node;
a metric determination unit for determining branch metrics from the product trellis structure;
and the information calculation unit is used for calculating the source information transmitted by the source node according to the product grid structure for determining the branch metric and the BCJR algorithm to obtain the approximately optimal decoding of the bit error rate of the source information.
Compared with the prior art, the invention has the beneficial effects that: the embodiment of the invention provides a method for acquiring approximate optimal decoding of error rate of convolutional code coding in a single-source single-terminal complex relay network based on a decoding and forwarding protocol. The method for obtaining the bit error rate approximate optimal decoding of the single-source single-terminal complex relay network based on the convolutional code coding of the DF protocol can realize better performance than the existing decoding algorithm in different relay channel scenes.
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Fig. 1 is a flowchart of an obtaining method of decoding with an approximately optimal bit error rate according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a single-source single-terminal relay network model based on the DF protocol according to an embodiment of the present invention;
FIG. 3 is a flow chart of an NBOD algorithm provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a relay network model based on the DF protocol in an NBOD algorithm example provided in an embodiment of the present invention;
fig. 5 is a schematic diagram of a 5-node model of a DF-protocol-based single-source single-terminal relay network model according to an embodiment of the present invention;
fig. 6 is a schematic diagram comparing the performance of NBOD algorithm (T ═ 1) provided by the present invention with MRC, C-MRC and SDF algorithms provided by the prior art and the 5-node model provided in fig. 5;
fig. 7 is a schematic structural diagram of an acquisition system for decoding with an approximately optimal bit error rate according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a method for obtaining approximate optimal decoding of bit error rate as shown in figure 1, which comprises the following steps:
s101, establishing a single-source single-terminal relay network model based on a decoding forwarding protocol, wherein the relay network model comprises a single source node, a plurality of relay nodes and a single target node, each relay node comprises a single input link and a multi-output link, and the target node is the only node with the multi-input link;
s102, establishing a product grid structure at the target node, wherein the product grid structure represents all possible scenes of decoding errors of the relay node and accurately models the correlation among error bits introduced by the decoding of the relay node;
s103, determining branch metrics according to the product grid structure;
and S104, calculating the source information transmitted by the source node according to the product grid structure for determining the branch metric and the BCJR algorithm to obtain the decoding with the error rate approximate to the optimal decoding of the source information.
The following further describes the embodiments of the present invention with reference to fig. 2 to 6:
the embodiment of the invention provides a method for obtaining Bit Error Rate (BER) approximate optimal decoding of convolutional code coding in a complex relay network of a single source and a single terminal based on a DF protocol, which comprises the following steps:
step 1: establishing a relay network model of a single source and a single terminal based on a DF protocol;
the relay network model established in this step is a more general single-source single-terminal complex relay network model, and fig. 2 is a general structure of a single-source single-terminal relay network model based on the DF protocol. In this relay network model, each relay node has a single input link and multiple output links based on the DF protocol, and the target node is the only node with multiple input links. Further, each relay node physically having multiple input links may be logically represented as multiple relay nodes having a single input link.
It should be noted that, a general multi-hop multi-branch relay network based on the DF protocol is generally considered as a special example of the above network, because a path including all links from a source node to a relay node is unique, the source node, all the relay nodes and the links therebetween form a tree structure with the source node as a root node, the relay nodes as leaf nodes, and the outward links connected to a destination node.
The information sent from the source node S may reach the destination node D through different paths, that is: from source node S directly to destination node D; from the source node S to the relay node R and then to the destination node D. In the present embodiment, it is assumed that three links (source-to-destination link, source-to-relay link and relay-to-destination link) have signal-to-noise ratios (SNRs) γ respectivelysd,γsrAnd gammardThe binary input of (a) has no memory channel.
In addition, γ isijI, j ∈ { s, r, d }, which can be defined as:
wherein h isijIs the channel gain coefficient between node i and node j, Eb/N0Representing the information bit to gaussian noise power ratio.
It should be noted that, in the following description,is the set of all leaf nodes of the tree structure. For any of its nodes v, useAndrespectively representing the set of all nodes of its parent node, child nodes and subtrees rooted at it. In addition, | · | is used to represent the cardinality of a set.
All nodes of the relay network model are assumed to be in half-duplex mode, i.e. cannot transmit and receive at the same timeAnd receiving data. Therefore, the relay network model provided by the embodiment of the invention contains two different time slots in each communication packet. In time slot 1, the modulation symbol transmitted by the source node is Xs. Since wireless communication has the broadcast property, the relay node and the target node respectively use ysrAnd ysdIndicating a heard X with noisesAnd (4) observing.
ysd＝hsdxs+zsd
ysr＝hsrxs+zsr
Wherein z issrAnd zsdIs a bilateral power spectral density of N0/2 zero mean gaussian noise per dimension.
The relay node pair receives ysrDecoding and generating crTransmitting codeword c as a source nodesIs estimated. For the sake of no loss of generality, it is assumed that a Binary Phase Shift Keying (BPSK) modulator is used and all transmission symbols have standard unit power. In this embodiment, XrFrom crGenerated by a BPSK modulator. Since there may be unsuccessful decoding procedures at the relay node, XrIs not necessarily identical to Xs. In slot 2, the relay node will XrTransmitted to the destination node by yrdX representing noise received by the target noderAnd then:
yrd＝hrdxr+zrd
wherein z isrdIs a bilateral power spectral density of N0/2 zero mean gaussian noise per dimension. Based on y abovesdAnd yrdThe target node can recover the source information by a specific decoding scheme.
It should be noted that, in the relay network model provided in the embodiment of the present invention, there are relay nodes in G layers in total, and there is M in the G-th layergA relay node using Ri，jTo representThe j-th relay node at the i-th level. In addition, the source node and all the relay nodes transmit symbols through orthogonal channels, and a BER-optimal decoding algorithm is used for an information sequence u in the relay network modelsThe decoding rule of the ith bit of (1) is as follows:
wherein, ysdAndrepresenting the destination node' S link sum from S-to-D (source node to destination node)The received symbols of the link (relay node to target node).All leaf nodes corresponding to the representation relay nodeA set of symbols transmitted to the target node.
Step 2: a new product grid structure is established at the target node. The grid can accurately represent all possible scenes of decoding errors of the relay nodes and accurately model the correlation among error bits introduced by the decoding of the relay nodes;
specifically, the embodiment of the present invention establishes a product grid structure for representing the decoding error rate of the relay node, and calculates x according to a reasonably defined branch metric structure of the product grid structuresAnd xrAs a relay node, sending xrTo the target node, and the source node sends xsConditional probability of (3) Pr (x)r|xs) An approximation of.
In particular, the grid error representation is introduced in this embodiment to describe the grid error from xsTo xrIs possible error. Due to xsAnd xrThe error vector between the two codes is also a valid code word, so that the product grid structure with the same structure as the convolutional code can effectively represent all possible error code words and accurately reflect the correlation of error information. Furthermore, for the conditional probability Pr(xr|xs) Constructing a binding xsAnd xrProduct grid of, will Pr (x)r|xs) Is associated with the path metrics of the product trellis structure.
It should be noted that this product trellis representation can make the NBOD algorithm effectively solve the two difficulties of achieving optimal decoding. Firstly, each pair x can be effectively calculatedsAnd xrPr (x) of (C) to (C)r|xs) A PEP approximation of (a); second, based on the product grid structure, an improved BCJR algorithm disclosed in the art can be implemented by examining all possible (x)r，xs) Combining the decoding rules required to obtain BER optimal decoding algorithms known in the artAnd then calculates the binary message packet u of the source nodes。
Wherein the source node sends XsEach relay node transmits separatelyConditional probability of (2)The recursive decomposition can be performed according to the following formula:
wherein, XsModulation symbols representing source node transmissionsWhich comprisesAnd S1\S2(belongs to the set S1But not belonging to the set S2Elements of (d) are combined. Step (a) applies the property that the transmitted symbols of the nodes in different subtrees are conditionally independent due to mutually independent noise between the links. Step (b) followsAnd giveX ofsThe fact that they are independent of each other. Subsequent steps apply similar parameters to the second and lower levels of the tree in a recursive manner. Finally, after considering all G layers of the tree,is decomposed into the product of error probabilities associated with all links (except the incoming link to the target node) in the product grid structure described above. This paragraph sets forth a method of calculating conditional probability wherein step (a) corresponds to the equation in the above formulaStep (b) corresponds to the equation in the above formulaThe subsequent steps refer to equations of each step except equation (a) and equation (b).
It should be noted that, in the following description,can be approximated as PEP and further approximated as the sum of exponential functions. When T ═ 1, i.e. an exponential function is used to approximate the Q-function, the following can be obtained:
wherein C is a constant, dHIs calculatedAs a function of the number of locations of (c),is thatThe SNR of the link.
And step 3: reasonably defining branch metrics based on the product grid structure of step 2;
it should be noted that each of the above conditional probabilities can be calculated based on the proposed product lattice structure representation. Since there is a total ofA relay node capable of including X by establishing a network at the target nodesAnd all ofThe NBOD algorithm described above is applied with a product grid structure of order (Ψ + 1). Each path on the (Ψ +1) -order product trellis structure is associated with a metaancestor of (Ψ +1)And (4) associating. The first partial path metric on the (Ψ +1) -th order product trellis structure may be represented as follows:
in which all are consideredLinks (from all leaf relay nodes to target nodes) and S-to-D links, the second and third portions of the path metric may be specified asAnd Pr (y)sd|xs)。
In particular, in the representation of the first partial path metric,a first portion of the path metrics, represented as the t-th product trellis, is defined as:
where L is the total depth of the product grid structure, diIs thatNumber of positions of (a), (b)Anddenotes x associated with the state transition from time i-1 to time isAnd xrThe respective sub-sequence),is xsAnd xrHamming distance between. The above equationEach entry on the right is a branch metric for the corresponding state transition.
And 4, step 4: in the product grid structure described above, a new NBOD algorithm is applied. The complexity of the new NBOD algorithm is in linear relation with the length of the information block, the existing relay node adopting the traditional DF protocol does not need to be changed, and the new NBOD algorithm can be expanded to any linear block code with a grid structure. FIG. 3 is an NBOD algorithm provided by an embodiment of the invention.
Specifically, the NBOD algorithm provided by the embodiment of the present invention is based on a product mesh structure constructed at a target node and implemented by using a BCJR algorithm. The method specifically comprises the following steps:
step 41: calculating a state transition factor;
step 42: calculating a forward recursion factor;
step 43: calculating a backward recursion factor;
step 44: calculating a log-likelihood ratio (LLR) of the source information;
step 45: resulting in a hard decoded output of the source information.
It should be noted that the state transition factor in step 41 is a three-part path metric of the product trellis structure
Multiplication, i.e.:
initialization α from the t-th product lattice structuret，0＝[1，0，...，0]Initially, the forward recursion factor may be recursively calculated according to the following equation:
where Γ is the set of states that can reach state s' in the (l +1) th trellis depth.
Following initialization βt，h＝[1，0，...，0]The backward recursion factor from the tth product mesh structure to the lth mesh depth of the root may be recursively calculated according to the following formula:
where Γ' is the set of states connected to state s at the l-th trellis depth.
Source information us，lThe log likelihood ratio LLR of (a) may be calculated according to the following formula:
wherein, gamma is+And Γ-Is and us，l1 and us，l0(s) at the ith grid depthl，sl+1) A set of state pairs.
It should be noted that the complexity of the NBOD algorithm provided by the embodiment of the present invention is linear with the length of the information block, and can be extended to any linear block code with a trellis structure. The difference is that a time-varying trellis is associated with a linear block code, while a time-invariant trellis is associated with a convolutional code. Furthermore, the NBOD algorithm does not limit the source node and the relay node to having the same convolutional code, and the product trellis can be constructed by tracking the same number of information bits of each convolutional code during state transition.
Fig. 4 is a structure of a relay network model based on the DF protocol in an NBOD algorithm example provided in an embodiment of the present invention. Wherein R is1，1With two directions R2，1And R2，2The output link of (1). Because R is2，2And R3，1Are leaf nodes, so they can be used separatelyAndto indicate. Constructing a container containing xsAnd a 5 th order product trellis of all relay node transmission symbols. Wherein the first part of the path metric may be represented in the form:
the second part of the path metric is equal toThe third part is Pr (y)sd|xs). The generated path metrics may be decomposed into products of branch metrics. Therefore, the NBOD algorithm provided by the embodiment of the present invention can be used in this 5 th order product grid. In particular, the branch metric of the trellis representation in which the path metric is equal to the product of all branch metrics along this path is defined according to the likelihood of all relay nodes.
In order to evaluate the method for obtaining the approximate optimal bit error rate decoding of the convolutional code coding in the complex relay network of the single source and the single terminal based on the DF protocol, the NBOD algorithm is applied to the general network based on the DF protocol, and the BER performance is compared with the existing algorithm.
Fig. 5 is a 5-node model of a DF protocol-based single-source single-terminal relay network model, where SNRs of all links are the same and there are 3 relay nodes in the relay network model. Suppose from S, R1，1，R1，2And R2，1The transmitted symbols are respectively denoted as xs，Andthe target node may establishOne simultaneously takes into account xs，And4-order product lattice structure of (1). The NBOD algorithm provided by the embodiment of the present invention will be applied to this 4 th order product grid.
Selecting a polynomial generator (5, 7) with 8 system8The convolutional encoder of code rate 4 of 1/2, wherein the length of the information block is set to 256 bits. Fig. 6 is a comparison between NBOD algorithm (T ═ 1) provided by the embodiment of the present invention and MRC, C-MRC and SDF algorithms disclosed in the art on the DF protocol based 5-node relay network model illustrated in fig. 5, assuming that each link has the same SNR. It should be noted that when the SNR is low, the performance of the SDF algorithm is poor due to the high probability of packet error events. The NBOD algorithm provided by embodiments of the present invention is at least 1dB better than the other schemes when the BER is about 10 "4.
In summary, according to the method for obtaining approximate optimal decoding of BER of convolutional code coding in a complex relay network model of a single source and single terminal based on the DF protocol provided by the embodiments of the present invention, a new product grid structure with reasonably defined branch metrics is established at a target node by establishing the complex relay network model of the single source and single terminal based on the DF protocol, the product grid structure can accurately represent all possible scenarios of decoding errors of relay nodes, and a NBOD algorithm is used in the product grid structure to perform bit decoding of an information sequence. The NBOD algorithm exploits the fact that the existing decoding algorithm ignores that the erroneous data packets transmitted by the relaying channel are still valid codewords. In addition, the complexity of the NBOD algorithm is in linear relation with the length of the information block, the existing relay node adopting the traditional DF protocol does not need to be changed, and the NBOD algorithm can be expanded to any linear block code with a grid structure. The method for obtaining the BER approximate optimal decoding of the complex relay network of the single-source single-terminal based on the convolutional code coding of the DF protocol can realize better performance than the existing decoding algorithm in different relay channel scenes.
An embodiment of the present invention further provides a system for obtaining an approximately optimal decoding error rate, as shown in fig. 7, where the system includes:
a model building unit 701, configured to build a single-source single-terminal relay network model based on a decode-and-forward protocol, where the relay network model includes a single source node, a plurality of relay nodes, and a single target node, each relay node is composed of a single input link and a multiple output link, and the target node is a unique node with multiple input links;
a network constructing unit 702, configured to establish a product grid structure at the target node, where the product grid structure represents a scenario of all possible relay node decoding errors and accurately models a correlation between error bits introduced by relay node decoding;
a metric determining unit 703 for determining branch metrics according to the product trellis structure;
and the information calculation unit 704 is configured to calculate the source information transmitted by the source node according to the product grid structure for determining the branch metric and the BCJR algorithm, so as to obtain an approximately optimal decoding error rate of the source information.
Further, the source node is represented by S, the relay node is represented by R, the destination node is represented by D, and the source node to destination node link, the source node to relay node link and the relay node to destination node link in the relay network model all have signal-to-noise ratios γ respectivelysd、γsrAnd gammardBinary input of (2) memoryless channel, gammaijI, j ∈ (s, r, d), in which:
hijdenotes the channel gain coefficient between node i and node j, Eb/N0Representing the ratio of information bits to gaussian noise power;
each node in the relay network model is in a half-duplex mode which cannot transmit and receive data at the same time, and each communication packet contains two different time slots;
in the first time slot, the source node transmits a modulation symbol XsNoisy X heard by the relay node and the target nodesRespectively is ysrAnd ysrWherein:
Zsrand ZsdIs a bilateral power spectral density of N02 zero mean gaussian noise per dimension;
the relay node pair receives ysrDecoding and generating CrTransmitting codeword C as the source nodesAccording to CrGenerating a transmission signal Xr；
In the second time slot, the relay node will XrTransmitting to the target node with yrdRepresenting a noisy X received by the target noderWherein:
yrd＝hrdXr+Zrd，Zrdis a bilateral power spectral density of N0/2 zero mean gaussian noise per dimension.
Further, in the first time slot, the transmission signal XrFrom CrGenerated by a binary phase shift keying modulator.
Further, the branch metric includes three parts:
the first part is represented as:
the second part is represented as:
the third part is represented as: pr (y)sd|xs)；
Wherein,a first portion of path metrics representing a t-th product trellis, defined as:
l denotes the total depth of the product grid, diTo representThe number of the positions of (a) is,anddenotes x associated with the state transition from time i-1 to time isAnd xrThe respective sub-sequences of the sequence are,denotes xsAnd xrThe hamming distance between the first and second electrodes,for any node v, a set of all leaf nodes representing a tree structure formed by nodes in the relay network model is usedAndrespectively representing the set of all nodes of its parent node, child nodes and subtrees rooted at it.
Further, the information calculating unit 704 is specifically configured to:
firstly, calculating a state transition factor according to a product grid structure of the determined branch metrics;
secondly, calculating a forward recursion factor according to a product grid structure of the determined branch metrics;
then, calculating a backward recursion factor according to the product grid structure of the determined branch metrics;
then, according to the state transition factor, the forward recursion factor and the backward recursion factor, calculating the log likelihood ratio of the source information transmitted by the source node;
and finally, calculating according to the log-likelihood ratio of the source information to obtain the approximate optimal decoding of the bit error rate of the source information.
The embodiment of the present invention further provides a terminal, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, where when the processor executes the computer program, the steps of the method for obtaining an approximately optimal decoding with a bit error rate based on a decode-and-forward protocol shown in fig. 1 are implemented.
The embodiment of the present invention further provides a readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for obtaining approximately optimal decoding with bit error rate based on a decode-and-forward protocol shown in fig. 1.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. A method for obtaining approximately optimal decoding with bit error rate, comprising:
step A, establishing a relay network model based on a decoding forwarding protocol and a single source single terminal, wherein the relay network model comprises a single source node, a plurality of relay nodes and a single target node, each relay node comprises a single input link and a multi-output link, and the target node is the only node with the multi-input link;
step B, establishing a product grid structure at the target node, wherein the product grid structure represents all possible scenes of decoding errors of the relay node and accurately models the correlation among error bits introduced by the decoding of the relay node;
step C, determining branch metrics according to the product grid structure;
and D, calculating the source information transmitted by the source node according to the product grid structure for determining the branch metric and the BCJR algorithm to obtain the decoding with the bit error rate approximate to the optimal decoding of the source information.
2. The acquisition method according to claim 1, wherein in the step a, the source node is represented by S, the relay node is represented by R, the destination node is represented by D, and the relay network model has a signal-to-noise ratio γ for each of the source node-to-destination node link, the source node-to-relay node link and the relay node-to-destination node linksd、γsrAnd gammardBinary input of (2) memoryless channel, gammaijI, j ∈ (s, r, d), in which:
hijdenotes the channel gain coefficient between node i and node j, Eb/N0Representing the ratio of information bits to gaussian noise power;
each node in the relay network model is in a half-duplex mode which cannot transmit and receive data at the same time, and each communication packet contains two different time slots;
in the first time slot, the source node transmits a modulation symbol XsNoisy X heard by the relay node and the target nodesRespectively is ysrAnd ysrWherein:
Zsrand ZsdIs a bilateral power spectral density of N02 zero mean gaussian noise per dimension;
the relayThe node pair receives ysrDecoding and generating CrTransmitting codeword C as the source nodesAccording to CrGenerating a transmission signal Xr；
In the second time slot, the relay node will XrTransmitting to the target node with yrdRepresenting a noisy X received by the target noderWherein:
yrd＝hrdXr+Zrd，Zrdis a bilateral power spectral density of N0/2 zero mean gaussian noise per dimension.
3. The acquisition method of claim 2, wherein the transmission signal X is transmitted in a first time slotrFrom CrGenerated by a binary phase shift keying modulator.
4. The acquisition method of claim 2, wherein the branch metrics comprise three parts:
the first part is represented as:
the second part is represented as:
the third part is represented as: pr (y)sd|xs)；
Wherein,a first portion of path metrics representing a t-th product trellis, defined as:
l denotes the total depth of the product grid, diTo representThe number of the positions of (a) is,anddenotes X associated with the state transition from time i-1 to time isAnd XrThe respective sub-sequences of the sequence are,represents XsAnd XrThe hamming distance between the first and second electrodes,for any node v, a set of all leaf nodes representing a tree structure formed by nodes in the relay network model is usedAndrespectively representing the set of all nodes of its parent node, child nodes and subtrees rooted at it.
5. The acquisition method according to claim 4, wherein the step D comprises:
step D1, calculating a state transition factor according to the product grid structure of the determined branch metrics;
step D2, calculating a forward recursion factor according to the product grid structure of the determined branch metrics;
step D3, calculating backward recursion factor according to the product grid structure of the determined branch metric;
step D4, calculating the log-likelihood ratio of the source information transmitted by the source node according to the state transition factor, the forward recursion factor and the backward recursion factor;
and D5, calculating according to the log-likelihood ratio of the source information to obtain the approximate optimal solution decoding of the bit error rate of the source information.
6. An acquisition system for near-optimal decoding of bit error rates, comprising:
the model building unit is used for building a relay network model based on a decoding forwarding protocol and a single source and single terminal, the relay network model comprises a single source node, a plurality of relay nodes and a single target node, each relay node is provided with a single input link and a multiple output link, and the target node is the only node with the multiple input links;
the network construction unit is used for establishing a product grid structure at the target node, wherein the product grid structure represents all possible scenes of decoding errors of the relay node and accurately models the correlation among error bits introduced by the decoding of the relay node;
a metric determination unit for determining branch metrics from the product trellis structure;
and the information calculation unit is used for calculating the source information transmitted by the source node according to the product grid structure for determining the branch metric and the BCJR algorithm to obtain the approximately optimal decoding of the bit error rate of the source information.
7. The acquisition system according to claim 6, wherein the source node is denoted by S, the relay node is denoted by R, the destination node is denoted by D, and the relay network model has a signal-to-noise ratio γ for each of the source node-to-destination node link, the source node-to-relay node link, and the relay node-to-destination node linksd、γsrAnd gammardBinary input of (2) memoryless channel, gammaijI, j ∈ (s, r, d), in which:
hijdenotes the channel gain coefficient between node i and node j, Eb/N0Representing the ratio of information bits to gaussian noise power;
each node in the relay network model is in a half-duplex mode which cannot transmit and receive data at the same time, and each communication packet contains two different time slots;
in the first time slot, the source node transmits a modulation symbol XsNoisy X heard by the relay node and the target nodesRespectively is ysrAnd ysrWherein:
Zsrand ZsdIs a bilateral power spectral density of N02 zero mean gaussian noise per dimension;
the relay node pair receives ysrDecoding and generating CrTransmitting codeword C as the source nodesAccording to CrGenerating a transmission signal Xr；
In the second time slot, the relay node will XrTransmitting to the target node with yrdRepresenting a noisy X received by the target noderWherein:
yrd＝hrdXr+Zrd，Zrdis a bilateral power spectral density of N0/2 zero mean gaussian noise per dimension.
8. The acquisition system of claim 7, wherein the transmission signal X is transmitted in a first time slotrFrom CrGenerated by a binary phase shift keying modulator.
9. The acquisition system of claim 7 wherein the branch metrics comprise three parts:
the first part is represented as:
the second part is represented as:
the third part is represented as: pr (y)sd|xs)；
Wherein,a first portion of path metrics representing a t-th product trellis, defined as:
l denotes the total depth of the product grid, diTo representThe number of the positions of (a) is,anddenotes x associated with the state transition from time i-1 to time isAnd xrThe respective sub-sequences of the sequence are,denotes xsAnd xrThe hamming distance between the first and second electrodes,for any node v, a set of all leaf nodes representing a tree structure formed by nodes in the relay network model is usedAndrespectively representing the set of all nodes of its parent node, child nodes and subtrees rooted at it.
10. The acquisition system of claim 8, wherein the information calculation unit is specifically configured to:
firstly, calculating a state transition factor according to a product grid structure of the determined branch metrics;
secondly, calculating a forward recursion factor according to a product grid structure of the determined branch metrics;
then, calculating a backward recursion factor according to the product grid structure of the determined branch metrics;
then, according to the state transition factor, the forward recursion factor and the backward recursion factor, calculating the log likelihood ratio of the source information transmitted by the source node;
and finally, calculating according to the log-likelihood ratio of the source information to obtain the approximate optimal decoding of the bit error rate of the source information.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105144598A (en) * 2012-12-03 2015-12-09 数字无线功率有限公司 Systems and methods for advanced iterative decoding and channel estimation of concatenated coding systems
CN106982106A (en) * 2015-12-28 2017-07-25 法国矿业电信学校联盟 Recurrence sub-block is decoded
WO2017219718A1 (en) * 2016-06-22 2017-12-28 Huawei Technologies Co., Ltd. Phase noise estimation and cancellation
CN108370253A (en) * 2015-12-24 2018-08-03 英特尔公司 Mixed schedule for ldpc decoding and the assembly line based on latch

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105144598A (en) * 2012-12-03 2015-12-09 数字无线功率有限公司 Systems and methods for advanced iterative decoding and channel estimation of concatenated coding systems
CN108370253A (en) * 2015-12-24 2018-08-03 英特尔公司 Mixed schedule for ldpc decoding and the assembly line based on latch
CN106982106A (en) * 2015-12-28 2017-07-25 法国矿业电信学校联盟 Recurrence sub-block is decoded
WO2017219718A1 (en) * 2016-06-22 2017-12-28 Huawei Technologies Co., Ltd. Phase noise estimation and cancellation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
BIN QIAN: ""A Near BER-Optimal Decoding Algorithm for Convolutionally Coded Relay Channels With the Decode-and-Forward Protocol"", 《IEEE》 *

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