CN112332863A - Polar code decoding algorithm, receiving end and system under low signal-to-noise ratio scene of low earth orbit satellite - Google Patents
Polar code decoding algorithm, receiving end and system under low signal-to-noise ratio scene of low earth orbit satellite Download PDFInfo
- Publication number
- CN112332863A CN112332863A CN202011169347.0A CN202011169347A CN112332863A CN 112332863 A CN112332863 A CN 112332863A CN 202011169347 A CN202011169347 A CN 202011169347A CN 112332863 A CN112332863 A CN 112332863A
- Authority
- CN
- China
- Prior art keywords
- decoding
- network modules
- signal
- information
- neural network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013528 artificial neural network Methods 0.000 claims abstract description 60
- 238000012549 training Methods 0.000 claims abstract description 15
- 230000003993 interaction Effects 0.000 claims abstract description 12
- 238000000034 method Methods 0.000 claims abstract description 9
- 230000010287 polarization Effects 0.000 claims description 19
- 238000004891 communication Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000005540 biological transmission Effects 0.000 claims description 10
- 239000011159 matrix material Substances 0.000 claims description 8
- 239000000654 additive Substances 0.000 claims description 7
- 230000000996 additive effect Effects 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 241000169170 Boreogadus saida Species 0.000 claims 5
- 230000004913 activation Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- H03M13/13—Linear codes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Error Detection And Correction (AREA)
Abstract
The invention discloses a polar code decoding algorithm, a receiving end and a system under a low signal-to-noise ratio scene of a low earth orbit satellite. The algorithm comprises the following steps: s1, demodulating the received polarized signal to obtain a signal stream, and calculating the likelihood value of the signal stream; s2, inputting the likelihood value into a decoder, and outputting a decoding result by the decoder; the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to different decoding links, likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a block mode, and output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results. Compared with the traditional SC algorithm, the method reduces the decoding delay, improves the decoding accuracy and improves the training speed of the neural network.
Description
Technical Field
The invention relates to the technical field of coding and decoding, in particular to a polar code decoding algorithm, a receiving end and a system under a low signal-to-noise ratio scene of a low earth orbit satellite.
Background
Since the polar code proposed by Arikan in 2009, the polar code, a completely new coding and decoding method, has been highly successful. The polar code shows considerable advantages in the aspects of encoding and decoding complexity and error rate control of encoding and decoding.
In the decoding Algorithm portion, the currently used algorithms are mainly a Successive Cancellation Algorithm (SC Algorithm, derived algorithms such as SCL, etc.) and a Belief Propagation (BP) Algorithm. The SC algorithm has the advantage of low computation complexity (according to the analysis of Arikan, the complexity of the SC decoding algorithm is N log N), but the advantage of the polarization code in the aspect of channel polarization can be reflected only when the code length reaches a certain degree, the decoding accuracy of the SC algorithm under a short code is improved at the cost of improving the computation complexity by the SCL algorithm, and the SC algorithm is inevitably incapable of adopting a parallel algorithm due to the characteristics of the SC algorithm, and meanwhile, the algorithm has relatively poor capability in the aspect of error code correction; although the BP algorithm can adopt parallel computation, the complexity is high, and an ideal bit error rate cannot be achieved at a low iteration number.
In recent years, with the rise of deep learning algorithms and corresponding artificial neural networks, the strong judgment capability of deep learning brings new ideas for decoding algorithms. With the background that polarization codes become the standard of 5G wireless communication, the artificial intelligence neural network receives attention to the improvement of the decoding speed. However, under the premise of lack of a large enough amount of training data, a segmented Linear neural network Decoding Model (PLNN) becomes a more ideal choice. Piecewise Linear Neural Networks (PLNN), i.e., neural networks whose activation functions employ analytical linear functions. Typical piecewise linear activation functions include ReLU and ReLU family activation functions, MaxOut activation functions.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly provides a polar code decoding algorithm, a receiving end and a system under the low signal-to-noise ratio scene of a low earth orbit satellite.
In order to achieve the above object, according to a first aspect of the present invention, there is provided a polar code decoding algorithm in a low signal-to-noise ratio scenario of a low earth orbit satellite, including: s1, demodulating the received polarized signal to obtain a signal stream, and calculating the likelihood value of the signal stream; s2, inputting the likelihood values into a decoder, the decoder outputting a decoding result; the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to the positions on different decoding links, the likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a block mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
The technical scheme is as follows: each block of the likelihood value is successively input into a corresponding decoding link for decoding, the decoder can process the likelihood value blocks in parallel, iterative learning is carried out on the blocks of the likelihood value in each decoding link sequentially through M neural network modules, and a judgment result is output.
In order to achieve the above object, according to a second aspect of the present invention, there is disclosed a receiving end comprising a communication module, a likelihood value calculation module, and a decoder; the likelihood value calculation module is respectively connected with the communication module and the decoder; the communication module receives a polarization signal sent by a transmitting end, demodulates the received polarization signal to form a signal stream, and transmits the signal stream to the likelihood value calculation module; the likelihood value calculation module calculates the likelihood value of the signal flow and outputs the likelihood value to a decoder; the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to the positions on different decoding links, the likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a block mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
The technical scheme is as follows: the receiving end decodes the received polarization signal, and successively inputs each block of the likelihood value into a corresponding decoding link for decoding in the decoding process, the decoder can process the blocks of the likelihood value in parallel, and iterates and learns the blocks of the likelihood value in each decoding link sequentially through M neural network modules to output a judgment result.
In order to achieve the above object of the present invention, according to a third aspect of the present invention, there is disclosed a communication system comprising a transmitting end and a receiving end; the transmitting terminal performs linear conversion on N bit channels to obtain a channel with transmission characteristic polarization, encodes information to be transmitted, performs CQPSK modulation on the encoded information, and transmits the information to the receiving terminal through an additive white Gaussian noise channel; the receiving end demodulates the received polarized signal to form a signal flow, and calculates the likelihood value of the signal flow; the receiving end inputs the likelihood value into a decoder, and the decoder outputs a decoding result; the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to the positions on different decoding links, the likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a block mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
The technical scheme is as follows: the system determines the information bit and the fixed bit of a decoder at a transmitting end, the generated signal is transmitted to a receiving end through an AWGN channel after being modulated by CQPSK, the receiving end of the system decodes the received polarized signal, and each block of a likelihood value is successively input into a corresponding decoding link for decoding in the decoding process, the decoder can process the block of the likelihood value in parallel, iterative learning is carried out on the blocks of the likelihood value in each decoding link by M neural network modules in turn, and a judgment result is output, compared with the traditional SC algorithm, the system adopts a mode of combining a BP algorithm and a PLNN to reduce decoding delay and improve decoding accuracy, the neural network module divides data into a plurality of short codes and cannot improve training speed and reduce the requirement of a training set required by training, the system has adjustable computation complexity and the number of neural network code blocks, the network training is very flexible.
Drawings
FIG. 1 is a block diagram of a decoder according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a BP network structure according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a BP algorithm network element according to an embodiment of the present invention;
fig. 4 is a flow chart illustrating an implementation of a communication system according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The invention provides a polar code decoding algorithm under a low signal-to-noise ratio scene of a low earth orbit satellite, which comprises the following steps in a preferred embodiment: s1, demodulating the received polarized signal to obtain a signal stream, and calculating the likelihood value of the signal stream; s2, inputting the likelihood value into a decoder, and outputting a decoding result by the decoder; the structure of the decoder is as shown in fig. 1, the decoder comprises at least one decoding link, each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to positions on different decoding links, likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a block mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
In this embodiment, each decoding chain is provided with a first BP decoding network module, a first neural network module, a second BP decoding network module, a second neural network module, … …, an mth BP decoding network module, and an mth neural network module in sequence from input to output. All decoding network modules with the same sequence number on the decoding chain consider the positions of the decoding network modules to be corresponding, for example, the positions of the first BP decoding network modules on all decoding chains are corresponding, an integral BP decoding network is formed among all BP decoding network modules with the corresponding positions, the integral decoding network structure is shown in FIG. 2, the total bit number of information flow is assumed to be 8 bits in FIG. 2, each bit of data occupies one layer of network, wherein the information bit is 4 bits, and the fixed bit is 4 bits, as shown in FIG. 2, data interaction is needed among the networks of each layer, and therefore, data interaction branches are arranged among the BP decoding network modules with the corresponding positions on different decoding chains.
In this embodiment, as shown in fig. 1, on any decoding link, the information output by the BP decoding network module enters the adjacent neural network module below for learning, and the output signal on the decoding link is subjected to iterative learning processing by M neural network modules. The neural network module may select an existing neural network structure.
In this embodiment, a schematic diagram of an arithmetic network element structure of the BP decoding network module is shown in fig. 3, where an information stream in the BP decoding network is divided into two parts, one part is an information stream from right to left (denoted by L), and the other part is an information stream from left to right (denoted by R).
In this embodiment, preferably, the BP algorithm in the BP network module is:
In this embodiment, in the initial phase of the BP algorithm, as shown in fig. 2, in the BP decoding network structure, the initial value of the input layer of the first BP decoding network module in the decoding chain is a partial data bit of the likelihood value, the initial state of the output layer of the first BP decoding network module is an information bit of 0, the fixed bit is infinity, and the initial values of the nodes between the input layer and the output layer are all 0. It is known which bits are information bits and which bits are fixed bits and are transmitted to the receiving end after polarization processing at the transmitting end.
In a preferred embodiment, the likelihood value LLR (y) of the signal stream y is calculated by the following formula;
wherein σ2Representing the noise variance of the transmission signal path between the transmitting end and the receiving end. In this embodiment, the noise is preferably, but not limited to, desirably 0 and the variance σ2The transmission signal channel between the transmitting end and the receiving end is an Additive White Gaussian Noise (AWGN) channel.
In a preferred embodiment, the neural network module comprises an input layer, T hidden layers and an output layer, and the whole neural network can be represented as: { L0，L1，L2，...，LT，LT+1}，L1To LTFor the hidden layer, L0And LT+1An input layer and an output layer respectively; the t-th layer output vector is: ot＝ft(ot-1)＝φt(Ct)；
Wherein, the variable Ct＝ot-1ωt+bt，ωtWeight matrix representing the layer t network, btBias matrix of the t-th network, ot-1Representing the input vector, T ∈ [1, T +1 ]]And T is a positive integer.
In a preferred embodiment, the weight matrix ω of the t-layer network of the neural network moduletAnd a bias matrix btObtained by back propagation training, the loss function L in back propagation training is:
In a preferred embodiment, in the decoding link, the output signal of the BP network module is input to the next adjacent neural network module and returned to the input end of the BP network module, the next adjacent neural network module performs learning processing on the input signal to obtain a soft judgment result, and the neural network module inputs the soft judgment result to the next adjacent BP network module.
In a preferred embodiment, the method further includes step S0: step S0 is: as shown in fig. 4, a transmitting end performs linear conversion on N bit channels to obtain a channel with polarized transmission characteristics, encodes information to be transmitted, performs CQPSK modulation on the encoded information, and transmits the information to a receiving end through an additive white gaussian noise channel, where N is a positive integer.
In a preferred embodiment, the information to be transmitted is encoded using the following formula:
wherein x represents information after encoding processing, u represents information to be transmitted,
representing the kronecker product of order n of G.
In the present embodiment, the principle of the polarization code is a channel polarization theory, and a channel with a polarized transmission characteristic can be obtained by performing specific linear conversion on N bit channels. The virtual channel with higher transmission quality is used as information bit, the rest bits do not transmit information, and the fixed bit stream is sent. The polar code structure is represented by p (N, K), where N is the code length, and N is generally an nth power of 2, that is, N is 2nK represents the length occupied by the information bits, the fixed information bits are N-K, the fixed bits are generally processed by all 0 or all 1, and the code rate of the polarization code is
Transmitting the modulated signal to a receiving end through an Additive White Gaussian Noise (AWGN) channel for decoding after CQPSK modulation, and marking the received signal as y ═ 1-2x) + z, wherein z is an expected 0 variance σ2White gaussian noise. The transmitting end firstly completes source coding, determines the information bit and the fixed bit of the decoder, and the generated signal is transmitted to the decoder through an AWGN channel by CQPSK modulation.
The invention also discloses a receiving end, in a preferred embodiment, the receiving end comprises a communication module, a likelihood value calculating module and a decoder; the likelihood value calculation module is respectively connected with the communication module and the decoder; the communication module receives the polarization signal sent by the transmitting terminal, demodulates the received polarization signal to form a signal stream, and transmits the signal stream to the likelihood value calculation module; the likelihood value calculation module calculates the likelihood value of the signal flow and outputs the likelihood value to the decoder; the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to different decoding links, likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a partitioning mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
The invention also discloses a communication system, in a preferred embodiment, the system comprises a sending end and a receiving end; the sending end carries out linear conversion on N bit channels to obtain a channel with transmission characteristic polarization, carries out coding processing on information to be sent, carries out CQPSK modulation on the coded information and then sends the coded information to the receiving end through an additive white Gaussian noise channel; the receiving end demodulates the received polarized signal to form a signal flow, and calculates the likelihood value of the signal flow; the receiving end inputs the likelihood value into a decoder, and the decoder outputs a decoding result; the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to different decoding links, likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a partitioning mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A polar code decoding algorithm under a low signal-to-noise ratio scene of a low earth orbit satellite is characterized by comprising the following steps:
s1, demodulating the received polarized signal to obtain a signal stream, and calculating the likelihood value of the signal stream;
s2, inputting the likelihood values into a decoder, the decoder outputting a decoding result;
the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to the positions on different decoding links, the likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a block mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
2. The algorithm for decoding polarization codes in low signal-to-noise ratio of low earth orbit satellites as claimed in claim 1, wherein the likelihood value LLR (y) of the signal stream y is calculated by the following formula;
3. The polar code decoding algorithm in the low signal-to-noise ratio scene of the low earth orbit satellite of claim 1, wherein the BP algorithm in the BP network module is:
wherein the function
A. B represents two variables respectively; ri,jInformation flow to the right of the ith bit of the jth layer in the BP network module, Li,jInformation flow to the left of the ith bit of the jth layer in the BP network module, Li,j+1The information flow to the left of the ith bit of the j +1 th layer in the BP network module,
i +2 of j +1 layer in BP network modulej-1The information stream to the left of a bit,
i +2 th layer of j in BP network modulej-1The information stream to the left of a bit,
i +2 th layer of j in BP network modulej-1Information flow with one bit to the right, Ri,j+1An information flow indicating the ith bit of the j +1 th layer in the BP network module to the right,
i +2 of j +1 layer in BP network modulej-1The information stream with bits to the right, i and j are positive integers.
4. The algorithm for decoding polar codes in low signal-to-noise ratio of low earth orbit satellites as claimed in claim 1, wherein the neural network module comprises an input layer, T hidden layers and an output layer, and the output vector of the T layer is:
ot＝ft(ot-1)＝φt(Ct)；
wherein, the variable Ct＝ot-1ωt+bt，ωtWeight matrix representing the layer t network, btBias matrix of the t-th network, ot-1Representing the input vector, T ∈ [1, T +1 ]]And T is a positive integer.
5. The algorithm for decoding polar codes in the low signal-to-noise ratio scenario of low earth orbit satellites as claimed in claim 4 wherein the weight matrix ω of the t-layer network of the neural network moduletAnd a bias matrix btObtained by back propagation training, the loss function L in back propagation training is:
wherein k represents the number of samples of a training set of the back propagation training; bi'Signal value representing i 'th information bit of sample, i.e. signal value of i' th information bit output from BP network module of preceding stage, bi'∈{0,1}；
A soft decision result representing the i' th information bit of the sample,
i' is a positive integer.
6. The algorithm for decoding polar codes under the low snr scenario of a low earth orbit satellite according to claim 1, wherein in a decoding link, an output signal of a BP network module is input to a next adjacent neural network module and simultaneously returned to an input end of the BP network module, the next adjacent neural network module performs a learning process on the input signal to obtain a soft decision result, and the neural network module inputs the soft decision result to the next adjacent BP network module.
7. The algorithm for decoding polar codes under low signal-to-noise ratio of low earth orbit satellites as claimed in claim 1, further comprising step S0:
the step S0 is:
the transmitting end carries out linear conversion on N bit channels to obtain a channel with transmission characteristic polarization, coding information to be transmitted, carrying out CQPSK modulation on the coded information, and then transmitting the coded information to the receiving end through an additive white Gaussian noise channel, wherein N is a positive integer.
8. The algorithm for decoding polar codes in low signal-to-noise ratio scenarios of low earth orbit satellites as claimed in claim 7,
the information to be sent is coded by the following formula:
wherein x represents information after encoding processing, u represents information to be transmitted,
representing the kronecker product of order n of G.
9. The receiving end is characterized by comprising a communication module, a likelihood value calculation module and a decoder; the likelihood value calculation module is respectively connected with the communication module and the decoder;
the communication module receives a polarization signal sent by a transmitting end, demodulates the received polarization signal to form a signal stream, and transmits the signal stream to the likelihood value calculation module;
the likelihood value calculation module calculates the likelihood value of the signal flow and outputs the likelihood value to a decoder;
the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to the positions on different decoding links, the likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a block mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
10. A communication system is characterized by comprising a sending end and a receiving end;
the transmitting terminal performs linear conversion on N bit channels to obtain a channel with transmission characteristic polarization, encodes information to be transmitted, performs CQPSK modulation on the encoded information, and transmits the information to the receiving terminal through an additive white Gaussian noise channel;
the receiving end demodulates the received polarized signal to form a signal flow, and calculates the likelihood value of the signal flow; the receiving end inputs the likelihood value into a decoder, and the decoder outputs a decoding result; the decoder comprises at least one decoding link, wherein each decoding link is provided with M BP decoding network modules and M neural network modules, the BP decoding network modules and the neural network modules are alternately arranged, data interaction branches are arranged between the BP decoding network modules corresponding to the positions on different decoding links, the likelihood values are input into the BP decoding network modules at the starting ends of the decoding links in a block mode, output signals of the neural network modules at the tail ends of all the decoding links are combined to obtain decoding results, and M is a positive integer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011169347.0A CN112332863B (en) | 2020-10-27 | 2020-10-27 | Polar code decoding algorithm, receiving end and system under low signal-to-noise ratio scene of low orbit satellite |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011169347.0A CN112332863B (en) | 2020-10-27 | 2020-10-27 | Polar code decoding algorithm, receiving end and system under low signal-to-noise ratio scene of low orbit satellite |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112332863A true CN112332863A (en) | 2021-02-05 |
| CN112332863B CN112332863B (en) | 2023-09-05 |
Family
ID=74296944
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011169347.0A Active CN112332863B (en) | 2020-10-27 | 2020-10-27 | Polar code decoding algorithm, receiving end and system under low signal-to-noise ratio scene of low orbit satellite |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112332863B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113162632A (en) * | 2021-04-29 | 2021-07-23 | 东方红卫星移动通信有限公司 | Intelligent QC-LDPC decoding method, decoder and low-orbit satellite communication system |
| CN113316216A (en) * | 2021-05-26 | 2021-08-27 | 电子科技大学 | Routing method for micro-nano satellite network |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107241106A (en) * | 2017-05-24 | 2017-10-10 | 东南大学 | Polarization code decoding algorithm based on deep learning |
| CN109495116A (en) * | 2018-10-19 | 2019-03-19 | 东南大学 | The SC-BP hybrid decoding method of polarization code and its adjustable hardware structure |
| CN109586730A (en) * | 2018-12-06 | 2019-04-05 | 电子科技大学 | It is a kind of based on the polarization code BP decoding algorithm intelligently post-processed |
| CN110212922A (en) * | 2019-06-03 | 2019-09-06 | 南京宁麒智能计算芯片研究院有限公司 | A kind of polarization code adaptive decoding method and system |
| CN110798228A (en) * | 2019-10-29 | 2020-02-14 | 南京宁麒智能计算芯片研究院有限公司 | Polarization code turning decoding method and system based on deep learning |
| CN111106839A (en) * | 2019-12-19 | 2020-05-05 | 北京邮电大学 | Polarization code decoding method and device based on neural network |
| CN111711455A (en) * | 2020-05-26 | 2020-09-25 | 北京邮电大学 | Polarization code BP decoding method based on neural network |
-
2020
- 2020-10-27 CN CN202011169347.0A patent/CN112332863B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107241106A (en) * | 2017-05-24 | 2017-10-10 | 东南大学 | Polarization code decoding algorithm based on deep learning |
| CN109495116A (en) * | 2018-10-19 | 2019-03-19 | 东南大学 | The SC-BP hybrid decoding method of polarization code and its adjustable hardware structure |
| CN109586730A (en) * | 2018-12-06 | 2019-04-05 | 电子科技大学 | It is a kind of based on the polarization code BP decoding algorithm intelligently post-processed |
| CN110212922A (en) * | 2019-06-03 | 2019-09-06 | 南京宁麒智能计算芯片研究院有限公司 | A kind of polarization code adaptive decoding method and system |
| CN110798228A (en) * | 2019-10-29 | 2020-02-14 | 南京宁麒智能计算芯片研究院有限公司 | Polarization code turning decoding method and system based on deep learning |
| CN111106839A (en) * | 2019-12-19 | 2020-05-05 | 北京邮电大学 | Polarization code decoding method and device based on neural network |
| CN111711455A (en) * | 2020-05-26 | 2020-09-25 | 北京邮电大学 | Polarization code BP decoding method based on neural network |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113162632A (en) * | 2021-04-29 | 2021-07-23 | 东方红卫星移动通信有限公司 | Intelligent QC-LDPC decoding method, decoder and low-orbit satellite communication system |
| CN113316216A (en) * | 2021-05-26 | 2021-08-27 | 电子科技大学 | Routing method for micro-nano satellite network |
| CN113316216B (en) * | 2021-05-26 | 2022-04-08 | 电子科技大学 | Routing method for micro-nano satellite network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112332863B (en) | 2023-09-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109586730B (en) | Polarization code BP decoding algorithm based on intelligent post-processing | |
| CN109257148B (en) | Polarization code BP decoding method based on Gaussian approximate threshold judgment | |
| US20050265387A1 (en) | General code design for the relay channel and factor graph decoding | |
| US7668267B2 (en) | Search efficient MIMO trellis decoder | |
| CN112332863B (en) | Polar code decoding algorithm, receiving end and system under low signal-to-noise ratio scene of low orbit satellite | |
| CN105897379A (en) | Polarization code cascade space-time code system and cascade polarization code coding method thereof | |
| Ye et al. | Circular convolutional auto-encoder for channel coding | |
| CN101119177B (en) | Bit-symbol signal processing method for coherent communication machine | |
| CN101420291A (en) | Combined decoding method for network and channel code in relay system | |
| CN113381799B (en) | Low orbit satellite-ground link end-to-end sparse code multiple access method based on convolutional neural network | |
| JPH1075271A (en) | Transmitting device and receiving device | |
| CN112332864A (en) | Polar code decoding method and system for self-adaptive ordered mobile pruning list | |
| US7218683B2 (en) | Channel encoding/decoding method and multiple-antenna communication transmitting/receiving system performing the same | |
| CN110430018B (en) | Bals code transmission method for sliding window with balanced protection | |
| CN107094026B (en) | Graph merging detection decoding method for NB-LDPC coding | |
| CN110601699B (en) | Method for realizing code rate dynamic variable multi-element LDPC code | |
| US20060182208A1 (en) | STBC scheme using MMSE decision in a non quasi-static channel | |
| Wang et al. | A novel deep learning architecture for wireless image transmission | |
| CN116418395A (en) | Relay laser communication coding method for FSO system | |
| CN106941393B (en) | LDPC interpretation method and system based on SOR | |
| CN107682122B (en) | Iterative demodulation decoding method of wireless optical communication multilevel coding modulation system | |
| CN112803951A (en) | Method for reducing noise of communication system received signal, receiving end and communication system | |
| CN111245568A (en) | Polar code decoding method based on feedback retransmission technology in low-earth orbit satellite | |
| CN113395138B (en) | PC-SCMA joint iterative detection decoding method based on deep learning | |
| CN110719112A (en) | Deep learning-based parameter adaptive RS code decoding method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: 401123 39-1, Building 1, No. 64, Middle Huangshan Avenue, Yubei District, Chongqing Patentee after: China Star Network Application Co.,Ltd. Address before: 618 Liangjiang Avenue, Longxing Town, Yubei District, Chongqing Patentee before: Dongfanghong Satellite Mobile Communication Co.,Ltd. |