CN111988045B

CN111988045B - Improved polarization code SCF decoder based on genetic algorithm

Info

Publication number: CN111988045B
Application number: CN202010815415.XA
Authority: CN
Inventors: 王秀敏; 马强强; 李君�; 张鸿超
Original assignee: China Jiliang University
Current assignee: China Jiliang University
Priority date: 2020-08-14
Filing date: 2020-08-14
Publication date: 2024-04-05
Anticipated expiration: 2040-08-14
Also published as: CN111988045A

Abstract

The present invention provides an improved polar code serial cancellation flip (Successive Cancellation Flip, SCF) decoder based on genetic algorithm (Genetic Algorithm, GA). Redundancy problems exist for the original set of candidate flipped positions (Candidate Flipping Positions Set, CFPS) based on the original SCF decoder, a new CFPS is built by using GA. An initial population of genetic algorithms is constructed with an index of all non-frozen bits and the channel reliability calculated by gaussian approximation is taken as fitness for each individual. The population is then subjected to continuous selection, crossover and mutation operations, and the population-optimal individuals of each generation are preserved. Finally, a new candidate flip position set CFPS-GA is obtained by counting the occurrence frequency of each population in the vector, and SCF decoding is carried out by using the newly constructed candidate flip position set CFPS-GA. The invention has the beneficial effects that: compared with other similar SCF decoders, the SCF decoder based on CFPS-GA can have lower computational complexity and decoding delay under the premise of ensuring decoding performance.

Description

Improved polarization code SCF decoder based on genetic algorithm

Technical Field

The invention belongs to the field of channel coding and decoding, and relates to a serial cancellation flip (Successive Cancellation Flip, SCF) decoder of a polarization code and a genetic algorithm in an artificial intelligence technology.

Background

Polarization code was manufactured from 2009 sinceThere is a great deal of attention after this proposal, and in the newly released 5G communication standard, the polarization code is selected as the coding scheme under the eMBB scene control channel. Polarization codes are the only channel coding schemes that have been theoretically proven to reach the shannon theoretical limit. The most primitive polar decoder is a serial cancellation (Successive Cancellation, SC) decoder, and it is under this decoder that the polar code reaches shannon's theoretical limit. However, the SC decoder is a serial decoder, and if the decoding of the preceding bit is wrong, the decoding of the following bit is affectedCausing error propagation. To improve the decoding performance of SC decoders, a serial cancellation list (Successive Cancellation List, SCL) decoder is proposed. Unlike an SC decoder, an SCL decoder retains at most L decoding paths at the time of decoding, and then selects the final path as a decoding result by a path metric value or cyclic redundancy check (Cyclic Redundancy Check, CRC). The SCL decoder greatly improves the decoding performance of the polarization code, so that the polarization code has the same decoding performance as other error correcting codes. However, the SCL decoder retains multiple decoding paths when decoding, and this decoding characteristic makes the SCL decoder have higher decoding delay and computational complexity.

In order to improve the decoding performance of SC decoders and reduce the complexity of SCL decoders, the relevant scholars have proposed a decoder named SCF. In the SCF decoder, SC decoding is first performed. And then performing CRC check on the result obtained by the SC decoder. And when the result obtained by SC decoding does not pass the CRC check, sequencing the absolute values of the log likelihood ratios corresponding to all the non-frozen bits, then turning over the bit with the minimum absolute value of the log likelihood ratio, and continuously decoding the code word after turning over the bit by using an SC decoder. T (T) _max Representing the maximum number of such attempts. In actual communication, errors caused by channel noise are few, but most of the errors are caused by errors caused by channel noise. The SCF decoder finds the errors caused by the channel noise through continuous flipping attempts, so as to improve the decoding performance of the polarization code. While SCF decoders achieve a balance between decoding performance and decoding complexity compared to SC and SCL decoders, in the above-mentioned SCF decoding algorithm, the candidate set of flip positions (Candidate Flipping Positions Set, CFPS) is made up of an index of all non-frozen bits. However, it has been found experimentally that some sub-channels are very reliable and that no inversion attempts are required to invert the decoded bits of these sub-channels. Then, in order to reduce the size of CFPS and the search space of candidate flip positions, a learner proposes to use Critical Set (CS) as CFPS of SCF decoder. The CS is obtained by finding out the code tree of the polarized codesAll rate-1 nodes then use the index corresponding to the first non-frozen bit of the rate-1 node as an element in the CS. The CS-based SCF decoder effectively improves the decoding performance of the SCF decoder and reduces the computational complexity of the original SCF decoder. Meanwhile, some scholars have also proposed a concept of a medium level sub-channel (MBC) set by utilizing channel characteristics. The Monte Carlo simulation results indicate that the SCF decoder based on the MBC set has the same decoding performance and computational complexity as the SCF decoder based on CS.

Disclosure of Invention

The invention provides an improved polarization code SCF decoder based on a genetic algorithm aiming at the problem that redundancy exists in a candidate flip position set of the polarization code SCF decoder. The polarization code SCF decoding module consists of a standard SC decoding module, a CRC checking module and a candidate flip bit construction module. And the decoder adopts a new CFPS construction method based on genetic algorithm in the candidate flip bit construction module. In an implementation of the invention, the index of each non-frozen bit is used as the individual of the genetic algorithm, and then the index of all non-frozen bits is used to construct the initial population of the genetic algorithm. Since the channel environment of the present invention is additive white gaussian noise, the present invention uses the channel reliability calculated by gaussian approximation as the fitness of each individual. The selection strategy used is tournament selection. And (3) binary coding is carried out on the new population after the selection operation, then crossover and mutation operations are carried out on the newly generated population, and the optimal individuals of each generation are saved. When the iteration is over, the frequency of occurrence of each non-frozen bit index is counted, and then a new GA-based CFPS is constructed with those indexes having a frequency other than 0. The specific steps of the construction are as follows:

step one: the population is initialized with an index of all non-frozen bits and fitness of each individual is calculated using gaussian approximation.

Step two: and selecting individuals by using a tournament selection strategy to obtain a new selected population.

Step three: binary coding is carried out on the selected population, then crossover and mutation operations are carried out on individuals through a strategy of single-point crossover and simple mutation, and the optimal individuals in the new population after the mutation operations are stored in a vector path.

Step four: repeating the second and third steps until the iteration is finished, and then obtaining a final vector path, wherein the vector size and the iteration times of the population are the same.

Step five: the frequency of occurrence of each non-frozen bit index in the vector path is counted and then the index with a frequency other than 0 is put into the new CFPS.

By using the newly constructed CFPS to perform SCF decoding, experimental simulation can find that the SCF decoder based on the CFPS-GA can reach the upper limit of single-bit flip decoding performance. And compared with other similar SCF decoders, the SCF decoder based on CFPS-GA has lower computational complexity and decoding delay.

Drawings

FIG. 1 is a block diagram of an SCF decoder system of the present invention;

FIG. 2 is a flow chart of the CFPS-GA construction process of the present invention;

FIG. 3 is a schematic diagram of a variation operation of the present invention;

FIG. 4 shows the average complexity of decoding at 1024 codes by different decoders;

fig. 5 shows the average decoding delay of different decoders at 1024 codes.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings, the following examples being preferred examples of the application of the present invention and should not be construed as limiting the invention.

In fig. 1, the system block diagram mainly comprises a polarization code encoding module, a channel module and a polarization code SCF decoding module. In addition, the polarization code SCF decoding module consists of a standard SC decoding module, a CRC checking module and a candidate inversion bit construction module based on a genetic algorithm. The channel module employs additive white gaussian noise. The CRC check bit length in the CRC check module is 16, and the CRC generation polynomial is g (x) =x ¹⁶ +x ¹⁵ +x ² +1. Candidate overturning position construction module adopts genetic technology belonging to artificial intelligence fieldAlgorithm technology is transmitted. As shown in fig. 2, because the information transmitted by frozen bits is known to both the sender and receiver, flipping these bits when an SC decoding error occurs does not improve decoding performance, so the index of non-frozen bits is used for initialization of the population. Because the binary code starts from 0 and the index starts from 1, the binary code corresponds to a value 1 less than the decimal value when the individual is binary coded. And whether to perform the crossover operation on the parent individual is determined by the crossover rate. Since new indexes may be generated by interleaving and these newly generated indexes may be frozen bit indexes, it is necessary to check the generated indexes that only children of those indexes corresponding to non-frozen bits can be valid individuals. Fig. 3 is a schematic diagram of a mutation operation in which one individual 514 of the population is first converted by binary encoding into its corresponding binary vector 100000001.flag1 represents a randomly generated random number ranging from 0 to 1, p _m The mutation rate is shown. As can be seen from fig. 3, the binary vector can be mutated from 1000000001 to 1000001010 by a mutation operation.

Based on the construction flow chart of fig. 2, the code length is 1024, the code rate is 0.5, and the signal-to-noise ratio is 2.5dB. The population size is the same as the number of non-frozen bits. Because the original SCF decoding requires ordering of the log likelihood ratios of all the non-frozen bits, its CFPS size is equal to the number of non-frozen bit bits. Under the same condition, the CFPS-GA constructed by the genetic algorithm has smaller set, so that the decoding complexity and decoding delay of the SCF decoder based on the genetic algorithm improvement can be effectively reduced.

In the simulation experiment, the code length is 1024, the code rate is 0.5, and the simulation condition adopts a control variable method. SCF-GA is used to represent the SCF decoder of CFPS-GA constructed based on genetic algorithm, and SCF-CS is used to represent the SCF decoder based on key set. While SCO1 represents the upper decoding performance limit of a single bit flip-flop decoder. Simulation experiment results show that under all signal-to-noise ratios, the SCF-GA decoder and the SCO1 decoder have almost the same decoding performance. And when the signal-to-noise ratio is less than 2dB, the SCF-GA decoder has the same decoding performance as the SCF-CS and the original SCF decoderCan be used. But better decoding performance can be achieved by the SCF-GA decoder when the signal-to-noise ratio is greater than 2 dB. SCF-CS has a frame error rate of 10 when compared to the original SCF decoder ^-3 The SCF-GA decoder has a performance gain of about 0.1 dB. In fig. 4, the present invention represents the average normalized computational complexity with an additional number of inversions. As the signal-to-noise ratio increases, the complexity of various SCF decoders quickly drops to the same level as SC decoders. Since the CFPS-GA constructed in the present invention contains fewer non-frozen bit indices, the search space is smaller when determining the flip position. It can be seen from fig. 5 that the SCF-GA decoder has a lower decoding delay over the entire signal-to-noise ratio range.

The above embodiments are not intended to limit the present invention in any way, and all technical solutions obtained by using the similar structures, methods and similar variations of the present invention are within the scope of the present invention.

Claims

1. An improved polarization code SCF decoder based on genetic algorithm, characterized in that: the polarization code SCF decoder comprises a polarization code SCF decoding module, wherein the polarization code SCF decoding module comprises a standard SC decoding module, a CRC checking module and a candidate turnover bit construction module based on a genetic algorithm, and the candidate turnover bit construction module constructs candidate turnover bits through the genetic algorithm and comprises the following steps:

utilizing indexes of all non-frozen bits to form an initial population of a genetic algorithm;

calculating the fitness of each individual by using Gaussian approximation, and then performing selection operation on the individual, wherein the selection strategy is selected for tournament;

step three, binary coding is carried out on the selected individuals, then the intersection and mutation operation is carried out on the individuals, and each generation of optimal individuals are stored;

and step four, counting the number of times of taking each frozen bit index as each generation of optimal individuals, and forming a new candidate position set by using indexes with non-0 counted times as a final construction result.