CN104393875A - Channel decoding method based on optimized logarithmic likelihood probability-belief propagation (LLP-BP) algorithm - Google Patents
Channel decoding method based on optimized logarithmic likelihood probability-belief propagation (LLP-BP) algorithm Download PDFInfo
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Abstract
The invention discloses a channel decoding method based on optimized logarithmic likelihood probability-belief propagation (LLP-BP) algorithm. According to the method, based on logarithmic likelihood ratio-belief propagation (LLR-BP) algorithm, the introduction of a multiplying weakening related factor compensates the correlation between variable messages in processing of variable information; and check nodes of the LLR-BP algorithm are simplified, so that the complexity of the LLR-BP algorithm is lowered. According to the channel decoding method based on the optimized LLP-BP algorithm, an improved LLR-BP decoding method based on optimization of variable nodes and simplification of check nodes is provided, so that the decoding complexity is reduced while the decoding performance is improved.
Description
The present invention obtains Tianjin Natural Science Fund In The Light, fund number: 13JCYBJC15800's is self-service.
Technical field
The invention belongs to mobile communication coding and decoding technical field, the belief propagation relating to a kind of LDPC code improves interpretation method.The method is mainly used in the decoding in MIMO-OFDM technology.
Background technology
On the basis not increasing system bandwidth or transmitting power, MIMO(Multiple Input Multiple Output, multiple-input and multiple-output) technology can realize more efficiently, more reliable transfer of data, the availability of frequency spectrum of further raising system.The application of mimo system multi-path jamming effect is had new value, can utilize multipath component to come anti-multipath fading.Mimo system is more applicable to modern wireless communication systems compared with traditional a single aerial system, furthers investigate to it pith becoming the communications field.
But, the effect that MIMO technology is but so not good to the frequency selective fading reducing channel, it is use balancing technique that the solution for this problem mainly contains two kinds: one class; Another kind of is use OFDM(Orthogonal Frequency Division Multiplexing, OFDM) technology.In recent years, OFDM technology receives the concern of a lot of researcher.OFDM technology is also multi-carrier modulation (MCM) technology, and its Neng Ba mono-tunnel data flow serioparallel exchange is at a high speed the low rate data streams of multichannel.The intersymbol interference problem that it also has good anti-fading ability and reduces between adjacent channel.Gather the communication system of MIMO and OFDM, given full play to the advantage of the two, improve systematic function.
MIMO-OFDM system can not only improve capability of wireless communication system, more changeable in suitable for modern radio communication channel, realizes the transmission of large data at a high speed; And communication system systematic function on a frequency-selective fading can be improved further, improve communication quality.LDPC code has the advantage that other chnnel codings do not have, and as low in decoding complexity, flexible design, preferably stochastic behaviour, its performance can more close to shannon limit etc.Therefore, by LDPC code (Low Density Parity Check Code, low density parity check code) as the chnnel coding of MIMO-OFDM communication system to improve the optimal selection that system communication performance is Modern wireless communication research field.
Belief propagation (BP) algorithm is the Tanner(check matrix based on LDPC code) graph structure, in the process of iterative decoding, the limit of reliability information through figure is transmitted between node, draws the value tended towards stability through successive ignition, do optimal decoding judgement accordingly.Have employed the LDPC code of BP algorithm, have the performance closer to Shannon tolerance limit.The main thought of BP algorithm is exactly the transmission of information, completes the renewal of information between variable node and check-node, through the interative computation preset, realizes the transmission of information in whole Tanner figure.
Due to carry out BP decoding algorithm process in there is a large amount of nonlinear operations, this just causes its algorithm complex very high.If use likelihood ratio to represent the information transmitted between node, this algorithm is called as log-domain likelihood ratio belief propagation algorithm, is called for short log-domain BP algorithm or LLR-BP algorithm.But this algorithm decoding performance still needs to improve, and decoding complexity is higher in addition.
Summary of the invention
The present invention, in order to solve above-mentioned problems of the prior art, provides a kind of channel decoding method based on optimizing LLP-BP algorithm.
For achieving the above object, the present invention adopts following technical scheme:
Channel decoding method based on optimizing LLP-BP algorithm of the present invention, comprises the following steps:
The first step, determines likelihood ratio information
The prior probability likelihood ratio function of defined variable node is:
The variable node message process of the LLR-BP algorithm of variable optimization process on the basis of LLR-BP algorithms (9), adds a multiplicative weaken correlation factor
The advantage that the present invention has and good effect are:
The present invention, on the basis of LLR-BP algorithm, weakening correlation factor by introducing a kind of multiplicative, compensating the correlation between variable message in the processing procedure of variable information; And the check-node of LLR-BP algorithm is done simplify, reduce the complexity of LLR-BP algorithm.Channel decoding method based on optimizing LLP-BP algorithm of the present invention, gives the improvement LLR-BP interpretation method simplified based on variable node optimization and check-node, while its decoding performance of improvement, reduce decoding complexity.
Accompanying drawing explanation
Fig. 1 is function
figure;
Fig. 2 is the Performance comparision figure of method of the present invention and traditional LLR-BP algorithm;
Fig. 3 is the determination curve chart of multiplicative correlation factor;
Fig. 4 is the comparative graph of different q value.
Embodiment
Below in conjunction with the drawings and specific embodiments, the channel decoding method based on optimizing LLP-BP algorithm of the present invention is described further.Following each embodiment is not only limitation of the present invention for illustration of the present invention.
BP decoding algorithm be utilize receive the check-node of information in Tanner figure, the enterprising row iteration computing of variable node and limit thereof, collected can be utilized message by variable node, and to adjudicate.Acyclic in Tanner figure (or approximate acyclic), so when iterative decoding number of times levels off to infinite, variable node message can restrain until posterior probability.
But in actual applications, the code length of LDPC code has certain limit, and this just causes the existence having ring in Tanner figure, and the decoding performance of wherein shorter ring to LDPC code has a significant impact.Therefore, on the basis of LLR-BP algorithm, weakening correlation factor by introducing a kind of multiplicative, compensating the correlation between variable message in the processing procedure of variable information.
In LLR-BP algorithm, the computational complexity of each verification (variable) node during in order to reduce decoding, for the check information renewal process that there is index, logarithm and multiplying, has larger optimization space.Therefore doing the check-node of LLR-BP algorithm and simplify, is the effective ways reducing LLR-BP algorithm complex.
Channel decoding method based on optimizing LLP-BP algorithm of the present invention, comprises the following steps:
The first step, determines likelihood ratio information
Wherein, multiplicative weaken relevant because of
son needs to be determined by emulation experiment.The computational complexity of this algorithm does not increase too much computing, only adds multiplication operation when an iteration.
Second step: initialization likelihood ratio information
Wherein, subscript (
l), (
l-1) iterations is represented.
In LLR-BP algorithm, the computational complexity of each verification (variable) node during in order to reduce decoding, for the check information renewal process that there is index, logarithm and multiplying, has larger optimization space.Therefore doing the check-node of LLR-BP algorithm and simplify, is the effective ways reducing LLR-BP algorithm complex.
Right
on the basis of the analysis of Functional Quality, further simplification is done to the renewal process of check information:
When the renewal of calculation check message, minimum value and the estimation of sub-minimum to authentic communication of all message absolute values from variable node have the greatest impact.Be directed to the parameter in this method for simplifying
determination, choose
.For choosing of the q value in method for simplifying, the comparison of LLR-BP decoding performance time in Fig. 4 for getting different q value.
Experiment proves that algorithm improvement effect is as follows:
Under AWGN (Additive White Gaussian Noise) channel circumstance, by BPSK (Binary Phase Shift Keying) modulation system, simulation analysis is carried out to the improvement LLR-BP algorithm of irregular QC-LDPC code.Wherein, the row of parity check matrix H is heavily 5, and column weight is 10; Code length
bit, code check
decoding maximum iteration time is 50, and frame number is 5.
Fig. 2 is simulation result, as seen from the figure,
time, decoding performance about improves 0.3dB.Introduce the multiplicative decorrelation factor, reduce the correlation between short cyclic variable message, innovatory algorithm is not when increasing considerably computation complexity simultaneously, improves the decoding performance of algorithm.Wherein when signal to noise ratio is 2dB, the performance boost of innovatory algorithm is the most obvious.Average through test of many times, the determination of this correlation factor we can determine from Fig. 2, when
time the error rate (BER) performance best.
In the algorithm,
value can produce certain impact to the BER of transmission of information bit between variable.In Fig. 3, when comparatively low signal-to-noise ratio (when signal to noise ratio is less than 1.5dB in figure), less on the impact of BER; But, when signal to noise ratio is greater than the situation of certain value (when signal to noise ratio is at 1.8 ~ 2.5dB in figure),
value facilitates the convergence of decode procedure, significantly reduces BER, further increases the performance of LLR-BP decoding algorithm.
Emulation experiment adopts irregular QC-LDPC code by BPSK modulation system in awgn channel, and wherein, the row of parity check matrix H is heavily 5, and column weight is 10; Code length
code check
decoding maximum iteration time is 50, and frame number is 5.Shown by the emulation experiment of Fig. 4, select 3 almost consistent with former traditional algorithm with 4 variable nodes, select the situation of 2 variable nodes and traditional algorithm to have a certain distance, but its performance is more excellent, therefore elects q as definite value 3.
The computational complexity of this algorithm reduces to some extent relative to LLR-BP algorithm, and decoding performance is but almost consistent with LLR-BP algorithm.Improve LLR-BP algorithm also more less than the program used time of LLR-BP algorithm.
Claims (1)
1., based on the channel decoding method optimizing LLP-BP algorithm, it is characterized in that the method comprises the following steps:
The first step, determines likelihood ratio information
The prior probability likelihood ratio function of defined variable node is:
The variable node message process of the LLR-BP algorithm of variable optimization process on the basis of LLR-BP algorithms (9), adds a multiplicative weaken correlation factor
Second step: initialization likelihood ratio information
5th step: decoding is adjudicated
Last in iterative processing, recalculate the authentic communication of each variable node:
Decoding completes, and stops iteration and output codons; Otherwise continue, until maximum iteration time.
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| CN111866753A (en) * | 2020-06-02 | 2020-10-30 | 中山大学 | Digital transmission broadcast communication method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040243917A1 (en) * | 2003-05-26 | 2004-12-02 | Samsung Electronics Co., Ltd. | Apparatus and method for decoding a low density parity check code in a communication system |
| JP2006060695A (en) * | 2004-08-23 | 2006-03-02 | Science Univ Of Tokyo | Information decoding and encoding method,information communication method, information decoding device, transmitting device, and information communication system |
| US20080168333A1 (en) * | 2007-01-05 | 2008-07-10 | Yamamoto Makiko | Decoding method and decoding apparatus as well as program |
| CN101488759A (en) * | 2009-02-24 | 2009-07-22 | 东南大学 | Decoding method for MIMO OFDM system low density correcting code |
| CN101557232A (en) * | 2008-04-08 | 2009-10-14 | 威望科技(苏州)有限公司 | Decoding method of low density parity check codes |
-
2014
- 2014-10-23 CN CN201410569326.6A patent/CN104393875A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040243917A1 (en) * | 2003-05-26 | 2004-12-02 | Samsung Electronics Co., Ltd. | Apparatus and method for decoding a low density parity check code in a communication system |
| JP2006060695A (en) * | 2004-08-23 | 2006-03-02 | Science Univ Of Tokyo | Information decoding and encoding method,information communication method, information decoding device, transmitting device, and information communication system |
| US20080168333A1 (en) * | 2007-01-05 | 2008-07-10 | Yamamoto Makiko | Decoding method and decoding apparatus as well as program |
| CN101557232A (en) * | 2008-04-08 | 2009-10-14 | 威望科技(苏州)有限公司 | Decoding method of low density parity check codes |
| CN101488759A (en) * | 2009-02-24 | 2009-07-22 | 东南大学 | Decoding method for MIMO OFDM system low density correcting code |
Non-Patent Citations (1)
| Title |
|---|
| 邵烽: "基于OFDM系统的LDPC编解码研究", 《万方企业知识服务平台》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111866753A (en) * | 2020-06-02 | 2020-10-30 | 中山大学 | Digital transmission broadcast communication method and system |
| CN111866753B (en) * | 2020-06-02 | 2021-06-29 | 中山大学 | Digital transmission broadcast communication method and system |
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