CN112260698A - Dynamic correction factor configuration method in LDPC decoder - Google Patents
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Abstract
The invention provides a method for configuring dynamic correction factors in an LDPC decoder, which is characterized by comprising the following steps: reading the code rate of the current signal, and decoding by using an LDPC decoder; reading LLR information waiting for processing; the iteration times, the signal-to-noise ratio value and the correction factor value are positively correlated, and the iteration times and the code rate are used for configuring a correction factor value configuration table; reading in a correction factor value table, and looking up the correction factor values corresponding to the read code rate and the iteration times; and calculating the information amplitude of the check bit according to the dynamically configured correction factor value, and replacing the previously used fixed alpha value by configuring a more reasonable alpha value according to different code rates and iteration times by using the dynamic correction factor, so that the decoding capability of the LDPC decoder can be effectively improved, and the working performance of the whole receiving system is obviously improved.
Description
Technical Field
The invention relates to the field of LDPC decoding, in particular to a dynamic parameter configuration method for improving the performance of an LDPC decoder.
Background
The LDPC code is a full-name low-density parity check code, has the advantages of good performance, simple description, low decoding complexity, parallel realization and the like, and is widely applied to high-definition digital video standards and systems such as ATSC3.0, DVB-T and the like at present.
Decoding methods of LDPC codes can be divided into two broad categories: (1) the decoding method of the LDPC code based on hard decision and (2) the decoding method of the LDPC code based on soft decision are provided, wherein, the soft decision decoding uses posterior probability information, and the performance of the LDPC code approaches to the Shannon limit through iterative operation. In the decoding process, each iteration process comprises two steps: check node processing and variable node processing. All check nodes receive the messages from the adjacent variable nodes, the messages are transmitted back to the adjacent variable nodes after being processed, then all the variable nodes carry out the same process, and finally all the available messages are collected by the variable nodes to carry out judgment.
As is known, the transmission process and the reception process of the ATSC3.0 system mainly include the following steps:
on the transmitting side, each LDPC codeword is modulated and the like, and then transmitted to a channel through an antenna. After passing through a wireless channel, for the receiving end side, the attenuated signal is received and subjected to synchronization, equalization, descrambling and other processing, then, constellation diagram mapping is performed to obtain LLR data (log-likelihood ratio) corresponding to each codeword block, and the LDPC decoder corrects a certain number of error bits in the codeword by using the LLR data through a series of calculations and processing, and outputs a correct codeword.
For binary LDPC codes, the messages may be represented in the form of LLRs (log-likelihood ratios), and the corresponding decoding algorithm is referred to as LLR decoding.
The formula for processing the variable node message is as follows:
if the probability message is represented by likelihood ratio, after a series of transformations, equation 1 above can be expressed as equation 2:
L(qij)＝L(Pi)+ΠL(rji)
(formula 2)
Wherein, PiFor channel initiation messages, qijMessages passed to check nodes for variable nodes, rjiMessages passed to variable nodes for check nodes, L (P)i) Representing message data in LLR form. It can be seen that the multiplication operation of formula 1 in the conventional belief propagation probability is changed into the addition operation of formula 2, which has the advantage of reducing the complexity of the operation.
In the ATSC3.0 prototype, to further optimize performance, one skilled in the art typically uses a Normalized confidence (Normalized BP-based) algorithm. In the current ATSC3.0 receiver algorithm platform, the Normalized confidence (Normalized BP-based) algorithm was improved, with the check node message updates represented in the form of symbols and amplitudes, the symbols being used to make decoding decisions, and the confidence of the decisions represented by the amplitudes. The improved algorithm is characterized in that in amplitude calculation, only the minimum value and the second minimum value which have the largest influence on the result are selected. This has two advantages: (1) the check node only needs to calculate the minimum value, and the calculation process is simplified; (2) the check node only needs to store the minimum value and the second minimum value transmitted by the bit node, so that the storage space is saved. However, the problem with this improved algorithm is that the magnitude of the output verification information is overestimated compared to the original confidence algorithm.
In the conventional LLR BP (belief) algorithm, when the nth iteration is performed, the message processing of the check node can be expressed as:
in the Normalized BP-based algorithm used in ATSC prototypes, the processing of check nodes can be expressed as:
L(rij)＝Πsgn(L(qij))*min(|L(qij)|)
(formula 4)
Here, the operation output result of the above equation 3 is denoted by "L1", and the operation output result of the above equation 4 is denoted by "L2". We can conclude that:
(1) l1 and L2 have the same sign, that is, sgn (L1) ═ sgn (L2).
(2) The magnitude of L2 is greater than the magnitude of L1, i.e. | L2| > | L1|.
The solution is to multiply a correction factor alpha smaller than 1, so that the LLR BP algorithm can be more approximate to the LLR BP algorithm, and the decoding performance is improved. We calculate the correction factor alpha by calculating the mean of | L1|, | L2|, i.e.:
in the current receiver algorithm platform, the processing procedure of the check node is realized as follows:
L(rij)＝alpha*Πsgn(L(qij))*min(|L(qij)|)
(formula 6)
As shown in formula 6, on the basis of formula 4, the information transmitted by the check node can be correctly calculated by multiplying the processing procedure of the check node of formula 4 by the alpha value.
The analysis shows that the performance of the Normalized BP-based algorithm is directly related to the value of alpha, and the performance of the Normalized BP-based algorithm can be close to or even exceed that of the algorithm by only selecting proper alpha and normalizing the minimum confidence algorithm.
Disclosure of Invention
The invention relates to a method for configuring dynamic correction factors in an LDPC decoder of an ATSC3.0 receiving system, which improves the performance of the LDPC decoder, and can effectively improve the decoding capability of the LDPC decoder by configuring more reasonable alpha values according to different code rates to replace the previously used fixed alpha values, thereby obviously improving the working performance of the whole receiving system.
The invention provides a method for configuring dynamic correction factors in an LDPC decoder, which is characterized in that: reading the code rate of the current signal, and decoding by using an LDPC decoder; reading LLR information waiting for processing; the iteration times, the signal-to-noise ratio value and the correction factor value are positively correlated, and the iteration times and the code rate are used for configuring a correction factor value configuration table; reading in a correction factor value table, and looking up the correction factor values corresponding to the read code rate and the iteration times; and calculating the information amplitude of the check bit according to the dynamically configured correction factor value.
In the method for configuring dynamic correction factors in an LDPC decoder provided by the present invention, further, the method may further have a feature that information transmitted by a check node is calculated by the following check node processing formula:
L(rij)＝alpha*Πsgn(L(qij))*min(|L(qij)|)
wherein alpha is a correction factor value less than 1, qijMessages passed to check nodes for variable nodes, rjiMessages passed to variable nodes for check nodes, L (q)ij) Representing the messages passed to the check nodes by the variable nodes in the form of LLRs.
In the method for configuring the dynamic correction factor in the LDPC decoder provided by the invention, further, the method can be characterized in that a receiver algorithm platform adopts a normalized confidence algorithm, the updating of the check node information is represented as a symbol and an amplitude, wherein the symbol is used for decoding judgment, and the confidence of the judgment is represented by the amplitude.
In the method for configuring the dynamic correction factor in the LDPC decoder provided by the invention, further, the method can be characterized in that in amplitude calculation, the minimum value and the second minimum value which have the largest influence on the result are selected.
In the method for configuring dynamic correction factors in the LDPC decoder provided by the invention, further, the method can also have the characteristics that,
in the normalized confidence algorithm, when the nth iteration operation is performed, the message processing formula of the check node is expressed as:
the check node processing formula is expressed as:
L(rij)＝Πsgn(L(qij))*min(|L(qij)|)
wherein q isijMessages passed to check nodes for variable nodes, rjiMessages passed to variable nodes for check nodes, L (q)ij) Representing the messages passed to the check nodes by the variable nodes in the form of LLRs.
In the method for configuring the dynamic correction factor in the LDPC decoder provided by the present invention, further, the method may further have a feature that a message processing formula of the check node obtains a first result, and a check node processing formula obtains a second result.
In the method for configuring dynamic correction factors in an LDPC decoder provided by the present invention, further, the method may further have a feature that the first result and the second result have the same sign, and the amplitude of the second result is greater than the amplitude of the first result.
In the method for configuring dynamic correction factors in an LDPC decoder provided by the present invention, further, the method may further have a feature that the correction factor value alpha is calculated by calculating a mean of the first result and the second result, that is:
in the method for configuring the dynamic correction factor in the LDPC decoder provided by the invention, the method further has the characteristic that the iteration times, the SNR numerical value and the corresponding alpha value in the LDPC decoder are positively correlated, and the dynamic alpha value is configured according to the code rate and the iteration times on a receiver software algorithm simulation platform.
In the method for configuring dynamic correction factors in an LDPC decoder provided by the present invention, further, the method may further have a characteristic that a correction factor value configuration table of correction factor values configured according to a code rate and an iteration number includes:
action and Effect of the invention
In the prior art, a fixed alpha value can be calculated according to Monte Carlo simulation and can also be calculated by using a related probability theoretical formula, in the method for configuring the dynamic correction factors in the LDPC decoder, the dynamic correction factors, namely the values and the code rates of alpha and SNR (signal to noise ratio) are related, and the more reasonable alpha values are configured according to different code rates and/or iteration times to replace the previously used fixed alpha values, so that the decoding capability of the LDPC decoder can be effectively improved, and the working performance of the whole receiving system is remarkably improved.
Drawings
The above and other features, properties and advantages of the present invention will become more apparent from the following description of embodiments thereof, taken in conjunction with the accompanying drawings, in which,
FIG. 1 is a flow chart of dynamic alpha configuration in an embodiment of the present invention.
Detailed Description
[ example one ]
The invention relates to a method for configuring dynamic correction factors in an LDPC decoder of an ATSC3.0 receiving system, which comprises the following steps:
step S1, reading the code rate of the current signal, and decoding by using an LDPC decoder;
step S2, reading LLR information waiting for processing;
step S3, the iteration times, the signal-to-noise ratio value and the correction factor value are positively correlated, and the iteration times and the code rate are used for configuring a correction factor value configuration table;
step S4, reading in a correction factor value table, and looking up the correction factor values corresponding to the read code rate and iteration times; and
and step S5, calculating the information amplitude of the check bit according to the dynamically configured correction factor value.
The information transmitted by the check nodes is calculated through the following check node processing formula: l (r)ij)＝alpha*Πsgn(L(qij))*min(|L(qij)|)
Wherein alpha is a correction factor value less than 1, qijMessages passed to check nodes for variable nodes, rjiMessages passed to variable nodes for check nodes, L (q)ij) Representing the messages passed to the check nodes by the variable nodes in the form of LLRs.
The receiver algorithm platform adopts a normalized confidence algorithm, and the updating of the check node information is represented as a symbol and an amplitude, wherein the symbol is used for decoding judgment, and the confidence of the judgment is represented by the amplitude. In the amplitude calculation, the minimum value and the next minimum value that most affect the result are selected.
In the normalized confidence algorithm, when the nth iteration operation is performed, the message processing formula of the check node is expressed as:
the check node processing formula is expressed as:
L(rij)＝Πsgn(L(qij))*min(|L(qij)|)
wherein q isijMessages passed to check nodes for variable nodes, rjiMessages passed to variable nodes for check nodes, L (q)ij) Representing the messages passed to the check nodes by the variable nodes in the form of LLRs.
The message processing formula of the check node obtains a first result, and the check node processing formula obtains a second result. The first result and the second result have the same sign, and the magnitude of the second result is greater than the magnitude of the first result.
The correction factor value alpha is calculated by calculating the mean of the first result, the second result, i.e.:
in practical application, the source and channel environments are different, and therefore stable SNR cannot be obtained for parameter configuration. Instead, alpha configuration is performed using the number of iterations. The higher the number of iterations in the LDPC decoder, the higher the SNR value and the higher the corresponding alpha value. On an ATSC3.0 receiver software method simulation platform, a dynamic alpha value is configured according to a code rate and iteration times, and a system inquires the alpha value in a data table according to the current code rate and the iteration times for calculation.
Table 1 is a table of alpha values configured according to code rates and iteration times, and the specific configuration is as follows:
TABLE 1
In a software algorithm simulation platform, the configuration of alpha by using the code rate and the iteration times is feasible, and the performance of the system is effectively improved. However, in a hardware development platform, dynamically increasing the alpha value according to the number of iterations is quite cumbersome and difficult. Developers need to add top-level interfaces and solve the problem of troublesome timing, which greatly increases the amount of computation and is quite difficult as a whole.
[ example two ]
In order to solve the current problems and requirements of a hardware development platform, another technical scheme proposed by us is as follows: an alpha value is set for each code rate, and the value is not changed along with the iteration times and is changed along with the code rate.
Table 2 is a table of alpha values configured according to the code rate, and the specific configuration is as follows:
TABLE 2
Setting the alpha value separately according to the code rate is completely feasible and is easy to implement by hardware development. The simulation is carried out by selecting samples of several common channel environments, and the fact that the simulation samples of the variable alpha values are improved in performance by about 0.5dB compared with the simulation samples of the fixed alpha values is found.
Table 3 is a comparison table of performance enhancement of the dynamic correction factor configuration method in the LDPC decoder under different channel environments. The values in table 3 are SNR (signal to noise ratio) in dB (decibel) for stable operation of the system.
TABLE 3
As shown in FIG. 1, the dynamic alpha configuration flow is as follows:
the method for configuring the dynamic correction factor in the LDPC decoder comprises the following four steps:
s1: in the BICM module of the ATSC3.0 receiver, the system calls the LDPC decoder for decoding. In the former-stage module, the system reads the signal code rate of the current PLP (physical layer pipe).
S2: the LDPC decoder reads in code rate information and reads in LLR information to be processed.
S3: reading in an alpha table by a register, and looking up the table to obtain an alpha value corresponding to the code rate.
S4: the information amplitude of the check bits is calculated using the alpha to be substituted into a processing formula for the check nodes configured with alpha, such as formula 6 above.
In summary, in the hardware development of the current ATSC3.0 prototype, the performance is greatly improved by dynamic alpha configuration. On the premise of increasing the complexity as little as possible, the invention introduces dynamic alpha values and improves the performance of the LDPC decoder.
Claims (10)
1. A method for configuring dynamic correction factors in an LDPC decoder is characterized by comprising the following steps:
reading the code rate of the current signal, and decoding by using an LDPC decoder;
reading LLR information waiting for processing;
the iteration times, the signal-to-noise ratio value and the correction factor value are positively correlated, and the iteration times and the code rate are used for configuring a correction factor value configuration table;
reading in a correction factor value table, and looking up the correction factor values corresponding to the read code rate and the iteration times; and
and calculating the information amplitude of the check bit according to the dynamically configured correction factor value.
2. The method of dynamic correction factor configuration in an LDPC decoder according to claim 1,
and calculating the information transmitted by the check nodes by the following check node processing formula:
L(rij)＝alpha*Πsgn(L(qij))*min(|L(qij)|)
wherein alpha is a correction factor value less than 1, qijMessages passed to check nodes for variable nodes, rjiMessages passed to variable nodes for check nodes, L (q)ij) Representing the messages passed to the check nodes by the variable nodes in the form of LLRs.
3. The method of dynamic correction factor configuration in an LDPC decoder according to claim 1,
the receiver algorithm platform adopts a normalized confidence algorithm, and the updating of the check node information is represented as a symbol and an amplitude, wherein the symbol is used for decoding judgment, and the confidence of the judgment is represented by the amplitude.
4. The method of dynamic correction factor configuration in an LDPC decoder according to claim 3,
in the amplitude calculation, the minimum value and the next minimum value that most affect the result are selected.
5. The method of dynamic correction factor configuration in an LDPC decoder according to claim 3,
in the normalized confidence algorithm, when the nth iteration operation is performed, the message processing formula of the check node is expressed as:
the check node processing formula is expressed as:
L(rij)＝Πsgn(L(qij))*min(|L(qij)|)
wherein q isijMessages passed to check nodes for variable nodes, rjiMessages passed to variable nodes for check nodes, L (q)ij) Representing the messages passed to the check nodes by the variable nodes in the form of LLRs.
6. The method of dynamic correction factor configuration in an LDPC decoder according to claim 5,
the message processing formula of the check node obtains a first result, and the check node processing formula obtains a second result.
7. The method of dynamic correction factor configuration in an LDPC decoder according to claim 5,
the first result and the second result have the same sign, and the magnitude of the second result is greater than the magnitude of the first result.
8. The method of dynamic correction factor configuration in an LDPC decoder according to claim 5,
the correction factor value alpha is calculated by calculating the mean of the first result, the second result, i.e.:
9. the method of dynamic correction factor configuration in an LDPC decoder as recited in claim 1, further comprising:
the iteration times, SNR numerical values and corresponding alpha values in the LDPC decoder are positively correlated, and a dynamic alpha value is configured according to the code rate and the iteration times on a receiver software algorithm simulation platform.
10. The method of dynamic correction factor configuration in an LDPC decoder as recited in claim 1, further comprising:
a modifier value configuration table for modifier values configured according to code rates and iteration times, comprising:
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