CN106533453B - Decoding method and decoder - Google Patents
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Abstract
The invention discloses a decoding method and a decoder, wherein the decoding method comprises the following steps: receiving a convolutional code of a service; carrying out de-duplication processing on the generator polynomial of the convolutional code to obtain a reduced convolutional code; and decoding the reduced convolutional code. The invention improves the decoding algorithm by adopting a reduced order mode, not only reduces the calculation of the accumulative measurement, but also reduces the circulating times of the algorithm, more importantly, reduces the complexity of the decoding operation and improves the decoding efficiency.
Description
Technical Field
The present invention relates to the field of mobile communications, and in particular, to a decoding method and a decoder.
Background
In a communication system, various voice and data services all use convolutional coding in the coding process, and when a receiving end decodes the voice and data services, the receiving end usually selects viterbi software to decode.
Principle of viterbi software transcoding: for an information sequence of length kL, the possible number of corresponding code sequences is about 2KLAnd (4) respectively. When L is large, this will be an astronomical number. Therefore, it is difficult to find an information sequence with the smallest path metric with respect to the received sequence among such many possible code sequences. The viterbi algorithm is just one algorithm introduced in solving this difficulty, and the basic idea of the algorithm is that it is not a one-time comparison 2 on the trellis diagramKLThe strip path (sequence) is instead a segment received, a comparison segment is calculated, and a most likely segment (branch) of the code is selected so that the entire code sequence is a sequence with the maximum likelihood function.
Trellis of convolutional codek(N-1)Number of branches leaving or reaching each state being 2KOne (which corresponds to the number of "states" of the signal to be encoded input). For simplicity, we discuss the case where k is 1, starting from the all 0 state (state 0) starting point. In the viterbi algorithm, the path metric values of two paths converging onto each node are compared, and then the path with the smaller path metric (euclidean distance) is saved (this path is called the "survivor path"), while the other path is discarded and the corresponding path metric value is saved. Since each node leads out two branches, the extension of the path in each stage after the N-1 stage is doubled, butAfter comparison and selection, half of the paths are discarded, and the total number of paths remaining as a result remains constant (equal to 2)(N-1)I.e., the number of states of the encoder). It can be seen that the basic operation in the decoding process is "add-compare-select" (ACS), i.e., each stage finds the accumulated value of the path metrics, then compares two by two and makes a selection. Sometimes, two accumulated path metric values are equal, and one of the two accumulated path metric values is selected as a "survivor path". This is the basic idea of viterbi software transcoding. Path metrics are often referred to as cumulative metrics, while branch metrics are referred to as branch metrics.
There are two different methods for the storage and handling of survivor paths, one is called the register exchange method and one is called the "Traceback" method. Because of more coding states and high-speed decoding, the GSM adopts a decoding strategy of a traceback method:
(1) at each time T, calculating the soft decision distance between the received signal and the state value of each branch, and taking the soft decision distance as the branch measurement of the branch;
(2) at the time T +1, adding the branch metric value entering a certain state and the accumulated metric value of the corresponding state at the previous time to calculate a new accumulated metric value;
(3) comparing;
(4) selecting and storing the maximum cumulative metric, and storing a branch judgment value corresponding to the maximum cumulative metric as a survival path judgment value of a corresponding state;
(5) if the length of the non-retrieval is more than 2-3 times of the decoding depth, retrieving to obtain the survival path; or when L is not large, the retrieval can be started after the whole code segment is processed;
(6) if the decoding depth is not more than 2-3 times, increasing T by 1 and returning to (1);
(7) and (4) reversely ordering the survivor paths obtained by the retrieval and outputting the survivor paths.
As can be seen, since there are many encoding states, the amount of processing is very large when decoding is performed according to protocol encoding, and thus the conventional decoding scheme has a problem of low decoding efficiency.
Disclosure of Invention
The invention provides a decoding method and a decoder, which are used for solving the problem of low decoding efficiency of a decoding mode in the prior art.
According to an aspect of the present invention, there is provided a decoding method, comprising:
receiving a convolutional code of a service;
carrying out de-duplication processing on the generator polynomial of the convolutional code to obtain a reduced convolutional code;
and decoding the reduced convolutional code.
Optionally, in the method of the present invention, the performing deduplication processing on the generator polynomial of the convolutional code includes: and merging the same polynomials in the generator polynomials of the convolutional codes to realize the de-duplication processing.
Optionally, in the method of the present invention, after merging the same polynomials in the generator polynomials of the convolutional code, the method further includes: and carrying out quantization processing on the combined result according to the dimension of the original polynomial.
Optionally, in the method of the present invention, the performing deduplication processing on the generator polynomial of the convolutional code includes: comparing the puncturing positions of the same polynomial in the generating polynomials of the convolutional code, and realizing the duplicate removal processing by only reserving the polynomial with the least puncturing positions.
Optionally, in the method of the present invention, the reduced convolutional code is subjected to viterbi decoding.
According to another aspect of the present invention, there is provided a decoder comprising:
a receiving module, configured to receive a convolutional code of a service;
the order reduction module is used for carrying out duplication removal processing on the generator polynomial of the convolutional code to obtain the order reduced convolutional code;
and the decoding module is used for decoding the reduced convolutional codes.
Optionally, in the decoder of the present invention, the order reduction module is specifically configured to combine the same polynomials in the generator polynomials of the convolutional codes, so as to implement deduplication processing.
Optionally, in the decoder of the present invention, the order reduction module is further configured to perform quantization processing on the combined result according to a dimension of the original polynomial.
Optionally, in the decoder of the present invention, the order reduction module is specifically configured to compare puncturing positions of the same polynomial in the generator polynomial of the convolutional code, and implement deduplication processing by only preserving the polynomial with the least puncturing positions.
Optionally, in the decoder of the present invention, the decoding module is specifically configured to decode the reduced convolutional code by using viterbi software decoding.
The invention has the following beneficial effects:
the invention improves the decoding algorithm by adopting a reduced order mode, not only reduces the calculation of the accumulative measurement, but also reduces the circulating times of the algorithm, more importantly, reduces the complexity of the decoding operation and improves the decoding efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a decoding method according to an embodiment of the present invention;
FIG. 2 is a comparison graph of simulation performance before and after order reduction in an application example of the present invention;
FIG. 3 is a comparison graph of simulation performance before and after soft decoding order reduction of TU3 channel;
FIG. 4 is a graph comparing simulation performance before and after soft decoding order reduction of TU50 interference channel;
FIG. 5 is a comparison graph of performance before and after soft decoding order reduction in TU50 environment;
FIG. 6 is a comparison graph of performance before and after soft decoding order reduction in TU1.5 environment;
fig. 7 is a block diagram of a decoder according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
An embodiment of the present invention provides a decoding method, as shown in fig. 1, including the following steps:
step S101, receiving a convolution code of a service;
step S102, carrying out duplication elimination processing on the generator polynomial of the convolutional code to obtain a reduced-order convolutional code;
in this step, the manner of performing deduplication processing on the generator polynomial of the convolutional code includes, but is not limited to, the following manner:
the first method is as follows: merging the same polynomials in the generator polynomials of the convolutional codes; wherein merging refers to simple accumulation;
the second method comprises the following steps: merging the same polynomials in the generator polynomials of the convolutional codes, and quantizing the merged results according to the dimension of the original polynomial, that is, keeping consistent with the dimension of the original polynomial, for example: if the 2 polynomials are combined and added, the result needs to be divided by 2, and the dimensional change caused by the addition is eliminated;
the third method comprises the following steps: comparing the puncturing positions of the same polynomial in the generating polynomials of the convolutional code, and realizing the duplicate removal processing by only reserving the polynomial with the least puncturing positions. The implementation principle of the deduplication mode is as follows: after the convolutional codes are encoded, in order to meet the requirement of the channel coding standard length, some processing called puncturing is performed on the encoded data. For each polynomial, the punctured data bits and the number are different, and therefore, for the receiving end, the punctured data needs to be recovered for decoding, and the common practice is zero padding. If a polynomial is punctured more data, its confidence is lower, and conversely, we want to keep the polynomial with the least punctured bits, the confidence is higher.
Step S103, decoding the reduced convolutional code.
In this embodiment, the reduced convolutional code is preferably subjected to viterbi decoding.
In summary, it can be seen that the present invention provides a decoding optimization scheme, which considers that the polynomial generators of convolutional codes used in multiple services are all of order 5 or more, finds out repetitive polynomials according to the structural characteristics of the polynomial generators, and performs merging or selection processing to implement order reduction processing on convolutional codes and decode the reduced convolutional codes, and this scheme for decoding the reduced convolutional codes saves resources and computational complexity and improves decoding speed.
In order to more clearly illustrate the present invention, a detailed description of a specific embodiment process of the present invention is provided below by way of a specific application example.
The application example takes (5, 1, 6) convolutional code of service AFS4.75 as an example, and (5, 1, 6) decoding can be reduced to (3, 1, 6) for processing, and other services have similar convolutional coding characteristics, which can also be simulated, and the specific implementation manner is as follows.
Referring to the GSM protocol 45003, the convolutional code generator polynomial of AFS4.75 is as follows:
G4/G6＝1+D2+D3+D5+D6/1+D+D2+D3+D4+D6
G4/G6＝1+D2+D3+D5+D6/1+D+D2+D3+D4+D6
G5/G6＝1+D+D4+D6/1+D+D2+D3+D4+D6
G6/G6＝1
G6/G6＝1
it can be seen that: the 1 st and 2 nd bit generator polynomials are the same, as are the 4 th and 5 th bits. The remaining bits are combined to form the (3, 1, 6) convolutional code, except the same bits.
Therefore, it is considered that in the decoding process, the (5, 1, 6) software decoding is reduced to (3, 1, 6) decoding. The invention obtains three implementation methods:
the 1, 2 bits are merged, and the 4, 5 bits are merged, so that the decoding becomes (3, 1, 6).
And (II) considering that the soft information output by demodulation is quantized, on the basis of the step (I), the result after combination is quantized according to the dimension of the original polynomial.
(III) the 1 st and 2 nd sites are repeated, the 4 th and 5 th sites are also repeated, and for the 1 st and 2 nd sites, the hole sites of the two sites are compared, and one site with fewer hole sites is reserved; the same process is carried out for 4 and 5 bits in the same way. This discards 2 bits and constitutes (3, 1, 6) decoding.
For the method of the reduced-order decoding processing, performance verification is carried out on a simulation platform by the method and the previous (5, 1, 6) decoding. Method (three) removes the information's significances directly, which results in a sensitivity loss of approximately 2.5dB compared to (5, 1, 6) decoding, and suggests that this is preferably not used in the order reduction process, see fig. 2.
Fig. 3 is a simulation result of a GSM900 single antenna TU3 channel, and it can be seen that the sensitivity of (5, 1, 6) decoding is completely matched with that of (one); compared with the second phase, the phase difference is only about 0.1 dB.
Fig. 4 is a simulation result under GSM900 single antenna TU50 co-channel interference, and it can be seen that the performance under interference of (5, 1, 6) decoding is about 0.2dB different from that of (one); compared with the second method, the method is almost completely consistent.
As described above, the simulated performance is very close to the original (5, 1, 6) decoding no matter whether the (one) or the (two) is adopted, so that the method of reducing (5, 1, 6) to (3, 1, 6) is feasible. We need to verify the two methods in the actual test environment, and choose an optimal scheme to use. The measured data in the environment are as follows:
TABLE 1 TU50 data
| (FER％) | Method gain (db) | Method gain (db) |
| 40 | -0.25 | -0.27 |
| 33 | 0 | 0 |
| 16 | -0.25 | -0.5 |
| 10 | 0 | -0.15 |
| 5 | 0.1 | 0 |
TABLE 2 TU1.5 data
| (FER％) | Method gain (db) | Method gain (db) |
| 16 | -0.25 | -0.5 |
| 12 | -0.1 | -0.25 |
| 8 | -0.2 | -0.7 |
| 7 | 0 | -0.4 |
| 4 | -0.5 | -1 |
| 3 | -0.4 | -0.6 |
| 2 | -0.2 | -0.4 |
Referring to fig. 5 and fig. 6, combining the above two tables, it can be seen that the method (i) has the closest performance to the original (5, 1, 6) decoding algorithm in the actual measurement. We therefore chose to, before entering viterbi decoding: directly merging the 1 st and 2 nd bits; the 4, 5 bits are combined and (3, 1, 6) soft decoding is performed.
It can be seen that there are multiple resets as long as the higher order convolutional code generator polynomial exists. The decoding algorithm can be improved by adopting the order reduction method, and the decoding algorithm can be adopted without being limited to the GSM system after the performance after the order reduction is verified to be not lost. By adopting the improved soft decoding algorithm, not only the calculation of the accumulative metric is reduced, but also the number of times of algorithm circulation is reduced. Most importantly, the complexity of decoding of the receiver is reduced.
Example two
An embodiment of the present invention provides a decoder, as shown in fig. 7, including:
a receiving module 710, configured to receive a convolutional code of a service;
a reduced order module 720, configured to perform deduplication processing on the generator polynomial of the convolutional code to obtain a reduced order convolutional code;
and a decoding module 730, configured to decode the reduced convolutional code.
In this embodiment, the manner for implementing the deduplication processing on the generator polynomial of the convolutional code by the order reduction module 720 includes:
the first method is as follows: and combining the same polynomials in the generator polynomials of the convolutional codes.
The second method comprises the following steps: and combining the same polynomials in the generator polynomials of the convolutional codes, and quantizing the combined result according to the dimension of the original polynomials.
The third method comprises the following steps: comparing the puncturing positions of the same polynomial in the generating polynomials of the convolutional code, and realizing the duplicate removal processing by only reserving the polynomial with the least puncturing positions.
Further, in this embodiment, the decoding module 730 is specifically configured to decode the reduced convolutional code by using viterbi software decoding.
The decoder of the embodiment of the invention adopts a reduced order mode to improve the decoding algorithm, thereby not only reducing the calculation of the accumulative metric, but also reducing the circulating times of the algorithm, more importantly reducing the complexity of the decoding operation and improving the decoding efficiency.
Although the present application has been described with reference to embodiments, those skilled in the art will appreciate that there are numerous variations and permutations of the present application without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (6)
1. A decoding method, comprising:
receiving a convolutional code of a service;
carrying out de-duplication processing on the generator polynomial of the convolutional code to obtain a reduced convolutional code;
decoding the reduced convolutional code;
the performing the deduplication processing on the generator polynomial of the convolutional code includes:
merging the same polynomials in the generator polynomials of the convolutional codes to realize the duplicate removal;
or
Comparing the puncturing positions of the same polynomial in the generating polynomials of the convolutional code, and realizing the duplicate removal processing by only reserving the polynomial with the least puncturing positions.
2. The method of claim 1, wherein after combining the same polynomial of the generator polynomials of the convolutional code, further comprising: and carrying out quantization processing on the combined result according to the dimension of the original polynomial.
3. The method of any of claims 1-2, wherein the reduced convolutional code is subjected to viterbi decoding.
4. A decoder, comprising:
a receiving module, configured to receive a convolutional code of a service;
the order reduction module is used for carrying out duplication removal processing on the generator polynomial of the convolutional code to obtain the order reduced convolutional code;
the decoding module is used for decoding the reduced convolutional codes;
the order reduction module is specifically configured to combine identical polynomials in the generator polynomial of the convolutional code to implement deduplication processing;
or, in particular, the puncturing positions of the same polynomial in the generator polynomial of the convolutional code are compared, and the deduplication processing is implemented by only preserving the polynomial with the least puncturing positions.
5. The decoder of claim 4, wherein the order reduction module is further configured to quantize the combined result according to the dimension of the original polynomial.
6. The decoder according to any of claims 4 to 5, wherein the decoding module is configured to decode the reduced convolutional code using viterbi software decoding.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07147546A (en) * | 1993-11-22 | 1995-06-06 | Nec Corp | Viterbi decoder |
| CN1333599A (en) * | 2001-08-28 | 2002-01-30 | 杨大成 | Decoding method and decoder realizing same |
| WO2005015750A1 (en) * | 2003-07-15 | 2005-02-17 | Thomson Licensing | Re-configurable viterbi detector for partial response signals |
| CN101237239A (en) * | 2008-03-03 | 2008-08-06 | 黄知涛 | A blind recognition method for deleting cirrocumulus code of (n-1)/n code rate |
| CN101667840A (en) * | 2009-09-08 | 2010-03-10 | 华为技术有限公司 | Method and device for tail biting decoding |
| CN103401650A (en) * | 2013-08-08 | 2013-11-20 | 山东大学 | Blind identification method for (n, 1 and m) convolutional code with error codes |
| CN103427850A (en) * | 2012-05-24 | 2013-12-04 | 中兴通讯股份有限公司 | Multi-mode viterbi decoding apparatus and decoding method thereof |
| CN104243095A (en) * | 2014-09-11 | 2014-12-24 | 山东大学 | Code word type blind identification method for convolutional code and linear block code |
| CN104467875A (en) * | 2014-12-09 | 2015-03-25 | 山东大学 | Blind recognition method for RS code and punctured convolutional code cascaded code parameters |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW569549B (en) * | 2002-10-25 | 2004-01-01 | Benq Corp | Branch metric generator for Viterbi decoder |
-
2015
- 2015-09-15 CN CN201510586989.3A patent/CN106533453B/en active Active
- 2015-12-25 WO PCT/CN2015/098959 patent/WO2016165395A1/en active Application Filing
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07147546A (en) * | 1993-11-22 | 1995-06-06 | Nec Corp | Viterbi decoder |
| CN1333599A (en) * | 2001-08-28 | 2002-01-30 | 杨大成 | Decoding method and decoder realizing same |
| WO2005015750A1 (en) * | 2003-07-15 | 2005-02-17 | Thomson Licensing | Re-configurable viterbi detector for partial response signals |
| CN101237239A (en) * | 2008-03-03 | 2008-08-06 | 黄知涛 | A blind recognition method for deleting cirrocumulus code of (n-1)/n code rate |
| CN101667840A (en) * | 2009-09-08 | 2010-03-10 | 华为技术有限公司 | Method and device for tail biting decoding |
| CN103427850A (en) * | 2012-05-24 | 2013-12-04 | 中兴通讯股份有限公司 | Multi-mode viterbi decoding apparatus and decoding method thereof |
| CN103401650A (en) * | 2013-08-08 | 2013-11-20 | 山东大学 | Blind identification method for (n, 1 and m) convolutional code with error codes |
| CN104243095A (en) * | 2014-09-11 | 2014-12-24 | 山东大学 | Code word type blind identification method for convolutional code and linear block code |
| CN104467875A (en) * | 2014-12-09 | 2015-03-25 | 山东大学 | Blind recognition method for RS code and punctured convolutional code cascaded code parameters |
Non-Patent Citations (1)
| Title |
|---|
| "RS码编译码及其快速实现";罗开杰;《中国优秀硕士学位论文全文数据库•信息科技辑》;20020215;第2002年卷(第1期);I136-43 * |
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