CN113938227B - Signal-to-noise ratio strength dynamic judgment method based on iterative decoding - Google Patents
Signal-to-noise ratio strength dynamic judgment method based on iterative decoding Download PDFInfo
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Abstract
The invention discloses a signal-to-noise ratio strength judging method based on iterative decoding, which utilizes decoded data to estimate signal-to-noise ratio, firstly sets a decoding fixed iteration number, carries out similarity calculation on output sequences of two adjacent iterative decoding to obtain a group of similarity sequences, carries out signal-to-noise ratio strength statistics after judging that the sequences are effective, and finally judges the signal-to-noise ratio strength according to the statistical proportion. The invention only relates to binary exclusive OR operation and ratio calculation, and has simple realization and less resource occupation; the signal to noise ratio judgment is carried out by utilizing the decoded data, so that the reliability is high; and dynamically updating the signal to noise ratio by utilizing periodic statistics.
Description
Technical Field
The invention belongs to the technical field of communication, and relates to a signal-to-noise ratio strength dynamic judgment method based on iterative decoding, in particular to a signal-to-noise ratio strength dynamic judgment method under the condition of no verification assistance.
Background
In a communication system, signal-to-noise ratio can reflect the power strength of a useful signal and directly determine the quality of communication. The signal-to-noise ratio estimation technology is applied to the receiving equipment, so that a user can grasp the current communication quality condition in real time, communication is avoided under the condition of poor communication quality, the communication success rate is ensured, the transmitting power can be reduced in real time when the signal-to-noise ratio is too strong, and the interference to other communication equipment in the adjacent frequency band is reduced. Therefore, the research and application of the signal-to-noise ratio estimation technology have high practical value.
The signal-to-noise ratio estimation method applied at present mainly comprises the following steps: the maximum likelihood estimation, the second-order fourth-order moment estimation, the high-order cumulant estimation, the minimum mean square error estimation signal-to-noise ratio estimation algorithm and the like are generally put in front of decoding, and are operated by utilizing multi-bit quantized soft information, so that on one hand, the operation amount is large, the calculation is complex, on the other hand, the signal-to-noise ratio estimation accuracy is greatly influenced by the synchronization error, and when the synchronization error is large, the signal-to-noise ratio estimation becomes unreliable.
Disclosure of Invention
Object of the invention
The purpose of the invention is that: the signal-to-noise ratio strength dynamic judging method based on iterative decoding is provided, the operand is reduced, the calculation complexity is simplified, and the high-reliability signal-to-noise ratio is realized.
(II) technical scheme
In order to solve the technical problems, the invention provides a signal-to-noise ratio strength judging method based on iterative decoding, which utilizes decoded data to perform signal-to-noise ratio estimation, firstly sets a fixed iteration number of decoding, performs similarity calculation on two adjacent iterative decoding output sequences to obtain a group of similarity sequences, performs signal-to-noise ratio strength statistics after judging that the sequences are effective, and finally judges the signal-to-noise ratio strength according to the statistical proportion.
Defining L as the length of the decoding output sequence, N as the iteration fixed termination times (obtained through simulation), and both L and N as positive integers greater than 0.
The signal-to-noise ratio strength judging method based on iterative decoding comprises the following steps:
step one: similarity calculation is carried out on the output sequences of the adjacent iterative decoding
The similarity calculation method comprises the following steps: and carrying out bit-by-bit binary exclusive OR on the output sequences of the two iterative decoding and then summing, wherein the obtained result is a similarity value of the two iterative decoding outputs, the value range of the similarity value is more than or equal to 0 and less than or equal to L, and the smaller the value is, the higher the similarity is.
Step two: constructing a similarity sequence by using the calculated similarity value, and judging the effectiveness
And when the similarity sequence is constructed, repeating the first step until the iteration times reach N, obtaining N-1 similarity values, and recording the N-1 similarity values as an S array, wherein S= [ S1, S2, and SN-1].
When the validity judgment is carried out on the S array, if any one of the first condition and the second condition is met, the valid times are added with 1, and the value of N-P is recorded:
condition one: the positive integer P,1 < P < N-1, S1 > S2 >, > SP-1 > sp=sp+1=, where SN-1 holds;
condition II: there is a positive integer P, p=1, with sp=sp+1=.a.. The term "=sn-1 holds.
Step three: signal to noise ratio strength statistics
And obtaining high signal-to-noise ratio statistics times, low signal-to-noise ratio statistics times and medium signal-to-noise ratio statistics times according to a preset signal-to-noise ratio threshold value, and respectively calculating to obtain a high signal-to-noise ratio statistics proportion, a low signal-to-noise ratio statistics proportion and a medium signal-to-noise ratio statistics proportion.
The obtaining process of the high signal-to-noise ratio statistics times, the low signal-to-noise ratio statistics times and the medium signal-to-noise ratio statistics times comprises the following steps:
two thresholds are set: the signal-to-noise ratio high threshold TH and the signal-to-noise ratio low threshold TL are positive integers and satisfy N > TH > TL > 0. Comparing N-P with TH and TL:
if N-P is larger than or equal to TH, representing that the signal to noise ratio of the received signal is strong, adding 1 to the statistics frequency KH of the high signal to noise ratio;
If N-P is smaller than TL, the signal to noise ratio of the received signal is weak, and the low signal to noise ratio statistics times KL is added with 1;
if TL is less than or equal to N-P and less than TH, representing that the signal to noise ratio of the received signal is medium, adding 1 to the statistics number KM of the medium signal to noise ratio.
The process of calculating the high signal-to-noise ratio statistical proportion, the low signal-to-noise ratio statistical proportion and the medium signal-to-noise ratio statistical proportion is as follows: and repeatedly executing the first step and the second step until the effective times are added to a preset value, namely K, wherein K=KH+KM+KL, and calculating a high signal to noise ratio statistical ratio KH/K, a low signal to noise ratio statistical ratio KL/K and a medium signal to noise ratio statistical ratio KM/K respectively.
Step four: determining the signal to noise ratio according to the statistical proportion
Comparing the magnitudes of three values KH/K, KL/K, KM/K, and if KH/K is maximum, outputting a high signal-to-noise ratio indication; outputting a low signal-to-noise ratio indication if KL/K is maximum; and if the KM/K is maximum, outputting a medium signal-to-noise ratio indication.
Step five: and (3) periodically repeating the first step to the fourth step to realize the dynamic update of the signal to noise ratio strength indication.
(III) beneficial effects
The signal-to-noise ratio strength dynamic judging method based on iterative decoding provided by the technical scheme only relates to binary exclusive OR operation and ratio calculation, and is simple to realize and small in resource occupation; the signal to noise ratio judgment is carried out by utilizing the decoded data, so that the reliability is high; and dynamically updating the signal to noise ratio by utilizing periodic statistics.
Drawings
Fig. 1 is a schematic diagram of a dynamic determination method of signal to noise ratio based on iterative decoding in the present invention.
Detailed Description
To make the objects, contents and advantages of the present invention more apparent, the following detailed description of the present invention will be given with reference to the accompanying drawings and examples.
The signal-to-noise ratio estimation method based on iterative decoding utilizes decoded data to carry out signal-to-noise ratio estimation, firstly sets a fixed decoding iteration number, carries out similarity calculation on the output sequences of two adjacent iterative decoding to obtain a group of similarity sequences, carries out signal-to-noise ratio statistics after judging that the sequences are effective, and finally judges the signal-to-noise ratio according to the statistical proportion.
Defining L as the length of the decoding output sequence, N as the iteration fixed termination times (obtained through simulation), and both L and N as positive integers greater than 0.
Referring to fig. 1, the signal-to-noise ratio strength determination method based on iterative decoding of the present invention includes the following steps:
step one: similarity calculation is carried out on the output sequences of the adjacent iterative decoding
The similarity calculation method comprises the following steps: and carrying out bit-by-bit binary exclusive OR on the output sequences of the two iterative decoding and then summing, wherein the obtained result is a similarity value of the two iterative decoding outputs, the value range of the similarity value is more than or equal to 0 and less than or equal to L, and the smaller the value is, the higher the similarity is.
Step two: constructing a similarity sequence by using the calculated similarity value, and judging the effectiveness
And when the similarity sequence is constructed, repeating the first step until the iteration times reach N, obtaining N-1 similarity values, and marking the similarity values as S arrays, wherein S= [ S 1,S2,......,SN-1 ].
When the validity judgment is carried out on the S array, if any one of the first condition and the second condition is met, the valid times are added with 1, and the value of N-P is recorded:
Condition one: the positive integer P is more than 1 and less than N-1, so that S 1＞S2＞......＞SP-1＞SP＝SP+1＝......＝SN-1 is established;
condition II: there is a positive integer P, p=1, making S P＝SP+1＝......＝SN-1 true.
Step three: signal to noise ratio strength statistics
And obtaining high signal-to-noise ratio statistics times, low signal-to-noise ratio statistics times and medium signal-to-noise ratio statistics times according to a preset signal-to-noise ratio threshold value, and respectively calculating to obtain a high signal-to-noise ratio statistics proportion, a low signal-to-noise ratio statistics proportion and a medium signal-to-noise ratio statistics proportion.
The obtaining process of the high signal-to-noise ratio statistics times, the low signal-to-noise ratio statistics times and the medium signal-to-noise ratio statistics times comprises the following steps:
Two thresholds are set: the signal-to-noise ratio high threshold T H and the signal-to-noise ratio low thresholds T L,TH and T L are positive integers and satisfy N > T H＞TL > 0. Comparing N-P with T H and T L:
If N-P is more than or equal to T H, representing that the signal to noise ratio of the received signal is strong, adding 1 to the high signal to noise ratio statistics number K H;
If N-P is less than T L, representing that the signal to noise ratio of the received signal is weak, adding 1 to the low signal to noise ratio statistics number K L;
if T L≤N-P＜TH represents that the signal to noise ratio of the received signal is medium, the statistics of the medium signal to noise ratio K M is increased by 1.
The process of calculating the high signal-to-noise ratio statistical proportion, the low signal-to-noise ratio statistical proportion and the medium signal-to-noise ratio statistical proportion is as follows: and repeatedly executing the first step and the second step until the effective times are added to a preset value, namely K, K=K H+KM+KL, and respectively calculating a high signal-to-noise ratio statistical proportion K H/K, a low signal-to-noise ratio statistical proportion K L/K and a medium signal-to-noise ratio statistical proportion K M/K.
Step four: determining the signal to noise ratio according to the statistical proportion
Comparing the magnitudes of the K H/K、KL/K、KM/K three values, and if the K H/K is the largest, outputting a high signal-to-noise ratio indication; if K L/K is maximum, outputting a low signal-to-noise ratio indication; if K M/K is maximum, a medium signal-to-noise ratio indication is output.
Step five: and (3) periodically repeating the first step to the fourth step to realize the dynamic update of the signal to noise ratio strength indication.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (3)
1. A signal-to-noise ratio strength judging method based on iterative decoding is characterized in that signal-to-noise ratio estimation is carried out by utilizing decoded data, firstly, a fixed decoding iteration number is set, similarity calculation is carried out on two adjacent iterative decoding output sequences, a group of similarity sequences are obtained, signal-to-noise ratio strength statistics is carried out after the sequences are judged to be effective, and finally, the signal-to-noise ratio strength is judged according to the statistical proportion;
defining L as the length of a decoding output sequence, N as the iteration fixed termination times, and both L and N as positive integers larger than 0;
the execution steps of the judging method are as follows:
step one: constructing a similarity sequence by using the calculated similarity value, and judging the effectiveness;
Step two: signal-to-noise ratio strong and weak statistics;
Step three: judging the signal to noise ratio strength according to the statistical proportion;
Step four: periodically and repeatedly executing the first step to the third step to realize the dynamic update of the signal to noise ratio strength indication;
In the first step, the similarity calculation method comprises the following steps: the two iterative decoding output sequences are bitwise binary exclusive-or and then summed, and the obtained result is a similarity value of the two iterative decoding output, wherein the value range is more than or equal to 0 and less than or equal to L, and the smaller the value is, the higher the similarity is;
in the second step, when the similarity sequence is constructed, the first step is repeatedly executed until the iteration times reach N, N-1 similarity values are obtained in total and recorded as an S array, and S= [ S1, S2, ] is the same as the SN-1;
In the second step, when the validity judgment is carried out on the S array, if any one of the first condition and the second condition is met, the valid times are added with 1, and the value of N-P is recorded:
condition one: the positive integer P,1 < P < N-1, S1 > S2 >, > SP-1 > sp=sp+1=, where SN-1 holds;
Condition II: there is a positive integer P, p=1, with sp=sp+1=. To satisfy SN-1;
Step three, according to a preset signal-to-noise ratio threshold value, obtaining high signal-to-noise ratio statistics times, low signal-to-noise ratio statistics times and medium signal-to-noise ratio statistics times, and respectively calculating to obtain high signal-to-noise ratio statistics proportion, low signal-to-noise ratio statistics proportion and medium signal-to-noise ratio statistics proportion;
In the third step, the obtaining process of the high signal-to-noise ratio statistics times, the low signal-to-noise ratio statistics times and the medium signal-to-noise ratio statistics times is as follows:
Two thresholds are set: the signal-to-noise ratio high threshold TH and the signal-to-noise ratio low threshold TL, both TH and TL are positive integers and satisfy N > TH > TL > 0; comparing N-P with TH and TL:
if N-P is larger than or equal to TH, representing that the signal to noise ratio of the received signal is strong, adding 1 to the statistics frequency KH of the high signal to noise ratio;
If N-P is smaller than TL, the signal to noise ratio of the received signal is weak, and the low signal to noise ratio statistics times KL is added with 1;
if TL is less than or equal to N-P and less than TH, representing that the signal to noise ratio of the received signal is medium, adding 1 to the statistics number KM of the medium signal to noise ratio.
2. The signal-to-noise ratio strength judging method based on iterative decoding as claimed in claim 1, wherein in the third step, the process of calculating the high signal-to-noise ratio statistical proportion, the low signal-to-noise ratio statistical proportion and the medium signal-to-noise ratio statistical proportion is as follows: and repeatedly executing the first step and the second step until the effective times are added to a preset value, namely K, wherein K=KH+KM+KL, and calculating a high signal to noise ratio statistical ratio KH/K, a low signal to noise ratio statistical ratio KL/K and a medium signal to noise ratio statistical ratio KM/K respectively.
3. The method for determining signal-to-noise ratio strength based on iterative decoding according to claim 2, wherein in step four, the magnitudes of three values KH/K, KL/K, KM/K are compared, and if KH/K is the largest, a high signal-to-noise ratio indication is output; outputting a low signal-to-noise ratio indication if KL/K is maximum; and if the KM/K is maximum, outputting a medium signal-to-noise ratio indication.
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