CN117692095A - Soft bit quantization processing method and device and electronic equipment - Google Patents
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Abstract
The application relates to the technical field of communication systems, in particular to a soft bit quantization processing method, a soft bit quantization processing device and electronic equipment, which are used for solving the problem that an inaccurate decoding result of a decoder is caused by inaccurate quantization processing results. The method comprises the steps of obtaining probability density distribution of log-likelihood ratio LLR of a received symbol under a target signal-to-noise ratio, wherein the probability density distribution comprises uncertain probabilities corresponding to floating point values in the LLR, the probability density distribution is probability density distribution under an Additive White Gaussian Noise (AWGN) condition, then determining N mapping values of the LLR based on the probability density distribution, N is the number of LLR values of the received symbol, and carrying out quantization processing on the N mapping values by adopting the target signal-to-noise ratio to obtain a quantization processing result. Based on the method, the signal-to-noise ratio variation under different fading scenes is considered, so that the decoding performance of a decoder can be optimized, and the accuracy of a final decoding result is improved.
Description
Technical Field
The present invention relates to the field of communications systems, and in particular, to a method and an apparatus for quantization processing of soft bits, and an electronic device.
Background
With the evolution of communication systems, in the fifth generation new radio system (english: 5th generation New Radio, abbreviated: 5G NR), modulation schemes such as binary phase shift keying (english: binary Phase Shift Keying, abbreviated: BPSK), quadrature phase shift keying (english: quadrature Phase Shift Keying, abbreviated: QPSK), quadrature amplitude modulation (english: quadrature Amplitude Modulation, abbreviated: 16 QAM), 64QAM, and 256QAM may be used for the corresponding modulation and demodulation. In general, modulation is relatively simple because the modulation process is a direct mapping process; demodulation is relatively complex, and is a probabilistic statistical process because the demodulation process is also subject to noise.
In a 5G NR system, a transmitting end may obtain modulation symbols corresponding to information bits through constellation mapping in different modulation modes, and then process the modulation symbols into signals through a series of operations to transmit the signals. The receiving end receives the signal transmitted by the transmitting end to obtain a receiving symbol recovered by the signal, wherein the receiving symbol is a modulation symbol affected by noise in the transmission process, so if the receiving symbol is directly demodulated, the error rate of the finally obtained information bit is high, that is, the demodulation result is inaccurate. To remove the influence of noise, the following operations are now generally performed: log likelihood ratios (english: log Likelihood Ratio, abbreviation: LLR) of the received symbols are calculated, and then the calculation result is fed into a decoder for decoding (demodulation).
Specifically, each LLR value calculated as described above can be understood as a probability value: if the LLR value is positive and the absolute value of the LLR value is larger, the probability of the information bit being 1 is larger; if the LLR value is negative and the absolute value of the LLR value is larger, the probability of the information bit being-1 is larger; the uncertainty of the information bits is higher as the absolute value of the LLR value approaches 0.
It should be noted that the LLR refers to a log-likelihood ratio, and represents a probability form that 1 binary information bit is 0 or 1. In general, there are tens of thousands of information bits transmitted, and N is denoted herein, that is, N information bits, corresponding to N LLR values when processed at the receiving end. In addition, the modulation mapping is to map 1 or several information bits into one constellation symbol, for example, 1 QPSK symbol is mapped by 2 bits, so that 2 LLR values can be calculated by 1 QPSK received symbol.
Based on this, a group of floating point number sequences consisting of floating point LLR values can be obtained by computing the received symbols by means of log likelihood ratios, wherein the floating point number is a decimal point not fixedNumber of (a), floating point LLR value X LLRs Can be expressed as: { X LLRs }＝{x 0 ,x 1 ,x 2 ,…,x N-1 },-∞≤x i ≤+∞。
However, from the implementation of the decoder, the decoder cannot directly process floating point numbers, and the decoder can only process a fixed point number sequence with a fixed saturation bit width, wherein the fixed saturation bit width is a designated value range, and the fixed point number is a fixed point number of decimal points. Therefore, in order to meet the implementation of the decoder, the above floating point LLR values need to be quantized, specifically, a set of fixed point number sequences composed of fixed point LLR values with fixed saturated bit widths is obtained by quantization, wherein if N input values are provided and the saturated bit width is K, the fixed point LLR value Q LLRs Can be expressed as: { Q LLRs }＝{q 0 ,q 1 ,q 2 ,…,q N-1 },-2 K ≤q i ≤2 K -1。
Obviously, the result of the quantization process described above will have an impact on the accuracy of the decoding result of the decoder. Therefore, in order to optimize decoding performance and improve accuracy of decoding results, the following quantization processing methods are proposed in the prior art: for the floating point LLR value x i Setting a quantization scaling factor a according to human experience, and then passingTo obtain the fixed point LLR value Q LLRs I.e. the prior art relies on manual experience. However, in practical application, various different fading scenes are faced, and when the above-mentioned prior art is applied to different fading scenes, there is still a problem that the quantization processing result is inaccurate, so that the decoding result of the decoder is inaccurate.
Disclosure of Invention
The application provides a soft bit quantization processing method, a soft bit quantization processing device and electronic equipment, which are used for optimizing decoding performance of a decoder and improving accuracy of a final decoding result.
In a first aspect, the present application provides a method for quantization processing of soft bits, where the method includes:
acquiring probability density distribution of log-likelihood ratio (LLR) of a received symbol under a target signal-to-noise ratio; wherein the probability density distribution comprises uncertainty probabilities corresponding to various floating point values in the LLR, and the probability density distribution is under the condition of additive white Gaussian noise (English: additive White Gaussian Noise, abbreviated: AWGN);
determining the LLR to obtain N mapping values based on the probability density distribution; wherein N is the number of LLR values of the received symbol;
and carrying out quantization processing on the N mapping values by adopting the target signal-to-noise ratio to obtain a quantization processing result.
According to the method, the change of the signal to noise ratio under different fading scenes is considered, then the LLR is mapped in K subintervals based on probability density distribution under the target signal to noise ratio, N mapping values are obtained, quantization processing is carried out on the N mapping values based on the target signal to noise ratio, a quantization processing result is finally obtained, and the obtained quantization processing result is input into a decoder, so that the decoding performance of the decoder is optimized, and the accuracy of the final decoding result is improved.
In one possible implementation, the target signal-to-noise ratio is based on the following formula:
wherein SNR is the target SNR, E s For the average symbol energy of the transmitting end, T s For symbol period, N 0 B is the noise energy of the AWGN n Is noise broadband.
In one possible implementation, the E s And said N 0 And (3) carrying out inversion calculation based on the bit error rate under the AWGN condition to obtain:
if the modulation mode is BPSK, the bit error rate is calculated based on the following formula:
if the modulation mode is QPSK, the error rate is calculated based on the following formula:
if the modulation mode is M-QAM, the error rate is calculated based on the following formula:
wherein, Q () is a Q function, and the calculation formula of the Q function is as follows:
wherein erfc () is a gaussian complement difference function, and the calculation formula of the gaussian complement difference function is as follows:
by the method, the actual fading scene is further considered, the target signal-to-noise ratio which is optimal in theory is selected, then the quantization processing result of the LLR is obtained based on the target signal-to-noise ratio, and the decoding performance of the decoder is optimized, so that the decoding accuracy of the decoder is improved.
In one possible implementation, the determining the N mapping values for the LLR includes: obtaining K subintervals which are continuously distributed; the K subintervals are obtained by dividing saturated bit widths of a decoder; and mapping the LLR in the K subintervals to obtain N mapped values after mapping.
According to the method, the LLR is mapped into K subintervals divided based on the saturated bit width in consideration of the difference of the saturated bit width of different decoders, N mapping values are obtained, and the N mapping values are further processed by adopting quantization scaling factors, so that the method is applicable to different fading scenes, and the optimization of decoding performance under different fading lengths is realized.
In one possible implementation, the mapping the LLRs in the K subintervals includes: for a single subinterval of the K subintervals, calculating a mapping value R in the single subinterval by adopting the following formula i ＝{a i-1 ,a i Mapping value y of the LLR n I=1, 2, … K-1, then
If l is E R K ＝[a K-1 ,∞]Then y n ＝sign(x n )·y K-1 ；
Wherein x is n Sign () is x for the nth floating point value of the LLR n A is a sign function of (a) i A, for the right end point of the single interval i-1 For the left end point of the single interval, l is x n P () is the probability density distribution at the target SNR under AWGN conditions.
According to the method, the LLR is mapped into K subintervals divided based on the saturated bit width in consideration of the difference of the saturated bit width of different decoders, N mapping values are obtained, and the N mapping values are further processed by adopting the quantization scaling factors, so that the method is applicable to different fading scenes, and the optimization of decoding performance under different fading lengths is realized.
In one possible implementation, the performing quantization processing on the N mapping values using the target signal-to-noise ratio to obtain a quantization processing result includes: acquiring a linear signal-to-noise ratio of the received symbol and the target signal-to-noise ratio; calculating the ratio of the linear signal-to-noise ratio to the target signal-to-noise ratio, and taking the ratio as a quantization scaling factor; and scaling the N mapping values by adopting the quantization scaling factors, and taking the scaling result as a quantization processing result.
The method can solve the problem that the quantization scaling factor is determined according to the manual experience in the prior art, realize the self-adaptive determination of the quantization scaling factor, and the scaling factor obtained in the way can be suitable for different fading scenes, namely realize the optimization of decoding performance under the scenes.
In one possible implementation, the scaling processing is performed on the N mapping values by using the quantization scaling factors, and the scaling processing result is taken as a quantization processing result, including: for a single mapping value of the N mapping values, performing the following processing operations: rounding after rounding the product between the quantized scaling factor and the single mapping value, and taking the rounding result as a scaling processing result of the single mapping value; and repeatedly executing the processing operation to obtain the scaling processing results corresponding to the N mapping values, and taking the scaling processing results as quantization processing results.
By the method, the change of the signal-to-noise ratio under different fading scenes is considered, and the LLR is subjected to different mapping processing and scaling processing and then rounding processing, so that the decoder can better recognize the size relation among the LLRs, and the decoding performance is improved.
In a second aspect, the present application provides an apparatus for quantization processing of soft bits, the apparatus comprising:
the acquisition module acquires probability density distribution of log-likelihood ratio LLR of the received symbol under the target signal-to-noise ratio; the probability density distribution comprises uncertainty probabilities corresponding to all floating point values in the LLR, and the probability density distribution is under the condition of Additive White Gaussian Noise (AWGN);
the determining module is used for determining the LLR to obtain N mapping values based on the probability density distribution; wherein N is the number of LLR values of the received symbol;
and the processing module is used for carrying out quantization processing on the N mapping values by adopting the target signal-to-noise ratio to obtain a quantization processing result.
In one possible implementation, the target signal-to-noise ratio is based on the following formula:
wherein SNR is the target SNR, E s For the average symbol energy of the transmitting end, T s For symbol period, N 0 B is the noise energy of the AWGN n Is noise broadband.
In one possible implementation, the E s And said N 0 And (3) carrying out inversion calculation based on the bit error rate under the AWGN condition to obtain:
if the modulation mode is BPSK, the bit error rate is calculated based on the following formula:
if the modulation mode is QPSK, the error rate is calculated based on the following formula:
if the modulation mode is M-QAM, the error rate is calculated based on the following formula:
wherein, Q () is a Q function, and the calculation formula of the Q function is as follows:
wherein erfc () is a gaussian complement difference function, and the calculation formula of the gaussian complement difference function is as follows:
in one possible implementation, the determining N mapping values of the LLR, the determining module is specifically configured to: obtaining K subintervals which are continuously distributed; the K subintervals are obtained by dividing saturated bit widths of a decoder; and mapping the LLR in the K subintervals to obtain N mapped values after mapping.
In a possible implementation, the mapping the LLR in the K subintervals, the determining module is specifically configured to: for a single subinterval of the K subintervals, calculating a mapping value R in the single subinterval by adopting the following formula i ＝{a i-1 ,a i Mapping value y of the LLR n I=1, 2, … K-1, then
If l is E R K ＝[a K-1 ,∞]Then y n ＝sign(x n )·y K-1 ；
Wherein x is n Sign () is x for the nth floating point value of the LLR n A is a sign function of (a) i A, for the right end point of the single interval i-1 For the left end point of the single interval, l is x n P () is the probability density distribution at the target SNR under AWGN conditions.
In one possible implementation, the quantization processing is performed on the N mapping values by using the target signal-to-noise ratio, so as to obtain a quantization processing result, and the processing module is specifically configured to: acquiring a linear signal-to-noise ratio of the received symbol and the target signal-to-noise ratio; calculating the ratio of the linear signal-to-noise ratio to the target signal-to-noise ratio, and taking the ratio as a quantization scaling factor; and scaling the N mapping values by adopting the quantization scaling factors, and taking the scaling result as a quantization processing result.
In one possible implementation, the scaling process is performed on the N mapping values by using the quantization scaling factors, and a result of the scaling process is taken as a quantization processing result, and the processing module is specifically configured to: for a single mapping value of the N mapping values, performing the following processing operations: rounding after rounding the product between the quantized scaling factor and the single mapping value, and taking the rounding result as a scaling processing result of the single mapping value; and repeatedly executing the processing operation to obtain the scaling processing results corresponding to the N mapping values, and taking the scaling processing results as quantization processing results.
In a third aspect, the present application provides an electronic device, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the soft bit quantization processing method when executing the computer program stored in the memory.
In a fourth aspect, the present application provides a computer readable storage medium having stored therein a computer program which, when executed by a processor, implements the above-described method steps of quantization processing of soft bits.
The technical effects of each of the second to fourth aspects and the technical effects that may be achieved by each aspect are referred to above for the technical effects that may be achieved by the first aspect or each possible aspect in the first aspect, and the detailed description is not repeated here.
Drawings
Fig. 1 is a schematic diagram of a signal processing flow of a transmitting end provided in the present application;
fig. 2 is a flowchart of a soft bit quantization processing method provided in the present application;
FIG. 3 is a graph showing probability density distribution of LLRs for BPSK at different signal-to-noise ratios;
fig. 4 is a schematic diagram of a soft bit quantization processing device provided in the present application;
Fig. 5 is a schematic diagram of a structure of an electronic device provided in the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings. The specific method of operation in the method embodiment may also be applied to the device embodiment or the system embodiment.
In the description of the present application "a plurality of" is understood as "at least two". "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. A is connected with B, and can be represented as follows: both cases of direct connection of A and B and connection of A and B through C. In addition, in the description of the present application, the words "first," "second," and the like are used merely for distinguishing between the descriptions and not be construed as indicating or implying a relative importance or order.
The embodiment of the application provides a possible application scene, which specifically comprises the following steps: a transmitting end and a receiving end.
As shown in fig. 1, which is a schematic diagram of a signal processing flow of a transmitting end, for the transmitting end, firstly, encoding information bits to obtain encoded bits after encoding; then scrambling the coded bits to obtain scrambled bits after scrambling; performing constellation mapping processing in different modulation modes to obtain modulation symbols after constellation mapping processing; the modulated symbols are sequentially subjected to layer mapping processing, precoding processing, antenna mapping processing and Orthogonal Frequency Division Multiplexing (OFDM) modulation processing, a time domain signal is obtained after the OFDM modulation processing, and the time domain signal is transmitted as a transmitting signal.
The receiving end receives the transmission signal transmitted from the transmitting end and then performs signal processing on the received transmission signal, and it is easy to understand that a person skilled in the art can consider the signal processing procedure of the receiving end as an inverse processing procedure of the signal processing procedure of the transmitting end, which is not described in detail herein.
That is, the transmitting end can obtain the modulation symbols corresponding to the information bits through constellation mapping of different modulation modes, and then process the modulation symbols into signals through a series of operations to transmit the signals. The receiving end receives the signal transmitted by the transmitting end to obtain a receiving symbol recovered by the signal, wherein the receiving symbol is a modulation symbol affected by noise in the transmission process, and if the receiving symbol is directly demodulated, the error rate of the finally obtained information bit is high, namely the demodulation result is inaccurate. So in order to remove the influence of noise, the following operations are generally performed: the LLR of the received symbol is calculated and then the calculation result is fed into a decoder for decoding (demodulation).
Wherein, each LLR value obtained by the calculation is understood as a probability value: if the LLR value is positive and the absolute value of the LLR value is larger, the probability of the information bit being 1 is larger; if the LLR value is negative and the absolute value of the LLR value is larger, the probability of the information bit being-1 is larger; the uncertainty of the information bits is higher as the absolute value of the LLR value approaches 0.
It should be noted that the LLR refers to a log-likelihood ratio, and represents a probability form that 1 binary information bit is 0 or 1. In general, there are tens of thousands of information bits transmitted, and N is denoted herein, that is, N information bits, corresponding to N LLR values when processed at the receiving end. In addition, the modulation mapping is to map 1 or several information bits into one constellation symbol, for example, 1 QPSK symbol is mapped by 2 bits, so that 2 LLR values can be calculated by 1 QPSK received symbol.
Based on this, the received symbol is calculated by log likelihood ratio, under AWGN condition, the floating point LLR value X LLRs Can be expressed as: { X LLRs }＝{x 0 ,x 1 ,x 2 ,…,x n ,…,x N-1 },-∞≤x n Is less than or equal to + -infinity, wherein x is n Is the nth floating point LLR value.
However, from the implementation of the decoder, the decoder cannot directly process floating point numbers, and the decoder can only process a fixed point number sequence with a fixed saturation bit width, wherein the fixed saturation bit width is a designated value range, and the fixed point number is a fixed point number of decimal points. Thus, to satisfy decodingThe implementation of the device needs to carry out quantization processing on the floating point LLR values, in particular to obtain a group of fixed point number sequences which have fixed saturation bit width and are composed of fixed point LLR values, wherein the fixed point number sequences Q composed of the fixed point LLR values LLRs Can be expressed as: { Q LLRs }＝{q 0 ,q 1 ,q 2 ,…,q n ,…,q N-1 },-2 K ≤q n ≤2 K -1, where K is the saturation bit width recognizable by the decoder, -2 K For the decoder to recognize the left end point of the range, 2 K -1 is the right end point of the decoder identifiable range. It should be noted that there may be a difference in the saturation bit width identifiable to different decoders, i.e., there is a difference in the saturation bit width K identifiable to the decoders.
Obviously, the result of the quantization process described above will have an impact on the accuracy of the decoding result of the decoder. Therefore, an optimized quantization processing method is helpful for optimizing decoding performance and improving accuracy of decoding results.
The application provides a quantization processing method and device for soft bits and electronic equipment, and solves the problem that an inaccurate decoding result of a decoder is caused by an inaccurate quantization processing result.
It should be noted that, the technical solution provided in the embodiments of the present application may be applied to any communication system that needs to perform quantization processing on LLRs and/or any decoder that needs to perform quantization processing on LLRs, and is, of course, particularly applicable to a low-density parity check code (english: low Density Parity Check Code, LDPC) decoder for a physical uplink shared channel (english: physical Uplink Shared Channel, abbreviated: PUSCH)/physical downlink shared channel (english: physical Downlink Shared Channel, abbreviated: PDSCH) channel in 5G NR.
Further, the technical features included in the embodiments of the present application may be combined at will, and those skilled in the art should understand that, from the practical application situation, the technical solution obtained by reasonably combining the technical features in the embodiments of the present application may also solve the same technical problem or achieve the same technical effect.
According to the application ofThe method provided by the embodiment firstly obtains each probability density distribution of the log likelihood ratio LLR of the received symbol under the target signal-to-noise ratio, namely, the signal-to-noise ratio variation under different fading scenes is considered. Then mapping LLR into respective mapping values of K subintervals based on each probability density distribution to obtain N mapping values, wherein N is greater than K and less than 2 K And then the N mapping values are quantized by adopting a target signal-to-noise ratio, and a quantization result is finally obtained, namely, the quantization result is obtained based on probability density distribution under the target signal-to-noise ratio, and the obtained quantization result is input into a decoder, so that the decoding performance of the decoder is optimized, and the accuracy of the final decoding result is improved.
The methods provided in the embodiments of the present application are described in further detail below with reference to the accompanying drawings.
Referring to fig. 2, an embodiment of the present application provides a soft bit quantization processing method, which specifically includes:
step 201: acquiring probability density distribution of log-likelihood ratio (LLR) of a received symbol under a target signal-to-noise ratio;
in the embodiment of the present application, a manner of obtaining the best snr is provided, which is specifically described below, and an example is assumed that the average symbol energy of the transmitting end is E s AWGN noise energy of N 0 The symbol period is T s Noise bandwidth of B n The noise power is N, the transmit signal power is S, and the bit error rate under the AWGN condition is as follows:
for BPSK:
for QPSK:
assuming that the minimum amplitude symbol energy of M-QAM modulation is E min Then for M-QAM:
and (3) carrying out inversion calculation according to the BER required by the system to obtain the optimal linear signal-to-noise ratio required by decoding under the AWGN:
N＝N 0 *B n ；
wherein, the Q function is:
the erfc (gaussian error compensation) function is:
in summary, the target signal-to-noise ratio can be obtained based on the following formula:
wherein SNR is the target SNR, E s For the average symbol energy of the transmitting end, T s For symbol period, N 0 B is the noise energy of the AWGN n Is noise broadband.
In the embodiment of the application, the probability density distribution includes uncertainty probabilities corresponding to various floating point values in the LLR.
In particular, the log-likelihood ratio LLR of a received symbol may be a set of sequences of floating-point LLR values: { X LLRs }＝{x 0 ,x 1 ,x 2 ,…,x n ,…,x N-1 },-∞≤x n Is less than or equal to + -infinity, wherein x is n Taking BPSK as an example, x is the floating point LLR value of the nth bit n (expressed as x in the formula) the probability density distribution function at different signal to noise ratios can be seen as follows:
wherein,for variance-> Is the noise power.
Based on the probability density distribution function, probability density distribution of LLR under different signal to noise ratios based on BPSK can be obtained.
For example, referring to fig. 3, a schematic diagram of probability density distribution of LLRs of BPSK under different signal-to-noise ratios is shown, where the abscissa is a floating point LLR value and the ordinate is an uncertainty probability corresponding to the floating point LLR value. In detail, taking a floating point LLR value as an example, when snr= -5dB, the uncertainty probability corresponding to the floating point LLR value is 0.18; when snr=0 dB, the uncertainty probability corresponding to the floating point LLR value is 0.05; when snr= +5dB, the uncertainty probability corresponding to the floating point LLR value is 0.003. That is, when the floating point LLR value is 0, snr= +5dB, the uncertainty probability corresponding to the floating point LLR value is minimum.
In summary, the probability density distributions of log-likelihood ratio LLRs of the received symbols at different signal-to-noise ratios are obtained by this step.
Step 202: determining N mapping values of the LLR based on the probability density distribution;
in the embodiment of the present application, each floating point LLR value of the LLR may be understood as a probability value, that is, if the floating point LLR value is a positive number and the absolute value of the floating point LLR value is greater, the probability that the information bit is 1 is greater; if the floating point LLR value is a negative number and the absolute value of the floating point LLR value is larger, the probability that the information bit is-1 is larger; the higher the uncertainty of the information bits, the closer the absolute value of the floating point LLR value is to 0. In other words, the uncertainty of the floating point LLR value is maximized when the absolute value of the floating point LLR value approaches 0, and it is necessary to reduce the uncertainty when the absolute value of the floating point LLR value approaches 0 in order to optimize decoding performance.
Therefore, in the embodiment of the present application, the LLR may be mapped to respective mapping values of K subintervals based on the probability density distribution under the target signal-to-noise ratio obtained in step 201, and the calculated N mapping values are obtained, where the K subintervals are obtained by dividing the saturated bit width of the decoder.
In detail, due to the limitation of the decoder saturation bit width, if the LLR value { X } is floating point LLRs }＝{x 0 ,x 1 ,x 2 ,…,x n ,…,x N-1 },-∞≤x n Is less than or equal to + -infinity, wherein x is n N-th bit of floating point LLR value, and sequence Q of fixed points consisting of fixed point LLR values LLRs The method comprises the following steps: { Q LLRs }＝{q 0 ,q 1 ,q 2 ,…,q n ,…,q N-1 },-2 K ≤q n ≤2 K -1, where K is the saturation bit width recognizable by the decoder, -2 K For the decoder to recognize the left end point of the range, 2 K -1 is the right end point of the decoder identifiable range, then the decoder is for q n At most, only 2 can be recognized K+1 In the case, the quantization mapping is performed at the optimal signal-to-noise ratio, where 0,2 K ]Divided into K-1 subintervals R i Can be expressed as { a } i-1 ,a i },i＝1,2,…,K-1。
In addition, the K-1 subinterval is a (positive) finite subinterval, and one subinterval [ a ] can be added K-1 ,∞]And obtaining final K subintervals, wherein K is determined according to decoder fixed-point settings of different manufacturers.
After the explicitly divided K subintervals,the mapping at a single subinterval R of the K subintervals may be calculated for that single subinterval i ＝{a i-1 ,a i Mapping value y of LLR of } n I=1, 2, … K-1, see the following formula:
for the last subinterval, if l E R K ＝[a K-1 ,∞]Then y n ＝sign(x n )·y K-1 ；
Wherein x is n Sign () is x, which is the nth floating point value of LLR n A is a sign function of (a) i A is the right end point of a single interval i-1 Is the left end point of a single interval, l is x n P () is the probability density distribution at the target SNR under AWGN conditions.
By the method, the mapping values of the LLR mapping in the K subintervals can be obtained, and the final N mapping values are obtained.
It should be noted that although the number of LLR values finally obtained is N, N is generally much larger than K, and K subintervals correspond to K mapping values, N mapping values are obtained after mapping the N LLR values respectively, and LLR has a positive and negative sign distinction, so that the N mapping values have at most 2 K Species (negative and positive numbers are inversely mapped values).
Step 203: and carrying out quantization processing on the N mapping values by adopting the target signal-to-noise ratio to obtain a quantization processing result.
In the embodiment of the application, a linear signal-to-noise ratio of a received symbol and a target signal-to-noise ratio are obtained, then a ratio of the linear signal-to-noise ratio to the target signal-to-noise ratio is calculated, the ratio is used as a quantization scaling factor, the quantization scaling factor is used for scaling N mapping values, and a scaling result is used as a quantization processing result.
Here, the calculation process of the quantization scaling factor can be referred to as the following formula:
wherein a is n For quantization scaling factors of received symbols, snr n For linear signal-to-noise ratio of received symbols, snr opt Is the target signal to noise ratio.
After the quantized scaling factor is calculated, the LLR is quantized by using the quantized scaling factor, and a quantization result is obtained.
In other words, the above-described processing can be understood as a scaling process including scaling the N mapping values with quantization scaling factors, respectively, and taking the result of the scaling processing as the quantization processing result.
Specifically, based on the N mapping values that can be obtained in step 201, the following processing operations are performed for a single mapping value of the N mapping values: calculating the product between the quantized scaling factor and the single mapping value, rounding the product, and taking the rounding result as the scaling processing result of the single mapping value; the above processing operation is repeatedly executed to obtain the scaling processing results corresponding to the N mapping values, and the scaling processing results are used as quantization processing results, that is, the calculation process of the above processing operation can be shown by the following formula:
wherein q n Scaling results for single mapped values, i.e. fixed point LLR values, a n To quantify the scaling factor, y n For a single mapped value, b is a specified value.
It should be noted that the above specified value may be determined according to practical application requirements, and is generally 0.5.
Through the above processing operation, each of the mapped values y n Can obtain the corresponding scaling result q n N mapping values { y } are also obtained 1 ,y 2 ,…,y n ,…,y N N=1, 2, …, N eachThe result { q ] of the corresponding scaling process 1 ,q 2 ,…,q n ,…,q N N=1, 2, …, N, where the results of the N scaling processes { q 1 ,q 2 ,…,q n ,…,q N N=1, 2, …, N as quantization processing results.
According to the method, the change of the signal to noise ratio under different fading scenes is considered, the target signal to noise ratio is selected based on probability density distribution of LLRs of received symbols under different signal to noise ratios of a certain modulation mode, a quantization scaling factor is obtained based on the selected target signal to noise ratio, then mapping processing is carried out on N LLR values of the received symbols based on saturated bit width of a decoder, the N mapping values are obtained by specifically mapping the N LLR values to K different continuous subintervals divided by the saturated bit width of the decoder, scaling processing is carried out on the N mapping values by adopting a quantization scaling factor, and rounding operation is carried out on the scaling processing result to obtain a final quantization processing result. The quantization processing result obtained in this way not only can be suitable for decoders of different manufacturers or models, but also is helpful for the decoder to better identify the magnitude relation between LLRs, thereby improving the decoding performance of the decoder.
Furthermore, the method takes actual fading scenes into consideration, namely, a method for obtaining the target signal-to-noise ratio is provided, and the quantization processing result of the LLR is obtained based on the target signal-to-noise ratio, so that the decoding performance of the decoder is optimized, and the decoding accuracy of the decoder is improved.
Optionally, in this embodiment of the present application, for the selection of the target snr, the decoding performance of the result of the LLR quantization processing under the different snrs may be further selected as the target snr by using an analog simulation method, that is, a simulation test. Here, the decoding performance may specifically be a decoding rate of the decoder, and the preset performance requirement may be set based on an actual simulation test, and may also be set according to a requirement of the decoder.
In some possible embodiments, a correspondence between the debug mode and the signal-to-noise ratio may also be set, and this correspondence further includes a priority relationship, that is, taking any debug mode as an example, it is preset with 3 signal-to-noise ratios: the signal to noise ratio 1, the signal to noise ratio 2 and the signal to noise ratio 3 are also in the priority order of the signal to noise ratio 1, the signal to noise ratio 2 and the signal to noise ratio 3, in the process of simulation debugging, the simulation debugging is carried out on the signal to noise ratio 1 preferentially, if the signal to noise ratio 1 is not the target signal to noise ratio meeting the condition, the signal to noise ratio 2 is simulated and debugged again, and the like. If the preset corresponding relation does not meet the condition, selecting the signal-to-noise ratio with the best decoding performance from the signal-to-noise ratios participating in simulation debugging as a target signal-to-noise ratio. Of course, the correspondence relationship here may also be a value range of the signal-to-noise ratio corresponding to the debug mode.
Alternatively, in the embodiment of the present application, the selection of the target signal-to-noise ratio may also be obtained by means of model training. The model can be built based on a neural network, input parameters of the model comprise a debugging mode and a signal-to-noise ratio sequence, and output parameters are signal-to-noise ratios corresponding to optimal decoding performance. Specifically, firstly, training parameters of a neural network are obtained, then, based on the training parameters, a target signal-to-noise ratio in a specified range is used as an input parameter for a certain debugging mode, an output result is obtained through iterative training of a model, and the output result is used as the target signal-to-noise ratio.
According to the method provided by the embodiment of the application, the change of the signal to noise ratio under different fading scenes can be considered, the target signal to noise ratio is adopted to conduct quantization processing on the LLR according to the probability density distribution of the LLR under the target signal to noise ratio, different mapping, scaling and rounding are conducted on the LLR in the quantization processing, and the size relation among the LLRs can be better recognized by a decoder, so that decoding performance is improved.
Based on the same inventive concept, the present application further provides a soft bit quantization processing device, which is configured to implement quantization processing on LLRs in different fading scenarios, solve the problem that the decoding result of a decoder is inaccurate due to inaccurate quantization processing result, effectively optimize the decoding performance of the decoder, and improve the accuracy of the final decoding result, see fig. 4, where the device includes:
The acquisition module 401 acquires probability density distribution of log likelihood ratio LLR of the received symbol under the target signal-to-noise ratio; the probability density distribution comprises uncertainty probabilities corresponding to all floating point values in the LLR, and the probability density distribution is under the condition of Additive White Gaussian Noise (AWGN);
a determining module 402 that determines N mapping values of the LLRs based on the probability density distribution; wherein N is the number of LLR values of the received symbol;
and the processing module 403 performs quantization processing on the N mapping values by adopting the target signal-to-noise ratio to obtain a quantization processing result.
In one possible implementation, the target signal-to-noise ratio is based on the following formula:
wherein SNR is the target SNR, E s For the average symbol energy of the transmitting end, T s For symbol period, N 0 B is the noise energy of the AWGN n Is noise broadband.
In one possible implementation, the E s And said N 0 And (3) carrying out inversion calculation based on the bit error rate under the AWGN condition to obtain:
if the modulation mode is BPSK, the bit error rate is calculated based on the following formula:
if the modulation mode is QPSK, the error rate is calculated based on the following formula:
If the modulation mode is M-QAM, the error rate is calculated based on the following formula:
wherein, Q () is a Q function, and the calculation formula of the Q function is as follows:
wherein erfc () is a gaussian complement difference function, and the calculation formula of the gaussian complement difference function is as follows:
in one possible implementation, the determining N mapping values of the LLR mapping, the determining module 402 is specifically configured to: obtaining K subintervals which are continuously distributed; the K subintervals are obtained by dividing saturated bit widths of a decoder; and mapping the LLR in the K subintervals to obtain N mapped values after mapping.
In one possible implementation, the mapping the LLRs in the K subintervals, the determining module 402 is specifically configured to: for a single subinterval of the K subintervals, calculating a mapping value R in the single subinterval by adopting the following formula i ＝{a i-1 ,a i Mapping value y of the LLR n I=1, 2, … K-1, then
If l is E R K ＝[a K-1 ,∞]Then y n ＝sign(x n )·y K-1 ；
Wherein x is n Sign () is x for the nth floating point value of the LLR n A is a sign function of (a) i A, for the right end point of the single interval i-1 For the left end point of the single interval, l is x n P () is the absolute value of p () at the target SNR under AWGN conditions Probability density distribution of (c).
In one possible implementation, the quantization processing is performed on the N mapping values by using the target signal-to-noise ratio, so as to obtain a quantization processing result, and the processing module 403 is specifically configured to: acquiring a linear signal-to-noise ratio of the received symbol and the target signal-to-noise ratio; calculating the ratio of the linear signal-to-noise ratio to the target signal-to-noise ratio, and taking the ratio as a quantization scaling factor; and scaling the N mapping values by adopting the quantization scaling factors, and taking the scaling result as a quantization processing result.
In one possible implementation, the scaling process is performed on the N mapping values by using the quantization scaling factors, and the scaling process result is taken as a quantization process result, and the processing module 403 is specifically configured to: for a single mapping value of the N mapping values, performing the following processing operations: rounding after rounding the product between the quantized scaling factor and the single mapping value, and taking the rounding result as a scaling processing result of the single mapping value; and repeatedly executing the processing operation to obtain the scaling processing results corresponding to the N mapping values, and taking the scaling processing results as quantization processing results.
Based on the device, considering the signal-to-noise ratio change under different fading scenes, obtaining quantization scaling factors based on probability density distribution of LLR under a certain modulation mode and a target signal-to-noise ratio, then carrying out mapping processing on K different continuous subintervals on the LLR based on saturated bit width of a decoder to obtain N mapping values, carrying out scaling processing on the N mapping values by adopting the quantization scaling factors, and then carrying out downward rounding operation on the scaling processing result to obtain a final quantization processing result. The quantization processing result obtained in this way not only can be suitable for decoders of different manufacturers or models, but also is helpful for the decoder to better identify the magnitude relation between LLRs, thereby improving the decoding performance of the decoder.
Based on the same inventive concept, the embodiment of the present application further provides an electronic device, where the electronic device may implement the function of the foregoing soft bit quantization processing apparatus, and referring to fig. 5, the electronic device includes:
the embodiment of the present application does not limit the specific connection medium between the processor 501 and the memory 502, but the connection between the processor 501 and the memory 502 through the bus 500 is exemplified in fig. 5. The connection between the other components of bus 500 is shown in bold lines in fig. 5, and is merely illustrative and not limiting. Bus 500 may be divided into an address bus, a data bus, a control bus, etc., and is represented by only one thick line in fig. 5 for ease of illustration, but does not represent only one bus or one type of bus. Alternatively, the processor 501 may be referred to as a controller, and the names are not limited.
In the embodiment of the present application, the memory 502 stores instructions executable by the at least one processor 501, and the at least one processor 501 may perform the soft bit quantization processing method described above by executing the instructions stored in the memory 502. The processor 501 may implement the functions of the various modules in the apparatus shown in fig. 4.
The processor 501 is a control center of the device, and various interfaces and lines can be used to connect various parts of the entire control device, and by executing or executing instructions stored in the memory 502 and invoking data stored in the memory 502, various functions of the device and processing data can be performed to monitor the device as a whole.
In one possible design, processor 501 may include one or more processing units, and processor 501 may integrate an application processor and a modem processor, where the application processor primarily processes operating systems, user interfaces, application programs, and the like, and the modem processor primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 501. In some embodiments, processor 501 and memory 502 may be implemented on the same chip, or they may be implemented separately on separate chips in some embodiments.
The processor 501 may be a general purpose processor such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, and may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present application. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the soft bit quantization processing method disclosed in connection with the embodiments of the present application may be directly embodied as a hardware processor executing, or may be executed by a combination of hardware and software modules in the processor.
The memory 502, as a non-volatile computer readable storage medium, may be used to store non-volatile software programs, non-volatile computer executable programs, and modules. The Memory 502 may include at least one type of storage medium, and may include, for example, flash Memory, a hard disk, a multimedia card, a card-type Memory, a random access Memory (english: random Access Memory, abbreviated as "RAM"), a static random access Memory (english: static Random Access Memory, abbreviated as "SRAM"), a programmable Read-Only Memory (english: programmable Read Only Memory, abbreviated as "PROM"), a Read Only Memory (english: ROM), a charged erasable programmable Read-Only Memory (english: electrically Erasable Programmable Read-Only Memory, abbreviated as "EEPROM"), a magnetic Memory, a magnetic disk, an optical disk, and the like. Memory 502 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 502 in the present embodiment may also be circuitry or any other device capable of implementing a memory function for storing program instructions and/or data.
By programming the processor 501, the code corresponding to the soft bit quantization method described in the foregoing embodiment can be solidified into a chip, so that the chip can execute the steps of the soft bit quantization method of the embodiment shown in fig. 2 at the time of operation. How to design and program the processor 501 is a technique well known to those skilled in the art, and will not be described in detail herein.
Based on the same inventive concept, the embodiments of the present application also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the soft bit quantization processing method described in the foregoing.
In some possible embodiments, aspects of the soft bit quantization method provided herein may also be implemented in the form of a program product comprising program code for causing the control apparatus to carry out the steps of the soft bit quantization method according to the various exemplary embodiments of the present application as described herein above when the program product is run on a device.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. A method for quantization processing of soft bits, the method comprising:
acquiring probability density distribution of log-likelihood ratio (LLR) of a received symbol under a target signal-to-noise ratio; the probability density distribution comprises uncertainty probabilities corresponding to all floating point values in the LLR, and the probability density distribution is under the condition of Additive White Gaussian Noise (AWGN);
Determining N mapping values of the LLR based on the probability density distribution; wherein N is the number of LLR values of the received symbol;
and carrying out quantization processing on the N mapping values by adopting the target signal-to-noise ratio to obtain a quantization processing result.
2. The method of claim 1, wherein the target signal-to-noise ratio is derived based on the following formula:
wherein SNR is the target SNR, E s For the average symbol energy of the transmitting end, T s For symbol period, N 0 B is the noise energy of the AWGN n Is noise broadband.
3. The method of claim 2, wherein E is s And said N 0 And (3) carrying out inversion calculation based on the bit error rate under the AWGN condition to obtain:
if the modulation mode is BPSK, the bit error rate is calculated based on the following formula:
if the modulation mode is QPSK, the error rate is calculated based on the following formula:
if the modulation mode is M-QAM, the error rate is calculated based on the following formula:
wherein, Q () is a Q function, and the calculation formula of the Q function is as follows:
wherein erfc () is a gaussian complement difference function, and the calculation formula of the gaussian complement difference function is as follows:
4. the method of claim 1, wherein the determining the N mapping values for the LLRs comprises:
Obtaining K subintervals which are continuously distributed; the K subintervals are obtained by dividing saturated bit widths of a decoder;
and mapping the LLR in the K subintervals to obtain N mapped values after mapping.
5. The method of claim 4, wherein the mapping the LLRs in the K subintervals comprises:
for a single subinterval of the K subintervals, calculating a mapping value R in the single subinterval by adopting the following formula i ＝{a i-1 ,a i Mapping value y of the LLR n I=1, 2, … K-1, then
If l is E R K ＝[a K-1 ,∞]Then y n ＝sign(x n )·y K-1 ；
Wherein x is n Sign () is x for the nth floating point value of the LLR n A is a sign function of (a) i A, for the right end point of the single interval i-1 For the left end point of the single interval, l is x n P () is the probability density distribution at the target SNR under AWGN conditions.
6. The method according to any one of claims 1-5, wherein said employing the target signal-to-noise ratio to quantize the N mapped values to obtain quantized results comprises:
acquiring a linear signal-to-noise ratio of the received symbol and the target signal-to-noise ratio;
calculating the ratio of the linear signal-to-noise ratio to the target signal-to-noise ratio, and taking the ratio as a quantization scaling factor;
And scaling the N mapping values by adopting the quantization scaling factors, and taking the scaling result as a quantization processing result.
7. The method of claim 6, wherein scaling the N mapping values with the quantization scaling factors, respectively, and taking the scaling result as a quantization processing result, comprises:
for a single mapping value of the N mapping values, performing the following processing operations:
rounding after rounding the product between the quantized scaling factor and the single mapping value, and taking the rounding result as a scaling processing result of the single mapping value;
and repeatedly executing the processing operation to obtain the scaling processing results corresponding to the N mapping values, and taking the scaling processing results as quantization processing results.
8. A quantization processing apparatus for soft bits, the apparatus comprising:
the acquisition module acquires probability density distribution of log-likelihood ratio LLR of the received symbol under the target signal-to-noise ratio; the probability density distribution comprises uncertainty probabilities corresponding to all floating point values in the LLR, and the probability density distribution is under the condition of Additive White Gaussian Noise (AWGN);
A determining module that determines N mapping values of the LLR based on the probability density distribution; wherein N is the number of LLR values of the received symbol;
and the processing module is used for carrying out quantization processing on the N mapping values by adopting the target signal-to-noise ratio to obtain a quantization processing result.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for carrying out the method steps of any one of claims 1-7 when executing a computer program stored on said memory.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-7.
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