CN113727283B - Zero-delay correlated information source broadcast communication method - Google Patents
Zero-delay correlated information source broadcast communication method Download PDFInfo
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- CN113727283B CN113727283B CN202110578446.2A CN202110578446A CN113727283B CN 113727283 B CN113727283 B CN 113727283B CN 202110578446 A CN202110578446 A CN 202110578446A CN 113727283 B CN113727283 B CN 113727283B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0009—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the channel coding
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0014—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the source coding
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention provides a zero-delay related information source broadcast communication method, which comprises the following steps: inputting a pair of memoryless and stable Gaussian related information sources into an encoder, mapping the information sources into two paths of independent channel symbols by the encoder, and then respectively outputting the two paths of independent channel symbols; after the Gaussian white noise signal is respectively superposed on the channel symbol output by each channel, transmitting the channel symbol to a 1-bit analog-to-digital converter for processing; after the 1-bit analog-to-digital converter is used for processing, the processed signal is transmitted to a decoder, the decoder reconstructs information source information at a user side, a first user pays attention to the reconstruction of a first information source, and a second user pays attention to the reconstruction of a second information source; and judging the distortion performance of the system according to the reconstructed information source information. The invention can improve the distortion performance of the system through the non-parametric mapping algorithm.
Description
Technical Field
The invention relates to the field of communication, in particular to a zero-delay related information source broadcast communication method.
Background
Modern wireless communication systems achieve reliable transmission of certain high-rate content types, such as Joint Photographic Experts Group (JPEG) and Moving Picture Experts Group (MPEG), by utilizing channel coding and highly optimized compression algorithms that approach channel capacity. However, many emerging applications, such as internet of things (IOT) or machine-to-machine communication (M2M), further limit the cost and complexity of the communication device, or place higher requirements on available energy and end-to-end delay, which makes many known coding methods and modulation techniques unsuitable. For example, in time-sensitive control applications, such as monitoring power line faults in smart grids, the underlying signal should be measured and transmitted to the receiving end within very small time delay constraints. In the above case, we can neither improve compression efficiency by measuring multiple signals nor approach channel capacity by using the channel multiple times.
Broadcast communication is a communication means of transmitting information from a central node to a plurality of devices using a common channel. Broadcast communication is applied in many scenarios, such as the downlink of cellular systems, or Wireless Sensor Networks (WSNs), whereby communication between a control node and a large number of sensors is controlled. One solution for reliably transmitting information over a broadcast channel is based on a mechanism of source-channel separation, i.e. source coding and channel coding are separately optimized.
Generally, a coding scheme based on source-channel separation can provide near-optimal performance, but at the same time, the scheme has certain disadvantages. On one hand, coding schemes based on source-channel separation require the encoding end to use long codewords to approach the theoretical optimum, but this results in increased coding complexity and higher latency. On the other hand, since the coding rate depends on the channel condition, the system needs to be redesigned in a time-varying environment so as to match the encoder to the channel condition. Therefore, coding schemes based on source-channel separation are generally not optimal in a multi-user environment.
Disclosure of Invention
The embodiment of the invention provides a zero-delay related information source broadcast communication method, which comprises the following steps:
step 1, inputting a pair of memoryless and stable Gaussian related information sources into an encoder, mapping the information sources into two paths of independent channel symbols by the encoder, and then respectively outputting the symbols;
step 2, after the Gaussian white noise signal is respectively superposed on the channel symbols output by each path, transmitting the channel symbols to a 1-bit analog-to-digital converter for processing;
step 3, after the 1-bit analog-to-digital converter processes the signals, the processed signals are transmitted to a decoder, the decoder reconstructs information source information at a user side, a first user pays attention to reconstruction of a first information source, and a second user pays attention to reconstruction of a second information source;
and 4, judging the distortion performance of the system according to the reconstructed information source information.
Wherein, the step 1 specifically comprises:
the encoder receives a pair of memoryless and stationary Gaussian correlation sources (X) 1 ,X 2 ) The mean value of the source is zero and the variance is
The encoder outputs V, receives a pair of source symbolsMapping a channel symbol as output to realize 2:1 compression coding, wherein the output of the coder needs to meet the power limit;
E[||α(X 1 ,X 2 )|| 2 ]≤P (1)。
wherein, the step 2 specifically comprises:
at the front end of the decoder, the 1-bit analog-to-digital converter outputs Y to the channel i Quantization is performed, and the quantization process is expressed as:
wherein, the step 3 specifically comprises:
the decoder employs a minimum mean square error estimator, which is represented by the following equation:
where p (-) represents a probability density function.
Wherein the step 4 comprises:
taking the average mean square error of two users as a performance evaluation standard, wherein the average mean square error is represented by the following formula:
the method further comprises optimizing a variable sign scalar quantizer linear encoder by using a non-parametric mapping algorithm, and obtaining optimized encoding mapping, specifically comprising:
initializing the coding mapping, and obtaining the estimated signals of two users under the initial coding by using a minimum mean square error decoder
Calculating the cost function value J of the system in the initial state 0 And setting a counter k to be 0;
updating the counter k to k +1, and keeping the decoding mapping beta 1 And beta 2 Updating the coding mapping alpha without changing;
keeping the encoding mapping alpha unchanged and updating the decoding mapping beta 1 And beta 2 ；
Updating the cost function value J k Calculate (J) k-1 -J k )/J k-1 If the ratio is smaller than a preset threshold value, ending the algorithm; otherwise, the counter k is updated again to k + 1.
The scheme of the invention has the following beneficial effects:
the zero-delay related information source broadcast communication method described in the above embodiment of the present invention combines a zero-delay related information source broadcast communication system of a 1-bit analog-to-digital converter, reduces the power requirement of the communication system by using the 1-bit analog-to-digital converter, calculates the distortion performance achieved by optimized coding mapping in the system by using a non-parametric mapping algorithm, and obtains a gain of 0.2dB basically for the average distortion of a user side under different channel signal-to-noise ratios (CSNR) compared to a scheme of a linear encoder of a variable-sign scalar quantizer.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a flow chart of the non-parametric mapping algorithm of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides a zero-latency correlated source broadcast communication method, including: step 1, inputting a pair of memoryless and stable Gaussian related information sources into an encoder, mapping the information sources into two paths of independent channel symbols by the encoder, and then respectively outputting the symbols; step 2, after the Gaussian white noise signal is respectively superposed on the channel symbols output by each path, transmitting the channel symbols to a 1-bit analog-to-digital converter for processing; step 3, after the 1-bit analog-to-digital converter processes the signals, the processed signals are transmitted to a decoder, the decoder reconstructs information source information at a user side, a first user pays attention to reconstruction of a first information source, and a second user pays attention to reconstruction of a second information source; and 4, judging the distortion performance of the system according to the reconstructed information source information.
The zero-delay correlated information source broadcast communication system of the 1-bit analog-to-digital converter of the invention is shown in the following figure 1, and the correlated information source (X) 1 ,X 2 ) The coded output is transmitted to a broadcast channel via an encoder, superimposed with white Gaussian noise, wherein the noise N of channel 1 1 Noise N stronger than channel 2 2 Numerically expressed as N 1 Is greater than N 2 The variance of (a) is determined,
the noise signals of the two channels respectively pass through a 1-bit analog-to-digital converter. The 1-bit analog-to-digital converter output is denoted as Z i 。Z i Reconstructing source information at a user side by a decoder, wherein user 1 only concerns source X 1 User 2 only concerns the source X 2 And (4) reconstructing. System performance is represented by the mean squared error of the two users.
Wherein, the step 1 specifically comprises: the encoder receives a pair of memoryless and stationary Gaussian correlation sources (X) 1 ,X 2 ) The mean value of the source is zero and the variance is
The output of the coder is V, the coder receives a pair of information source symbols and maps a channel symbol as the output, 2:1 compression coding is realized, and the output of the coder needs to meet the power limit;
E[||α(X 1 ,X 2 )|| 2 ]≤P (1)。
the encoder of the present invention receives a pair of memoryless and stationary Gaussian correlation sources (X) 1 ,X 2 ) The mean value of the source is zero and the variance is
The encoder output is V. Encoder alpha receives a pair of source symbols for mappingOne channel symbol is taken as output, and 2:1 compression coding is realized.
Wherein, the step 2 specifically comprises: at the front end of the decoder, the 1-bit analog-to-digital converter outputs Y to the channel i Quantization is performed, and the quantization process is expressed as:
wherein, the step 3 specifically comprises: the decoder employs a minimum mean square error estimator, which is represented by the following equation:
where p (-) represents a probability density function.
The system of the invention aims to minimize the total mean-square error of the estimated signal values and the source signal values of the two clients, the decoder beta i A Minimum Mean Square Error (MMSE) estimator is employed.
Wherein the step 4 comprises: taking the average mean square error of two users as a performance evaluation standard, wherein the average mean square error is represented by the following formula:
the method further comprises optimizing a variable sign scalar quantizer linear encoder by using a non-parametric mapping algorithm, and obtaining optimized encoding mapping, specifically comprising: initializing the coding mapping, and obtaining the estimated signals of two users under the initial coding by using the minimum mean square error decoder
Calculating the cost function value J of the system in the initial state 0 And setting a counter k to be 0; updating the counter k to k +1, and keeping the decoding mapping beta 1 And beta 2 Updating the coding mapping alpha without changing; keeping the encoding mapping alpha unchanged and updating the decoding mapping beta 1 And beta 2 (ii) a Updating the cost function value J k Calculating (J) k-1 -J k )/J k-1 If the ratio is smaller than a preset threshold value, ending the algorithm; otherwise, the counter k is updated again to k + 1.
The non-parametric mapping algorithm of the invention constructs the Lagrangian cost function, which is shown in formula 5,
λ represents the lagrange multiplier used to tie the constraint function and the objective function together. For a given λ, if the solution that minimizes equation 5 also satisfies the average power limit of equation 1, then the solution will also be a solution with constraint problems. For a given λ, a certain code mapping α can be obtained according to the algorithm flow of fig. 2. And calculating the actual power corresponding to the code mapping alpha. If the actual power E [ | | | α (X) 1 ,X 2 )|| 2 ]If the power is greater than the limit power P, increasing lambda and executing the experimental process of the figure 2 again; if E [ | | α (X) 1 ,X 2 )|| 2 ]Less than the limit power P, λ is decreased and the experimental procedure of fig. 2 is re-executed.
Observing the structure of formula 5, formula 5 includes two parts of encoding and decoding, and the two parts to be optimized are interdependent, therefore, the invention deals with the problem by an iterative optimization method, and only one part is optimized in each step while the other part is kept unchanged in the optimization process.
Assume a decoded mapping (β) 1 ,β 2 ) The fixed, optimal code mapping α is represented by equation 6:
wherein the decoding distortion MSE of each user i And transmitting end actual power P act Expressed by equation 7 and equation 8, respectively:
MSE i ＝∫∫∫p(x 1 ,x 2 ,z i )×(x i -β i (z i )) 2 dz i dx 2 dx 1
＝∫∫∫p(x 1 ,x 2 )p(z i |x 1 ,x 2 )×(x i -β i (z i )) 2 dz i dx 2 dx 1
＝∫∫∫p(x 1 ,x 2 )p(z i |α(x 1 ,x 2 ))×(x i -β i (z i )) 2 dz i dx 2 dx 1
P act ＝∫∫p(x 1 ,x 2 )α(x 1 ,x 2 ) 2 dx 2 dx 1
looking at equations 7 and 8, due to the joint probability p (x) 1 ,x 2 ) Non-negative, we can rewrite equation 6 to equation 9:
p(x 1 ,x 2 ) Representing a joint probability density function. According to Bayes' principle, p (x) 1 ,x 2 )＝p(x 1 )p(x 2 |x 1 )。p(x 1 ) Representing a source component X 1 P (x) as a function of the probability density 2 |x 1 ) Representing a known source component X 1 Conditional probability density function of. p (x) 1 ,x 2 ) Omitted in equation 9.
p(x 1 ) And p (x) 2 |x 1 ) Is defined as:
assuming fixed encoding ends, the optimal decoding method is to give the ADC output z i Last pair x i The decoding mapping is represented by equation 3.
The invention modifies and approximates the algorithm and generates a set of Monte Carlo samples according to the distribution probability characteristics of the information source. The channel input is represented by a finite set of discrete points, symbolized by
And (4) showing. Collection
Respectively expressed as
Where L determines the number of discrete points in the discrete set and d determines the accuracy of the discrete set. Discrete sets with decreasing precision d and increasing value L
Gradually approximating the analog continuum.
After the discretization operation, the encoding update formula 9 is modified into formula 10, and formula 3 is respectively expressed as formula 11 and formula 12:
v＝α(x 1 ,x 2 )，Pr(z 1 | v) represents z obtained after known coding mapping output 1 A priori probability of.
Pr(z 1 |v)＝Pr(z 1 |α(x 1 ,x 2 ))。
In z 1 0, i.e. Y 1 For example, if not less than 0, we have Y 1 ＝α(x 1 ,x 2 )+N 1 Not less than 0, i.e. N 1 ≥-α(x 1 ,x 2 )
The method described in the above embodiments of the present invention uses the variable sign scalar quantizer linear encoder scheme as the contrast scheme and takes this scheme as the initialization function of the algorithm of the present invention; the symbol-variable scalar quantizer linear encoder scheme is a parameterized zero-delay encoding scheme, which is proposed and applied to a zero-delay correlated source broadcast communication system, and the parameterized mapping scheme has the advantages that the structure of encoding mapping is fixed, and any point in a source space can be directly mapped into a corresponding channel input symbol. The parameterized map may update the map by adjusting parameters of the map according to signal properties and channel conditions. Meanwhile, the optimization of the linear encoder of the variable-sign scalar quantizer is realized by applying a non-parametric mapping algorithm. The non-parametric mapping algorithm expresses necessary conditions of optimal code mapping through discretization operation, and the optimized code mapping is obtained based on joint optimization and iteration between the mapping of the transmitting end and the mapping of the receiving end. The advantage of non-parametric mapping is that a near-optimal coding mapping can be found given the a-priori distribution of source symbols.
While the foregoing is directed to the preferred embodiment of the present invention, it will be appreciated by those skilled in the art that various changes and modifications may be made therein without departing from the principles of the invention as set forth in the appended claims.
Claims (5)
1. A zero-delay correlation source broadcast communication method, comprising:
step 1, a pair of memoryless and stable Gaussian related information sources (X) 1 ，X 2 ) The input coder is mapped into two paths of independent channel symbols by the coder and then respectively output;
optimizing a variable sign scalar quantizer linear encoder by using a non-parametric mapping algorithm to obtain optimized encoding mapping, specifically comprising:
initializing the coding mapping, and obtaining the estimated signals of two users under the initial coding by using a minimum mean square error decoder
Calculating the cost function value J of the system in the initial state 0 And setting a counter k to be 0;
the cost function of the system is calculated by
λ represents the lagrange multiplier;
updating the counter k to k +1, and keeping the decoding mapping beta 1 And beta 2 Updating the coding mapping alpha without changing;
assume a decoded mapping (β) 1 ，β 2 ) The fixed, optimal code mapping α is represented by:
keeping the coding mapping alpha unchanged, updating the decoding mapping beta 1 And beta 2 ；
Assuming fixed encoding ends, the optimal decoding method is to give the ADC output z i Last pair x i Is smallest and allSquare error estimation;
updating the cost function value J k Calculating (J) k-1 -J k )/J k-1 If the ratio is smaller than a preset threshold value, ending the algorithm; otherwise, the counter k is updated again to k + 1;
step 2, after the Gaussian white noise signal is respectively superposed on the channel symbols output by each path, transmitting the channel symbols to a 1-bit analog-to-digital converter for processing;
step 3, after the 1-bit analog-to-digital converter processes the signals, the processed signals are transmitted to a decoder, the decoder reconstructs information source information at a user side, a first user pays attention to reconstruction of a first information source, and a second user pays attention to reconstruction of a second information source;
and 4, judging the distortion performance of the system according to the reconstructed information source information.
2. The method for zero-latency correlated source broadcast communication according to claim 1, wherein the step 1 specifically includes:
the encoder receives a pair of memoryless and stationary Gaussian correlation sources (X) 1 ，X 2 ) The mean value of the source is zero and the variance is
The output of the encoder is V, the encoder receives a pair of information source symbols and maps a channel symbol as output, 2:1 compression coding is realized, and the output of the encoder needs to meet power limitation;
E[||α(X 1 ，X 2 )|| 2 ]≤P (1)
where α is the encoding map of the encoder.
3. The method for zero-latency correlated source broadcast communication according to claim 1, wherein the step 2 specifically includes:
at the front end of the decoder, the 1-bit analog-to-digital converter outputs Y to the channel i Quantization is performed, and the quantization process is expressed as:
4. the method for zero-latency correlated source broadcast communication according to claim 1, wherein the step 3 specifically includes:
the decoder employs a minimum mean square error estimator, which is represented by the following equation:
wherein z is i Is the output of a 1-bit analog-to-digital converter, alpha is the coding map of the encoder, p (x) 1 ，x 2 ) Representing a joint probability density function, p (z) i |α(x 1 ，x 2 ) Represents a known alpha (x) 1 ，x 2 ) Conditional probability density function of (a).
5. The method for zero-latency correlated source broadcast communication according to claim 1, wherein the step 4 comprises:
taking the average mean square error of two users as a performance evaluation standard, wherein the average mean square error is represented by the following formula:
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