Control and communication combined maximum posterior probability detection analysis method and device

Abstract

The invention discloses a control and communication combined maximum posterior probability detection analysis method and device. The method comprises the following steps: calculating and estimating probability density of transmission data according to a control model and a filtering algorithm; performing maximum posterior probability detection according to the probability density and the coding scheme; and analyzing the upper and lower bounds of the maximum posterior probability detection of the control and communication combination according to the probability density and the coding scheme. According to the control and communication combined maximum posterior probability detection and analysis method and device disclosed by the invention, combined detection of communication and control is realized, the transmission error probability can be reduced by utilizing the prior information of the control model, and the transmission reliability and the control performance are improved. According to the invention, the requirements of low-delay and high-reliability control scenes can be effectively met.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of control and communication technology, in particular to a maximum a posteriori probability detection analysis method and device combined with control and communication. BACKGROUND

[0002] In a traditional control system, system components are connected by point-to-point mode, which is difficult to build a complete control system or the cost of building the system is high when the system components are geographically distributed. One of the effective means to solve this problem is to use networked communication, that is, the data exchange between the core components in the system such as controlled objects, sensors, controllers, etc. is realized by network transmission, so as to achieve the purpose of remote control. The system based on this connection mode is called networked control system.

[0003] The existing networked control system is mainly designed under the conditions of network communication resources, network transmission delay, quantization error, network attack, etc. Neither the communication algorithm is used to improve the control performance, nor the control system is used to improve the communication performance. The existing networked control system still uses the design idea of separate module, which cannot realize the fusion of communication and control. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a maximum a posteriori probability detection analysis method and device combined with control and communication, which realizes the joint detection of communication and control, reduces the error probability of transmission by using the prior information of the control model, and improves the transmission reliability and control performance. The present application can effectively meet the demand of low latency and high reliability control scene.

[0005] The technical scheme adopted by the present application to achieve the above purpose is:

[0006] The maximum a posteriori probability detection analysis method combined with control and communication comprises the following steps:

[0007] 1) calculating the probability density of estimated transmission data according to the control model and the filtering algorithm;

[0008] 2) performing maximum a posteriori probability detection according to the probability density and the coding scheme of the wireless transmission system and the channel model;

[0009] 3) analyzing the upper and lower bounds of the maximum a posteriori probability detection combined with control and communication according to the probability density and the coding scheme of the wireless transmission system and the channel model.

[0010] The step 1) comprises the following steps:

[0011] 1.1) determining the state equation of the control system;

[0012] 1.2) Determine the wireless transmission system and channel model based on the control system state equation;

[0013] 1.3) Calculate the probability density of the estimated transmission data according to the control system state equation and Kalman filtering algorithm.

[0014] The step 1.2) comprises the following steps:

[0015] 1.2.1) Determine the quantization manner and the transmission bits of the control system

[0016] Uniformly quantize each element in the system output y[k] by n bits, and the quantization range is (-Z, Z), and the step size is The quantization boundaries are z l =z l-1 +Δ, l=1, 2, …, 2 n , define the midpoint of the quantization interval x∈[z l ,z l+1 ), The quantization value is represented by m, and the quantization interval index is the binary representation of m, then the quantization result of the system output y[k] with a length of N y is:

[0017]

[0018] The N y n long transmission bits of the control system are:

[0019]

[0020] 1.2.2) Encode and modulate the transmission bits

[0021] The outer code uses a CRC code of (N out , K=N y n) to encode b[k], and the inner code uses a channel code of (N in , N out ), N in represents the inner code length, N out represents the inner code information bit length, and the overall code rate R=K / N in , b[k] is encoded into a CRC code word s[k]=G out b[k], s[k] is encoded into a channel code word c[k]=G in s[k], wherein G out is a CRC code generation matrix, and G inIt is the channel coding generator matrix, which is obtained by BPSK modulation to produce the transmitted symbol t[k] = 1 - 2c[k] and the received sequence r[k] = t[k] + n[k], where n[k] is a sequence with a mean of 0 and a variance of σ. 2 Independent and identically distributed AWGN vectors.

[0022] Step 2) includes the following steps:

[0023] 2.1) Based on the transmitted bits after coding and modulation, determine the decision metric M(b) for maximum a posteriori probability detection:

[0024]

[0025] Where t(b) = 1 - 2G in G out b is the transmission sequence corresponding to b, q(b) represents the quantization sequence corresponding to b, and r T [k] represents the transpose of the received sequence r[k], t(b) represents the transmitted sequence corresponding to the quantized bit vector b, and N0 = 2σ 2 Indicates the noise variance. This represents the probability corresponding to the quantized sequence q(b). Indicates the predicted output of the control system. Let p(y) represent the covariance matrix of the control system output. The probability density function p(y) of the control system output is known to be:

[0026]

[0027] but:

[0028]

[0029] 2.2) Based on the decision metric, the maximum a posteriori probability is detected:

[0030] Traverse all Each codeword is detected based on the following criteria:

[0031]

[0032] in, This represents the result of the maximum a posteriori probability detection.

[0033] Step 3) includes the following steps:

[0034] 3.1) Calculate the lower bound of the maximum a posteriori probability detection.

[0035] Given the transmitted bit sequence b t Estimate sequence b e Not equal to sending sequence b t The probability of:

[0036]

[0037] where Q(·) denotes the standard normal distribution right tail probability function, d(c t ,c e ) denotes the Hamming distance between process variables c t and c e , and c t = G in G out b t and c e = G in G out b e ;

[0038] The error lower bound of the transmitted bit sequence b t is known as:

[0039]

[0040] The lower bound of the maximum a posteriori detection is:

[0041]

[0042] 3.2) Calculate the upper bound of the maximum a posteriori detection

[0043] The error upper bound of the transmitted bit sequence b t is known as:

[0044]

[0045] The upper bound of the maximum a posteriori detection is:

[0046]

[0047] The maximum a posteriori detection analysis device combined with control and communication includes:

[0048] The filtering module is used to calculate the probability density of the estimated transmission data according to the control model and the filtering algorithm.

[0049] The decoding module is used to perform maximum a posteriori detection according to the probability density and the encoding scheme.

[0050] The prior probability calculation module is used to analyze the upper and lower bounds of the maximum a posteriori detection combined with control and communication according to the probability density and the encoding scheme.

[0051] A control and communication-integrated maximum a posteriori probability detection and analysis system includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the control and communication-integrated maximum a posteriori probability detection and analysis method when the computer program is executed.

[0052] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned maximum a posteriori probability detection and analysis method combining control and communication.

[0053] The present invention has the following beneficial effects and advantages:

[0054] 1. This invention achieves joint detection of communication and control, and can utilize prior information from the control model to reduce the probability of transmission errors, thereby improving transmission reliability and control performance. This invention can effectively meet the requirements of low-latency and high-reliability control scenarios.

[0055] 2. The method of the present invention is particularly suitable for application in control systems in actual industrial scenarios and has good prospects for practical application. Attached Figure Description

[0056] Figure 1 Flowchart of the maximum a posteriori probability detection and analysis method for the joint control and communication provided by the present invention;

[0057] Figure 2 The maximum a posteriori probability detection and analysis device for the combined control and communication provided in this application;

[0058] Figure 3 This is a comparison chart of the bit error rate between the method of this invention and the traditional communication decoding method. Detailed Implementation

[0059] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0060] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0061] Unless otherwise defined, the technical or scientific terms used in one or more embodiments of this specification shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure pertains.

[0062] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0063] like Figure 1 As shown, the maximum a posteriori probability detection and analysis method jointly based on control and communication includes the following basic steps:

[0064] Step 1: Calculate the probability density of the estimated transmission data according to the control model and filtering algorithm:

[0065] 1.1) Determine the state equation of the control system.

[0066] For a control system, its discrete state equation is

[0067]

[0068] where x[k] is the N x dimensional system state, u[k] is the N u dimensional control input, y[k] is the N y dimensional system output, and are independent and identically distributed Gaussian white noise, W and V are covariance matrices. A is the N x ×N x system matrix, B is the N x ×N u control input matrix, and C is the N y ×N x output matrix.

[0069] 1.2) Determine the wireless transmission system and channel model.

[0070] 1.2.1) Determine the quantization method and control system transmission bits. First, each element of the system output y[k] is uniformly quantized to n bits, with a quantization range of (-Z, Z),

[0071] step size quantization boundaries are l z l-1 + Δ, l = 1, 2, …, 2 n , define the midpoint of the quantization interval x ∈ [z l , z l+1 ), quantized value, quantization interval index is the binary representation of m, then the quantization result of the N y dimensional system output y[k] is:

[0072]

[0073] The N y dimensional transmission bits of the control system are

[0074]

[0075] 1.2.2) Encode and modulate the transmission bits.

[0076] The outer code is a CRC code with (N out , K = N y ) and the inner code is a channel code with (N in , N out ) and code rate R = K / N in . b[k] is encoded into a CRC code word s[k] = G out b[k], s[k] is encoded into a channel code word c[k] = G in s[k], where G out is the CRC code generator matrix and G in is the channel code generator matrix. Each encoded bit c i [k] is modulated by BPSK to get the transmitted symbol t i [k] = 1 - 2c i [k]. The received sequence is r[k] = t[k] + n[k], where n i [k] is an independent and identically distributed AWGN with mean 0 and variance σ 2 .

[0077] 1.3) Calculate the probability density of the estimated transmitted data from the control model and the filtering algorithm.

[0078] Predict the output of the control system using Kalman filtering.

[0079] Let the initial state of the Kalman filter be the initial covariance matrix The Kalman filter estimates the system state at time k - 1 to be the covariance matrix estimate of the system state The Kalman filter predicts the state of the control system at time k to be

[0080]

[0081] The predicted output of the control system is

[0082]

[0083] The covariance matrix of the output of the control system is

[0084]

[0085] Step 2: Perform maximum a posteriori detection based on the probability density and the coding scheme:

[0086] 2.1) Determine the decision metric for maximum a posteriori detection.

[0087] The decision metric for maximum a posteriori detection is

[0088]

[0089] Where t(b) = 1 - 2G in G out b is the transmission sequence corresponding to b, and q(b) represents the quantization sequence corresponding to b. Let represent the probability corresponding to the quantized sequence q(b). The probability density function of the system output is known to be...

[0090]

[0091] Calculated by the following formula

[0092]

[0093] 2.2) Perform maximum a posteriori probability detection.

[0094] Traverse all Each codeword is detected based on the following criteria.

[0095]

[0096] Step 3: Analyze the upper and lower bounds of the maximum a posteriori probability detection for joint control and communication based on probability density and coding scheme:

[0097] 3.1) Calculate the lower bound of the maximum a posteriori probability detection.

[0098] Given the transmitted bit sequence b t Estimate sequence b e Not equal to sending sequence b t The probability is

[0099]

[0100] Where Q(·) represents the right-tail probability function of the standard normal distribution, d(b t ,b e ) represents c t and c e Hamming distance between

[0101] Given the transmitted bit sequence b t The lower bound of the error is

[0102]

[0103] The lower bound of maximum a posteriori probability detection is

[0104]

[0105] 3.2) Calculate the upper bound of the maximum a posteriori probability detection.

[0106] Given the transmitted bit sequence b t The upper bound of the error is

[0107]

[0108] The upper bound of maximum a posteriori probability detection is

[0109]

[0110] The embodiments provided in this application above describe the maximum a posteriori probability detection and analysis method for joint control and communication from the perspective of communication and control fusion. It is understood that the above method can be implemented in various network elements. Each network element, such as a terminal, base station, or control node, includes corresponding hardware structures and / or software modules to perform the above functions. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0111] Figure 2 The control and communication combined maximum a posteriori probability detection and analysis apparatus provided in this application includes:

[0112] Transceiver S201: Transmitter and receiver, used for sending and receiving signals;

[0113] Controller / processor S202: Used for filtering, decoding, and calculating prior probabilities;

[0114] Memory S203: Used to store calculation results;

[0115] Communication interface S204: Used to support communication between the control and communication combined maximum a posteriori probability detection and analysis device and other network entities.

[0116] The control and communication combined maximum a posteriori probability detection and analysis device provided in this application can perform the method steps of the previous embodiments, and will not be repeated here.

[0117] The functions of the controller / processor S202 described above can be implemented by circuitry or by executing software code using general-purpose hardware. When the latter is used, the memory S203 is also used to store program code that can be executed by the controller / processor S202. When the controller / processor S202 runs the program code stored in the memory S203, it performs the aforementioned functions.

[0118] It can be understood that, Figure 2 Only a simplified design of the control and communication combined maximum a posteriori probability detection analysis device is shown. In actual applications, the device can include any number of transceivers, controllers / processors, memories, and / or communication interfaces, etc.

[0119] The present application has been simulated for a plurality of simulation embodiments, and the following describes the implementation process and performance analysis of the present application in detail based on the simulation embodiment test results:

[0120] Figure 3 The following is a comparison chart of the method of the present application and the conventional communication decoding method in terms of bit error rate, with a communication configuration of polar code with a code length of 64, an information bit length of 16, a CRC length of 16, a quantization range of -5 to 5, a quantization bit number of 16, and W=V=10. -7 The control method is LQG control, and the control system model is

[0121]

[0122] The present application has a performance gain of about 3.0 dB compared to the conventional ML decoding at a bit error rate of 10 -3 .

[0123] The above device can be a terminal or a network side device in specific implementation. The network side device can be a base station or a control node.

[0124] The controller / processor of the above base station, terminal, or control node of the present application can be a central processing unit (CPU), a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in combination with the disclosure of the present application. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0125] The steps of a method or algorithm described in connection with the present disclosure can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. Alternatively, the processor and the storage medium can be located in a terminal. The processor and the storage medium can comprise any combination of a microprocessor, a microcontroller, a processor, a controller, a microcomputer, a digital signal processor, a central processing unit (CPU), a graphics processing unit (GPU), a state machine, and / or any other device suitable for reading information from, and writing information to, a storage medium.

[0126] Those skilled in the art can realize that the functions described in the one or more examples described above can be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions can be stored in or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. Storage media can be any available media that can be accessed by a general purpose or special purpose computer.

[0127] The above detailed description has disclosed, by way of example, illustrative implementations of the present application. It is understood that modifications and / or changes can be suggested by those skilled in the art, and that the above described embodiments are merely illustrative of the principles of the present application. Thus, it is intended that the scope of the present application extend not only to the embodiments disclosed in this document, but to all such modifications and / or changes that come within the scope of the appended claims.

Claims

1. A maximum a posteriori probability detection and analysis method combining control and communication, characterized in that, Includes the following steps: 1) Calculate and estimate the probability density of the transmitted data based on the control model and filtering algorithm; 2) Perform maximum a posteriori probability detection based on probability density and the coding scheme of the wireless transmission system and channel model; 3) Analyze the upper and lower bounds of the maximum a posteriori probability detection for joint control and communication based on the probability density and the coding scheme of the wireless transmission system and channel model.

2. The maximum a posteriori probability detection and analysis method for joint control and communication according to claim 1, characterized in that, Step 1) includes the following steps: 1.1) Determine the state equations of the control system; 1.2) Based on the state equations of the control system, determine the wireless transmission system and channel model; 1.3) Calculate the probability density of the transmitted data based on the state equation of the control system and the Kalman filter algorithm.

3. The maximum a posteriori probability detection and analysis method for joint control and communication according to claim 2, characterized in that, Step 1.2) Includes the following steps: 1.2.1) Determine the quantization method and the transmission bits of the control system. Each element in the system output y[k] is subjected to n-bit uniform quantization, with a quantization range of (-Z, Z) and a step size of [missing value]. Quantization boundaries are respectively z l =z l-1 +Δ, l=1,2,…,2 n Define the midpoint of the quantization interval. x∈[z l ,z l+1 ), Represents the quantized value, quantization interval index If m is the binary representation, then the length is N. y The quantization result of the system output y[k] is: N of the control system y The length of n bits to be sent is: 1.2.2) Encode and modulate the transmitted bits. Foreign code uses (N) out K = N y The CRC code of n) is used to encode b[k], and the internal code uses (N) in N out Channel coding of N in N represents the internal code length. out This represents the length of the internal code information bits, and the overall code rate R = K / N in b[k] is encoded into CRC codeword s[k] = G out b[k], s[k] are encoded into channel coding codewords c[k] = G in s[k], where G out It is the CRC code generation matrix, G in It is the channel coding generator matrix, which is obtained by BPSK modulation to produce the transmitted symbol t[k] = 1 - 2c[k] and the received sequence r[k] = t[k] + n[k], where n[k] is a sequence with a mean of 0 and a variance of σ. 2 Independent and identically distributed AWGN vectors.

4. The maximum a posteriori probability detection and analysis method for joint control and communication according to claim 1, characterized in that, Step 2) includes the following steps: 2.1) Based on the transmitted bits after coding and modulation, determine the decision metric M(b) for maximum a posteriori probability detection: Where t(b) = 1 - 2G in G out b is the transmission sequence corresponding to b, q(b) represents the quantization sequence corresponding to b, and r T [k] represents the transpose of the received sequence r[k], t(b) represents the transmitted sequence corresponding to the quantized bit vector b, and N0 = 2σ 2 Indicates the noise variance. This represents the probability corresponding to the quantized sequence q(b). Indicates the predicted output of the control system. Let p(y) represent the covariance matrix of the control system output. The probability density function p(y) of the control system output is known to be: but: 2.2) Based on the decision metric, the maximum a posteriori probability is detected: Traverse all Each codeword is detected based on the following criteria: in, This represents the result of the maximum a posteriori probability detection.

5. The maximum a posteriori probability detection and analysis method for joint control and communication according to claim 1, characterized in that, Step 3) includes the following steps: 3.1) Calculate the lower bound of the maximum a posteriori probability detection. Given the transmitted bit sequence b t Estimate sequence b e Not equal to sending sequence b t The probability of: Where Q(·) represents the right-tail probability function of the standard normal distribution, d(c t ,c e ) represents the process variable c t and c e The Hamming distance between them, and c t =G in G out b t and c e =G in G out b e ; Given the transmitted bit sequence b t The lower bound of the error is: The lower bound for maximum a posteriori probability detection is: 3.2) Calculate the upper bound of the maximum a posteriori probability detection. Given the transmitted bit sequence b t The upper bound of the error is: The upper bound for maximum a posteriori probability detection is:

6. A maximum a posteriori probability detection and analysis device combining control and communication, characterized in that, include: The filtering module is used to calculate the probability density of the transmitted data based on the control model and filtering algorithm. The decoding module is used to perform maximum a posteriori probability detection based on the probability density and the encoding scheme; The prior probability calculation module is used to analyze the upper and lower bounds of the maximum a posteriori probability detection of the joint control and communication based on the probability density and coding scheme.

7. A maximum a posteriori probability detection and analysis system combining control and communication, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement, when executing the computer program, a maximum a posteriori probability detection and analysis method for control and communication as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements a maximum a posteriori probability detection and analysis method combining control and communication as described in any one of claims 1-5.

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