Random access network information timeliness optimization method based on information correlation

Abstract

The invention discloses a random access network information timeliness optimization method based on information correlation. The method specifically comprises the following steps: 1) constructing a wireless communication network model comprising a plurality of information sources and base stations; 2) defining a correlation matrix between the information sources, and describing an information sharing probability between the information sources; 3) deriving an expression of network average AoI; and 4) using an optimization algorithm to jointly consider the information correlation and the random access probability, and minimizing the average AoI of the network. Simulation results show that compared with a traditional random access strategy ignoring information correlation, the method of the invention significantly reduces the average AoI of the network, and improves the timeliness and stability of information transmission. The method is suitable for time-delay-sensitive wireless communication scenes such as the Internet of Things and video monitoring.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication network optimization, and in particular to a method for optimizing the timeliness of random access network information based on information correlation. Background Art

[0002] With the rapid development of IoT and smart devices, the demand for large-scale terminal access is increasing, and random access has become the main technology to support data transmission of these devices. Random access technology allows terminals to directly transmit data without central coordination, which improves the flexibility and scalability of the network. However, facing delay-sensitive application scenarios, existing random access strategies have many challenges, such as increased packet collisions, decreased throughput, and increased transmission delays, which affect the timeliness of data. Information timeliness is one of the key indicators for measuring network performance. AoI describes the time from the generation to the reception of the latest received information and is an important parameter for evaluating the timeliness of data updates. Most of the current random access optimization methods ignore the spatiotemporal correlation between information sources and usually assume that each information source is updated independently. This assumption is not accurate in practical applications because adjacent information sources, such as video surveillance cameras, sensor nodes, etc., usually have spatial overlap or information sharing characteristics, resulting in strong correlation in the data they transmit. Optimization methods that ignore information correlation fail to fully utilize the potential of information sharing, and thus cannot achieve optimal control of the average AoI of the network. Summary of the invention

[0003] Purpose of the invention: The present invention aims to solve the problem of insufficient information timeliness caused by ignoring the relevance of information sources in random access networks, and proposes a method for optimizing access probability. By constructing an information relevance matrix and using a sequential quadratic programming algorithm, the average AoI of the network is minimized, thereby improving the timeliness and stability of data updates.

[0004] Technical solution: To achieve the above-mentioned invention object, the present invention adopts a random access network information timeliness optimization method based on information correlation, characterized in that the method comprises the following steps:

[0005] Step 1) establishing a random access network model including multiple information sources and a base station, wherein each information source performs random access communication through a shared spectrum resource, and data packet transmission is driven by a time slot;

[0006] Step 2) According to the spatial distribution of information sources and the information sharing characteristics between adjacent sources, an information correlation matrix is ​​constructed, and each element in the matrix describes the sharing probability between information sources;

[0007] Step 3) Based on the information relevance matrix, analyze the impact of the sharing relationship between information sources on the change of information age of information sources, and establish a mathematical model of the average information age of the network;

[0008] Step 4) With the goal of minimizing the average information age of the network, an algorithm based on sequential quadratic programming is used to solve the mathematical model to obtain the optimal access probability of each information source.

[0009] Further, the step 1) comprises the following steps:

[0010] Step 1.1) Construct a random access network model consisting of N information sources and one base station, where each information source independently decides whether to send a data packet to the base station in a given time slot. i With access probability τ i Initiate transmission, all information sources share the same wireless channel resources; in a certain time slot, if only one information source successfully sends a data packet, the base station can correctly receive the data packet and update the status of the information source; if multiple information sources send at the same time, a collision occurs, resulting in the data packet cannot be correctly received; the transmission success rate of the information source is η i It indicates the probability that the channel successfully transmits data in the current time slot;

[0011] Step 1.2) Define the information age AoI as the time difference from information generation to successful reception by the base station. The information source S i The expression of the information age at time slot t is:

[0012] A i (t) = tt suc

[0013] Among them, t suc Indicates the information source S i The time slot of the last successful data packet transmission;

[0014] Step 1.3) At time slot t, depending on whether the transmission is successful, the information age of the information source is updated according to the following rules:

[0015]

[0016] When the source successfully sends a packet, the message age is reset to 1; otherwise, the message age is incremented in the next time slot.

[0017] Further, the step 2) comprises the following steps:

[0018] Step 2.1) Calculate the sharing probability between information sources based on the physical distance and other relevant characteristics between information sources to quantify the relevance of information sources. The calculation formula of sharing probability is as follows:

[0019] p ij =ω i exp(-kL i,j)

[0020] Among them, ω i ∈[0,1], indicating the information source S i The weight factor reflects its importance in the network; k is the correlation coefficient, which is used to describe the rate at which the sharing probability decays with distance; L i,j Information source S i and S j The distance between

[0021] Step 2.2) Construct the information correlation matrix P, which is asymmetric, where the element p ij Indicates the information source S i With information source S j The shared probability between them is in the following matrix form:

[0022]

[0023] Among them, the diagonal elements p in the matrix ii Set to 1, indicating that the information source is fully aware of its own state;

[0024] Step 2.3) Use the information correlation matrix P as the input of the subsequent access probability optimization process to provide a quantitative description of the sharing relationship between information sources to support the formulation of optimization decisions.

[0025] Further, step 3) comprises the following steps:

[0026] Step 3.1) Based on the constructed information relevance matrix, the changing law of information age is combined with the sharing probability to establish the objective function of the optimization problem. The goal is to minimize the average information age of the network to reduce the overall information update delay. The objective function of the optimization problem and its constraints are as follows:

[0027]

[0028] Constraints:

[0029]

[0030] in, Indicates the average age of information on the network.

[0031] Further, the step 4) comprises the following steps:

[0032] Step 4.1), initialize the access probability vector in, Indicates the information source S i The initial access probability is calculated to calculate the value function V of each information source. i , which is defined as follows:

[0033] Among them, V i Indicates the information source S i The value of p ij is the sharing probability, η j Information source S j The transmission success rate is , and the M information sources with the highest value are selected according to the value function. The initial access probability of these information sources is set to The access probability of other information sources is set to 0;

[0034] Step 4.2), set the iteration parameters, initialize the number of iterations k = 0, and set the maximum number of iterations K max , step size adjustment parameter range and convergence threshold ε to control the computational complexity of the optimization process, set the initial Hessian matrix H 0 =I, where I is the identity matrix;

[0035] Step 4.3), enter the iteration loop until the maximum number of iterations is reached or the convergence condition is met. At the kth iteration, a quadratic programming QP subproblem is constructed to determine the update direction S. The standard form of the QP subproblem is:

[0036] Constraints:

[0037]

[0038] in:

[0039] is the access probability vector of the current iteration step, where Indicates the information source S i Access probability at the kth iteration;

[0040] f(·) is the objective function defined in step 3.1) to minimize the average information age of the network;

[0041] is the gradient of the objective function, describing its rate of change at a certain point;

[0042] S is the update direction vector, which indicates the direction of movement from the current iteration point to the next iteration;

[0043] H k It is the Hessian matrix, which is used to describe the second-order derivative information of the objective function and to find the optimal direction more accurately;

[0044] g i(·) is the i-th constraint function, which describes the physical constraints of the access probability. m = 2N means there are 2N constraint functions, as follows: For i = 1, ..., N, the form of the constraint function is: To ensure that the access probability is non-negative, for i=N+1,…,2N, the constraint function is in the form of: Ensure that the access probability does not exceed 1;

[0045] is the gradient of the constraint function, describing its rate of change at a certain point;

[0046] T represents the transpose operation of a matrix or vector, which is used to convert a column vector into a row vector, or to convert a row vector into a column vector;

[0047] Step 4.4), use the Active Set method or the Interior Point method to solve the quadratic programming subproblem and obtain the update direction S;

[0048] Step 4.5), perform step size adjustment and determine the step size parameter vector β by line search method k , the step size parameter vector β k The elements in are positive numbers, which are used to control the distance moved from the current iteration point along the update direction S, so that the value of the objective function in the update direction decreases. The update formula of the access probability is: τ k+1 =τ k +β k S;

[0049] Step 4.6), use the BFGS algorithm to update the Hessian matrix H k , and its update formula is:

[0050]

[0051] in:

[0052] Δτ k+1 =τ k+1 -τ k is the change of access probability vector;

[0053] λ i is the Lagrange multiplier, which is used to measure the importance of each constraint in the current optimization solution, that is, it represents the weight of the constraint in the optimization process;

[0054] Step 4.7) The convergence of the optimization algorithm is judged based on any of the following three conditions. Once any of the three conditions is met, the optimization process is considered to have reached convergence:

[0055] Gradient norm: If It is considered to have reached convergence;

[0056] Objective function improvement: If the change of the objective function between two consecutive iterations is less than the set threshold, the iteration is stopped;

[0057] Maximum number of iterations: If the maximum number of iterations K is reached max , then stop the iteration;

[0058] Step 4.8), if the above convergence conditions are not met, set k = k + 1 and return to step 4.3) to continue the next iteration; otherwise, output the final optimal access probability combination τ * , which is applied to the access strategy of information sources to minimize the average information age of the network.

[0059] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0060] (1) The present invention proposes a random access probability optimization method based on information relevance. By constructing an information relevance matrix, analyzing the impact of information sharing on AoI, and using an optimization algorithm based on sequential quadratic programming (SQP), the average AoI of the network is significantly reduced, and the timeliness and reliability of data transmission are improved. Intelligent transportation systems (ITS) are an important part of future smart cities. Autonomous driving vehicle platooning technology is a very promising ITS service. Vehicles can effectively increase road capacity and reduce traffic congestion by platooning; they can also effectively save energy consumption such as fuel.

[0061] (2) In addition, platooning can also improve the comfort of passengers, especially during long-distance travel. Compared with the traditional random access strategy, the present invention can make full use of the correlation between information sources, reduce transmission conflicts, and improve resource utilization efficiency. The adopted SQP algorithm has the characteristics of fast convergence and strong stability, can dynamically adapt to different scenarios, and is suitable for delay-sensitive application scenarios such as the Internet of Things and video surveillance, achieving simple and efficient optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a specific flow chart of a method for optimizing the timeliness of random access network information based on information relevance in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention by those skilled in the art fall within the scope defined by the claims attached to this application. The examples of the present invention analyze in detail the random access network information timeliness optimization method based on information relevance in combination with specific scenarios, but the method of the present invention is not limited to the specific scenarios and parameter settings in the examples of the present invention.

[0064] like Figure 1 As shown, the present invention adopts a random access network information timeliness optimization method based on information correlation, characterized in that the method comprises the following steps:

[0065] Step 1) establishing a random access network model including multiple information sources and a base station, wherein each information source performs random access communication through a shared spectrum resource, and data packet transmission is driven by a time slot;

[0066] Step 2) According to the spatial distribution of information sources and the information sharing characteristics between adjacent sources, an information correlation matrix is ​​constructed, and each element in the matrix describes the sharing probability between information sources;

[0067] Step 3) Based on the information relevance matrix, analyze the impact of the sharing relationship between information sources on the change of information age of information sources, and establish a mathematical model of the average information age of the network;

[0068] Step 4) With the goal of minimizing the average information age of the network, an algorithm based on sequential quadratic programming is used to solve the mathematical model to obtain the optimal access probability of each information source.

[0069] Further, the step 1) comprises the following steps:

[0070] Step 1.1) Construct a random access network model consisting of N information sources and one base station, where each information source independently decides whether to send a data packet to the base station in a given time slot. i With access probability τ i Initiate transmission, all information sources share the same wireless channel resources; in a certain time slot, if only one information source successfully sends a data packet, the base station can correctly receive the data packet and update the status of the information source; if multiple information sources send at the same time, a collision occurs, resulting in the data packet cannot be correctly received; the transmission success rate of the information source is η i It indicates the probability that the channel successfully transmits data in the current time slot;

[0071] Step 1.2) Define the information age AoI as the time difference from information generation to successful reception by the base station. The information source S i The expression of the information age at time slot t is:

[0072] A i (t) = tt suc

[0073] Among them, t suc Indicates the information source S i The time slot of the last successful data packet transmission;

[0074] Step 1.3) At time slot t, depending on whether the transmission is successful, the information age of the information source is updated according to the following rules:

[0075]

[0076] When the source successfully sends a packet, the message age is reset to 1; otherwise, the message age is incremented in the next time slot.

[0077] Further, the step 2) comprises the following steps:

[0078] Step 2.1) Calculate the sharing probability between information sources based on the physical distance and other relevant characteristics between information sources to quantify the relevance of information sources. The calculation formula of sharing probability is as follows:

[0079] p ij =ω i exp(-kL i,j )

[0080] Among them, ω i ∈[0,1], indicating the information source S i The weight factor reflects its importance in the network; k is the correlation coefficient, which is used to describe the rate at which the sharing probability decays with distance; L i,j Information source S i and S j The distance between

[0081] Step 2.2) Construct the information correlation matrix P, which is asymmetric, where the element p ij Indicates the information source S i With information source S j The shared probability between them is in the following matrix form:

[0082]

[0083] Among them, the diagonal elements p in the matrix ii Set to 1, indicating that the information source is fully aware of its own state;

[0084] Step 2.3) Use the information correlation matrix P as the input of the subsequent access probability optimization process to provide a quantitative description of the sharing relationship between information sources to support the formulation of optimization decisions.

[0085] Further, step 3) comprises the following steps:

[0086] Step 3.1) Based on the constructed information relevance matrix, the changing law of information age is combined with the sharing probability to establish the objective function of the optimization problem. The goal is to minimize the average information age of the network to reduce the overall information update delay. The objective function of the optimization problem and its constraints are as follows:

[0087]

[0088] Constraints:

[0089]

[0090] in, Indicates the average age of information on the network.

[0091] Further, the step 4) comprises the following steps:

[0092] Step 4.1), initialize the access probability vector in, Indicates the information source S i The initial access probability is calculated to calculate the value function V of each information source. i , which is defined as follows:

[0093] Among them, V i Indicates the information source S i The value of p ij is the sharing probability, η j Information source S j The transmission success rate is , and the M information sources with the highest value are selected according to the value function. The initial access probability of these information sources is set to The access probability of other information sources is set to 0;

[0094] Step 4.2), set the iteration parameters, initialize the number of iterations k = 0, and set the maximum number of iterations K max , step size adjustment parameter range and convergence threshold ε to control the computational complexity of the optimization process, set the initial Hessian matrix H 0 =I, where I is the identity matrix;

[0095] Step 4.3), enter the iterative loop until the maximum number of iterations is reached or the convergence condition is met. At the kth iteration, a quadratic programming QP subproblem is constructed to determine the update direction S. The standard form of the QP subproblem is:

[0096] Constraints:

[0097]

[0098] in:

[0099] is the access probability vector of the current iteration step, where Indicates the information source S i Access probability at the kth iteration;

[0100] f(·) is the objective function defined in step 3.1) to minimize the average information age of the network;

[0101] is the gradient of the objective function, describing its rate of change at a certain point;

[0102] S is the update direction vector, which indicates the direction of movement from the current iteration point to the next iteration;

[0103] H k It is the Hessian matrix, which is used to describe the second-order derivative information of the objective function and to find the optimal direction more accurately;

[0104] g i (·) is the i-th constraint function, which describes the physical constraints of the access probability. m = 2N means there are 2N constraint functions, as follows: For i = 1, ..., N, the form of the constraint function is: To ensure that the access probability is non-negative, for i=N+1,…,2N, the constraint function is in the form of: Ensure that the access probability does not exceed 1;

[0105] is the gradient of the constraint function, describing its rate of change at a certain point;

[0106] T represents the transpose operation of a matrix or vector, which is used to convert a column vector into a row vector, or to convert a row vector into a column vector;

[0107] Step 4.4), use the Active Set method or the Interior Point method to solve the quadratic programming subproblem and obtain the update direction S;

[0108] Step 4.5), perform step size adjustment and determine the step size parameter vector β by line search method k, the step size parameter vector β k The elements in are positive numbers, which are used to control the distance moved from the current iteration point along the update direction S, so that the value of the objective function in the update direction decreases. The update formula of the access probability is: τ k+1 =τ k +β k S;

[0109] Step 4.6), use the BFGS algorithm to update the Hessian matrix H k , and its update formula is:

[0110]

[0111] in:

[0112] Δτ k+1 =τ k+1 -τ k is the change of access probability vector;

[0113] λ i is the Lagrange multiplier, which is used to measure the importance of each constraint in the current optimization solution, that is, it represents the weight of the constraint in the optimization process;

[0114] Step 4.7) The convergence of the optimization algorithm is judged based on any of the following three conditions. Once any of the three conditions is met, the optimization process is considered to have reached convergence:

[0115] Gradient norm: If It is considered to have reached convergence;

[0116] Objective function improvement: If the change of the objective function between two consecutive iterations is less than the set threshold, the iteration is stopped;

[0117] Maximum number of iterations: If the maximum number of iterations K is reached max , then stop the iteration;

[0118] Step 4.8), if the above convergence conditions are not met, set k = k + 1 and return to step 4.3) to continue the next iteration; otherwise, output the final optimal access probability combination τ * , which is applied to the access strategy of information sources to minimize the average information age of the network.

[0119] Through the above-mentioned convergence judgment and output mechanism, the optimization process is guaranteed to complete the setting of the optimal access probability within a reasonable computing resource range, avoiding the waste of resources caused by excessive iteration. The final output access probability combination not only improves the information timeliness of the network, but also enhances the stability of transmission, providing solid technical support for actual delay-sensitive applications such as the Internet of Things and intelligent transportation.

Claims

1. A method for optimizing the timeliness of random access network information based on information correlation, characterized in that: The method comprises the following steps: Step 1) establishing a random access network model including multiple information sources and a base station, wherein each information source performs random access communication through a shared spectrum resource, and data packet transmission is driven by a time slot; Step 2) construct an information correlation matrix based on the spatial distribution of information sources and the information sharing characteristics between adjacent sources, where each element in the matrix describes the sharing probability between information sources; Step 3) Based on the information relevance matrix, analyze the impact of the sharing relationship between information sources on the change of information age of information sources, and establish a mathematical model of the average information age of the network; Step 4) With the goal of minimizing the average information age of the network, an algorithm based on sequential quadratic programming is used to solve the mathematical model to obtain the optimal access probability of each information source.

2. According to claim 1, a random access network information timeliness optimization method based on information correlation is characterized in that: The step 1) comprises the following steps: Step 1.1) Construct a random access network model consisting of N information sources and one base station, where each information source independently decides whether to send a data packet to the base station in a given time slot. i With access probability τ i Initiate transmission, all information sources share the same wireless channel resources; in a certain time slot, if only one information source successfully sends a data packet, the base station can correctly receive the data packet and update the status of the information source; if multiple information sources send at the same time, a collision occurs, resulting in the data packet cannot be correctly received; the transmission success rate of the information source is η i It indicates the probability that the channel successfully transmits data in the current time slot; Step 1.2) Define the information age AoI as the time difference from information generation to successful reception by the base station. The information source S i The expression of the information age at time slot t is: A i (t)=t-t suc Among them, t suc Indicates the information source S i The time slot of the last successful data packet transmission; Step 1.3) At time slot t, depending on whether the transmission is successful, the information age of the information source is updated according to the following rules: When the source successfully sends a packet, the message age is reset to 1; otherwise, the message age is incremented in the next time slot.

3. The method for optimizing the timeliness of random access network information based on information correlation according to claim 2, characterized in that: The step 2) comprises the following steps: Step 2.1) Calculate the sharing probability between information sources based on the physical distance and other relevant characteristics between information sources to quantify the relevance of information sources. The calculation formula of sharing probability is as follows: p ij =ω i exp(-kL i,j ) Among them, ω i ∈[0,1], indicating the information source S i The weight factor reflects its importance in the network; k is the correlation coefficient, which is used to describe the rate at which the sharing probability decays with distance; S i,j Information source S i and S j The distance between Step 2.2) Construct the information correlation matrix P, which is asymmetric, where the element p ij Indicates the information source S i With information source S j The shared probability between them is in the following matrix form: Among them, the diagonal elements p in the matrix ii Set to 1, indicating that the information source is fully aware of its own state; Step 2.3) Use the information correlation matrix P as the input of the subsequent access probability optimization process to provide a quantitative description of the sharing relationship between information sources to support the formulation of optimization decisions.

4. The method for optimizing the timeliness of random access network information based on information correlation according to claim 3, characterized in that: Step 3) comprises the following steps: Step 3.1) Based on the constructed information relevance matrix, the changing law of information age is combined with the sharing probability to establish the objective function of the optimization problem. The goal is to minimize the average information age of the network to reduce the overall information update delay. The objective function of the optimization problem and its constraints are as follows: Constraints: in, Indicates the average age of information on the network.

5. The method for optimizing the timeliness of random access network information based on information correlation according to claim 4, characterized in that: The step 4) comprises the following steps: Step 4.1), initialize the access probability vector in, Indicates the information source S i The initial access probability is calculated to calculate the value function V of each information source. i , which is defined as follows: Among them, V i Indicates the information source S i The value of p ij is the sharing probability, η j Information source S j The transmission success rate is , and the M information sources with the highest value are selected according to the value function. The initial access probability of these information sources is set to The access probability of other information sources is set to 0; Step 4.2), set the iteration parameters, initialize the number of iterations k = 0, and set the maximum number of iterations K max , step size adjustment parameter range and convergence threshold ε to control the computational complexity of the optimization process, set the initial Hessian matrix H 0 =I, where I is the identity matrix; Step 4.3), enter the iteration loop until the maximum number of iterations is reached or the convergence condition is met. At the kth iteration, a quadratic programming QP subproblem is constructed to determine the update direction S. The standard form of the QP subproblem is: Constraints: in: is the access probability vector of the current iteration step, where Indicates the information source S i Access probability at the kth iteration; f(·) is the objective function defined in step 3.1) to minimize the average information age of the network; is the gradient of the objective function, describing its rate of change at a certain point; S is the update direction vector, which indicates the direction of movement from the current iteration point to the next iteration; H k It is the Hessian matrix, which is used to describe the second-order derivative information of the objective function and to find the optimal direction more accurately; g i (·) is the i-th constraint function, which describes the physical constraints of the access probability. m = 2N means there are 2N constraint functions, as follows: For i = 1, ..., N, the form of the constraint function is: To ensure that the access probability is non-negative, for i=N+1,…,2N, the constraint function is in the form of: Ensure that the access probability does not exceed 1; is the gradient of the constraint function, describing its rate of change at a certain point; T represents the transpose operation of a matrix or vector; Step 4.4), use the Active Set method or the Interior Point method to solve the quadratic programming subproblem and obtain the update direction S; Step 4.5), perform step size adjustment and determine the step size parameter vector β by line search method k , the step size parameter vector β k The elements in are positive numbers, which are used to control the distance moved from the current iteration point along the update direction S, so that the value of the objective function in the update direction decreases. The update formula of the access probability is: τ k+1 =τ k +β k S; Step 4.6), use the BFGS algorithm to update the Hessian matrix H k , and its update formula is: in: Δτ k+1 =τ k+1 -τ k is the change of access probability vector; λ i is the Lagrange multiplier, which is used to measure the importance of each constraint in the current optimization solution, that is, it represents the weight of the constraint in the optimization process; Step 4.7) The convergence of the optimization algorithm is judged according to any of the following three conditions. Once any of the three conditions is met, the optimization process is considered to have reached convergence: Gradient norm: If It is considered to have reached convergence; Improvement of the objective function: If the change of the objective function between two consecutive iterations is less than the set threshold, the iteration is stopped; Maximum number of iterations: If the maximum number of iterations K is reached max , then stop the iteration; Step 4.8), if the above convergence conditions are not met, set k = k + 1 and return to step 4.3) to continue the next iteration; otherwise, output the final optimal access probability combination τ * , which is applied to the access strategy of information sources to minimize the average information age of the network.

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