A random access network information time effectiveness optimization method based on information correlation

Abstract

The application discloses a random access network information time effectiveness optimization method based on information correlation, and 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 to describe the information sharing probability between the information sources; 3) deriving an expression of network average AoI; and 4) utilizing an optimization algorithm to jointly consider the information correlation and the random access probability and minimize the network average AoI. Simulation results show that, compared with a traditional random access strategy ignoring information correlation, the method of the application significantly reduces the network average AoI and improves the timeliness and stability of information transmission. The application is suitable for wireless communication scenes sensitive to time delay, such as the Internet of Things and video monitoring.

Description

Technical Field

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

[0002] With the rapid development of the Internet of Things (IoT) and smart devices, the demand for large-scale terminal access is constantly increasing, making random access a primary technology supporting data transmission for these devices. Random access technology allows terminals to transmit data directly without central coordination, improving network flexibility and scalability. However, in latency-sensitive application scenarios, existing random access strategies face numerous challenges, such as increased packet collisions, decreased throughput, and increased transmission latency, affecting data timeliness. Information timeliness is one of the key indicators for measuring network performance. AoI describes the time elapsed from the generation to reception of the latest received information and is an important parameter for evaluating the timeliness of data updates. Most current random access optimization methods ignore the spatiotemporal correlation between information sources, typically assuming that each information source updates independently. This assumption is inaccurate in practical applications because adjacent information sources, such as video surveillance cameras and sensor nodes, often have spatial overlap or information sharing characteristics, leading to strong correlation in their transmitted data. Optimization methods that ignore information correlation fail to fully utilize the potential of information sharing, thus failing to achieve optimal control of the network's average AoI. Summary of the Invention

[0003] Purpose of the invention: This invention aims to solve the problem of insufficient information timeliness caused by ignoring the correlation of information sources in random access networks. It proposes a method to optimize access probability by constructing an information correlation matrix and using a sequential quadratic programming algorithm to minimize the average AoI of the network, thereby improving the timeliness and stability of data updates.

[0004] Technical Solution: To achieve the above-mentioned objectives, this invention employs a method for optimizing the timeliness of random access network information based on information correlation, characterized in that the method includes the following steps:

[0005] Step 1) Establish a random access network model containing multiple information sources and one base station. Each information source communicates through random access using shared spectrum resources, and data packet transmission is time-slot driven.

[0006] Step 2) Based on the spatial distribution of information sources and the information sharing characteristics between adjacent sources, construct an information correlation matrix, where each element 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, use an algorithm based on sequential quadratic programming to solve the mathematical model and obtain the optimal access probability of each information source.

[0009] Furthermore, step 1) includes the following steps:

[0010] Step 1.1) Construct a random access network model containing N information sources and one base station, where each information source independently decides whether to send a data packet to the base station within a given time slot. Each information source S i With access probability τ i Initiating a transmission, all information sources share the same wireless channel resources. Within a given time slot, if only one information source successfully transmits a data packet, the base station can correctly receive the packet and update the information source's status. If multiple information sources transmit simultaneously, a collision occurs, preventing the data packet from being correctly received. The transmission success rate of the information sources is denoted by η. i This indicates the probability that the channel will successfully transmit data within the current time slot.

[0011] Step 1.2) Define the information age AoI as the time difference between information generation and successful reception by the base station, and the information source S i The expression for the information age in time slot t is:

[0012] A i (t)=tt suc

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

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

[0015]

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

[0017] Furthermore, step 2) includes the following steps:

[0018] Step 2.1) Based on the physical distance and other relevant characteristics between information sources, calculate the sharing probability between information sources to quantify the relevance of information sources. The formula for calculating the sharing probability is as follows:

[0019] p ij =ω i exp(-kL i,j)

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

[0021] Step 2.2) Construct an information relevance matrix P. This matrix is ​​asymmetric, where the elements p ij Indicates information source S i With information source S j The shared probabilities between them, in matrix form, are as follows:

[0022]

[0023] Wherein, the diagonal element p in the matrix ii Setting it to 1 indicates that the information source has complete awareness of its own state;

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

[0025] Furthermore, step 3) includes the following steps:

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

[0027]

[0028] Constraints:

[0029]

[0030] in, This indicates the average age of information on the internet.

[0031] Furthermore, step 4) includes the following steps:

[0032] Step 4.1), initialize the access probability vector in, Indicates information source S i Given the initial access probability, calculate the value function V for each information source. i Its definition is as follows:

[0033] Among them, V i Indicates information source S i The value of p ij For shared probability, η j For information source S j To determine the transmission success rate, the M information sources with the highest value are selected based on the magnitude of the value function, and 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 The step size is adjusted by modifying the parameter range and the convergence threshold ε to control the computational complexity of the optimization process. An initial Hessian matrix H is set. 0 =I, where I is the identity matrix;

[0035] Step 4.3): Enter the iterative loop until the maximum number of iterations is reached or the convergence condition is met. In the k-th iteration, construct a quadratic programming (QP) subproblem to determine the update direction S. The standard form of the QP subproblem is:

[0036] Constraints:

[0037]

[0038] in:

[0039] It is the access probability vector for the current iteration step, where, Indicates information source S i The access probability at the k-th iteration;

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

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

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

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

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

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

[0046] T represents the transpose operation of a matrix or vector, used to convert a column vector into a row vector, or vice versa.

[0047] Step 4.4): Solve the quadratic programming subproblem using the Active Set method or the Interior Point method to obtain the updated direction S;

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

[0049] Step 4.6): Update the Hessian matrix H using the BFGS algorithm. k Its update formula is:

[0050]

[0051] in:

[0052] Δτ k+1 =τ k+1 -τ k This represents the change in the access probability vector;

[0053] λ i These are Lagrange multipliers, used to measure the importance of each constraint in the current optimization solution, that is, to represent the weight of the constraints during the optimization process;

[0054] Step 4.7) Determine the convergence of the optimization algorithm. Convergence is determined based on any one of the following three conditions. Once any one of these conditions is met, the optimization process is considered to have converged:

[0055] Gradient norm: if Then it is considered to have reached convergence;

[0056] Improvement magnitude of objective function: If the change in objective function between two consecutive iterations is less than a set threshold, then the iteration stops;

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

[0058] Step 4.8): If none of the above convergence conditions are met, let k = k + 1 and return to step 4.3) to continue the next iteration; otherwise, output the final optimal access probability combination τ. * This can be 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) This invention proposes a random access probability optimization method based on information correlation. By constructing an information correlation matrix, the impact of information sharing on AoI is analyzed, and an optimization algorithm based on sequential quadratic programming (SQP) is adopted to significantly reduce the average AoI of the network and improve the timeliness and reliability of data transmission. Intelligent Transportation Systems (ITS) are an important component of future smart cities. Autonomous vehicle platooning technology is a very promising ITS service. By platooning, vehicles can effectively increase road capacity, reduce traffic congestion, and effectively save fuel and other energy consumption.

[0061] (2) Furthermore, platooning can improve passenger comfort, especially during long-distance travel. Compared to traditional random access strategies, this invention can fully utilize the correlation between information sources, reduce transmission conflicts, and improve resource utilization efficiency. The adopted SQP algorithm features fast convergence and strong stability, and can dynamically adapt to different scenarios. It is suitable for latency-sensitive applications such as the Internet of Things and video surveillance, achieving simple and efficient optimization. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating the method for optimizing the timeliness of random access network information based on information correlation in this invention. Detailed Implementation

[0063] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art fall within the scope defined by the appended claims. Examples of the present invention are analyzed in detail in conjunction with specific scenarios to optimize the timeliness of random access network information based on information correlation; however, 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, this invention employs a method for optimizing the timeliness of random access network information based on information correlation. The method comprises the following steps:

[0065] Step 1) Establish a random access network model containing multiple information sources and one base station. Each information source communicates through random access using shared spectrum resources, and data packet transmission is time-slot driven.

[0066] Step 2) Based on the spatial distribution of information sources and the information sharing characteristics between adjacent sources, construct an information correlation matrix, where each element 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, use an algorithm based on sequential quadratic programming to solve the mathematical model and obtain the optimal access probability of each information source.

[0069] Furthermore, step 1) includes the following steps:

[0070] Step 1.1) Construct a random access network model containing N information sources and one base station, where each information source independently decides whether to send a data packet to the base station within a given time slot. Each information source S i With access probability τ i Initiating a transmission, all information sources share the same wireless channel resources. Within a given time slot, if only one information source successfully transmits a data packet, the base station can correctly receive the packet and update the information source's status. If multiple information sources transmit simultaneously, a collision occurs, preventing the data packet from being correctly received. The transmission success rate of the information sources is denoted by η. i This indicates the probability that the channel will successfully transmit data within the current time slot.

[0071] Step 1.2) Define the information age AoI as the time difference between information generation and successful reception by the base station, and the information source S i The expression for the information age in time slot t is:

[0072] A i (t)=tt suc

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

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

[0075]

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

[0077] Furthermore, step 2) includes the following steps:

[0078] Step 2.1) Based on the physical distance and other relevant characteristics between information sources, calculate the sharing probability between information sources to quantify the relevance of information sources. The formula for calculating the sharing probability is as follows:

[0079] p ij =ω i exp(-kL i,j )

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

[0081] Step 2.2) Construct an information relevance matrix P. This matrix is ​​asymmetric, where the elements p ij Indicates information source S i With information source S j The shared probabilities between them, in matrix form, are as follows:

[0082]

[0083] Wherein, the diagonal element p in the matrix ii Setting it to 1 indicates that the information source has complete awareness of its own state;

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

[0085] Furthermore, step 3) includes the following steps:

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

[0087]

[0088] Constraints:

[0089]

[0090] in, This indicates the average age of information on the internet.

[0091] Furthermore, step 4) includes the following steps:

[0092] Step 4.1), initialize the access probability vector in, Indicates information source S i Given the initial access probability, calculate the value function V for each information source. i Its definition is as follows:

[0093] Among them, V i Indicates information source S i The value of p ij For shared probability, η j For information source S j To determine the transmission success rate, the M information sources with the highest value are selected based on the magnitude of the value function, and 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 The step size is adjusted by modifying the parameter range and the convergence threshold ε to control the computational complexity of the optimization process. An initial Hessian matrix H is set. 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. In the k-th iteration, construct a quadratic programming (QP) subproblem to determine the update direction S. The standard form of the QP subproblem is:

[0096] Constraints:

[0097]

[0098] in:

[0099] It is the access probability vector for the current iteration step, where, Indicates information source S i The access probability at the k-th iteration;

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

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

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

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

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

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

[0106] T represents the transpose operation of a matrix or vector, used to convert a column vector into a row vector, or vice versa.

[0107] Step 4.4): Solve the quadratic programming subproblem using the Active Set method or the Interior Point method to obtain the updated direction S;

[0108] Step 4.5): Perform step size adjustment, and determine the step size parameter vector β using the line search method. kStep size parameter vector β k The middle element is a positive number, used to control the distance moved from the current iteration point along the update direction S, so that the value of the objective function decreases in the update direction. The update formula for the access probability is: τ k+1 =τ k +β k S;

[0109] Step 4.6): Update the Hessian matrix H using the BFGS algorithm. k Its update formula is:

[0110]

[0111] in:

[0112] Δτ k+1 =τ k+1 -τ k This represents the change in the access probability vector;

[0113] λ i These are Lagrange multipliers, used to measure the importance of each constraint in the current optimization solution, that is, to represent the weight of the constraints during the optimization process;

[0114] Step 4.7) Determine the convergence of the optimization algorithm. Convergence is determined based on any one of the following three conditions. Once any one of these conditions is met, the optimization process is considered to have converged:

[0115] Gradient norm: if Then it is considered to have reached convergence;

[0116] Improvement magnitude of objective function: If the change in objective function between two consecutive iterations is less than a set threshold, then the iteration stops;

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

[0118] Step 4.8): If none of the above convergence conditions are met, let k = k + 1 and return to step 4.3) to continue the next iteration; otherwise, output the final optimal access probability combination τ. * This can be applied to the access strategy of information sources to minimize the average information age of the network.

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

Claims

1. A method for optimizing the timeliness of information in random access networks based on information correlation, characterized in that, The method includes the following steps: Step 1) Establish a random access network model containing multiple information sources and one base station. Each information source communicates through random access using shared spectrum resources, and data packet transmission is time-slot driven. Step 2) Based on the spatial distribution of information sources and the information sharing characteristics between adjacent sources, construct an information correlation matrix, where each element 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, the mathematical model is solved using an algorithm based on sequential quadratic programming to obtain the optimal access probability of each information source. Step 1) includes the following steps: Step 1.1) Construct a random access network model containing 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, each information source S i has an access probability τ i Initiates 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 state of the information source; if multiple information sources send at the same time, a collision occurs, resulting in the data packet being unable to be correctly received; the transmission success rate of the information source is denoted by η i , which represents the probability of successful transmission of data by the channel in the current time slot; Step 1.2) Define the information age AoI as the time difference between information generation and successful reception by the base station, and the information source S i The expression for the information age in time slot t is: A i (t)=t-t suc Among them, t suc Indicates information source S i The time slot of the last successfully transmitted data packet; Step 1.3) At time slot t, based on whether the transmission was successful, the information age of the information source is updated according to the following rules: When the information source successfully sends the data packet, the information age is reset to 1; otherwise, the information age is incremented in the next time slot. Step 2) includes the following steps: Step 2.1) Based on the physical distance and other relevant characteristics between information sources, calculate the sharing probability between information sources to quantify the relevance of information sources. The formula for calculating the sharing probability is as follows: p ij =ω i exp(-kL i,j ) Where, ω i ∈[0,1], representing information source S i The weighting factor reflects its importance in the network; k is the correlation coefficient, used to describe the rate at which the sharing probability decays with distance; L i,j For information source S i and S j The distance between them; Step 2.2) Construct an information relevance matrix P. This matrix is ​​asymmetric, where the elements p ij Indicates information source S i With information source S j The shared probabilities between them, in matrix form, are as follows: Wherein, the diagonal element p in the matrix ii Setting it to 1 indicates that the information source has complete awareness of its own state; Step 2.3) Use the information relevance matrix P as input to the subsequent access probability optimization process to provide a quantitative description of the sharing relationship between information sources, so as to support the formulation of optimization decisions; Step 3) includes the following steps: Step 3.1) Based on the constructed information relevance matrix, the changing pattern of information age is combined with the sharing probability to establish the objective function of the optimization problem. The objective is to minimize the average information age of the network to reduce the overall information update delay. The objective function and its constraints are as follows: Constraints: in, This indicates the average age of information on the internet.

2. The method for optimizing the timeliness of random access network information based on information correlation according to claim 1, characterized in that, Step 4) includes the following steps: Step 4.1), initialize the access probability vector in, Indicates information source S i Given the initial access probability, calculate the value function V for each information source. i Its definition is as follows: Among them, V i Indicates information source S i The value of p ij For shared probability, η j For information source S j To determine the transmission success rate, the M information sources with the highest value are selected based on the magnitude of the value function, and 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 The step size is adjusted by modifying the parameter range and the convergence threshold ε to control the computational complexity of the optimization process. An initial Hessian matrix H is set. 0 =I, where I is the identity matrix; Step 4.3): Enter the iterative loop until the maximum number of iterations is reached or the convergence condition is met. In the k-th iteration, construct a quadratic programming (QP) subproblem to determine the update direction S. The standard form of the QP subproblem is: Constraints: in: It is the access probability vector for the current iteration step, where, Indicates information source S i The access probability at the k-th iteration; f(·) is the objective function defined in step 3.1), used to minimize the average information age of the network; It is the gradient of the objective function, which describes its rate of change at a certain point; S is the update direction vector, representing the direction of movement from the current iteration point to the next iteration; H k It is a Hessian matrix, used to describe the second derivative information of the objective function, which is used to find the optimal direction more accurately; g i (·) is the i-th constraint function, describing the physical constraints on the access probability. m = 2N indicates that there are 2N constraint functions, specifically as follows: For i = 1, ..., N, the constraint function takes the form: To ensure the access probability is non-negative, for i = N+1, ..., 2N, the constraint function takes the form: Ensure the probability of access does not exceed 1; It is the gradient of the constraint function, which describes its rate of change at a certain point; T represents the transpose operation of a matrix or vector; Step 4.4): Solve the quadratic programming subproblem using the Active Set method or the Interior Point method to obtain the updated direction S; Step 4.5): Perform step size adjustment, and determine the step size parameter vector β using the line search method. k Step size parameter vector β k The middle element is a positive number, used to control the distance moved from the current iteration point along the update direction S, so that the value of the objective function decreases in the update direction. The update formula for the access probability is: τ k+1 =τ k +β k S; Step 4.6): Update the Hessian matrix H using the BFGS algorithm. k Its update formula is: in: Δτ k+1 =τ k+1 -τ k This represents the change in the access probability vector; λ i These are Lagrange multipliers, used to measure the importance of each constraint in the current optimization solution, that is, to represent the weight of the constraints during the optimization process; Step 4.7) Determine the convergence of the optimization algorithm. Convergence is determined based on any one of the following three conditions. Once any one of these conditions is met, the optimization process is considered to have converged: Gradient norm: if Then it is considered to have reached convergence; Improvement magnitude of objective function: If the change in objective function between two consecutive iterations is less than a set threshold, then the iteration stops; Maximum number of iterations: If the maximum number of iterations K is reached... max If the iteration stops, then stop. Step 4.8): If none of the above convergence conditions are met, let k = k + 1 and return to step 4.3) to continue the next iteration; otherwise, output the final optimal access probability combination τ. * This can be applied to the access strategy of information sources to minimize the average information age of the network.

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