Method for data distribution based on information value in a mobile communication network

Abstract

The application provides a data distribution method based on information value in a mobile communication network, and belongs to the field of wireless mobile communication networks and data distribution. First, a data distribution network architecture for quality of service in the mobile communication is constructed. Second, an information value evaluation function is established to represent the value of information received by a user based on multi-dimensional attributes of the information. Next, a data distribution optimization model is established based on the data distribution model and the information value evaluation function. Then, an information value-based data distribution (IVMD) algorithm is proposed based on the optimization model. Finally, the mobile communication network distributes data based on the IVMD strategy to improve the overall performance of the system and the satisfaction of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of value evaluation and information distribution, and particularly discloses a data distribution method based on information value in a mobile communication network. BACKGROUND

[0002] Since the birth of computer networks, researchers have been conducting extensive research on the performance of networks, optimizing performance in terms of throughput, latency, bandwidth, etc., in order to achieve maximum reduction of network resource consumption, and service quality has become the main measurement method of network information transmission. In existing 5G and 6G communication technologies, the requirements for service quality such as latency and bandwidth can be met in most cases. However, in the current network level research, the main focus is simply to improve the service capability on the data transmission level, while ignoring the service capability of the data itself, thereby causing the unlimited growth of data volume and the low improvement of value income, resulting in a series of problems such as "information redundancy", "digital garbage", "information flooding", etc., making it difficult for users to obtain the real value information they need from a large amount of data. In mobile communication networks, due to changes in the environment and the mobility of users, it is particularly important to ensure that information can be transmitted and distributed according to its value and to reduce the invalid forwarding of data in the network. In addition, mobile communication networks are mainly user-oriented services, and are mostly designed and deployed for specific applications, and the completion of specific tasks largely depends on the available information and its quality provided by the communication network, so attention needs to be turned to the information quality provided by the network to various users in order to better meet the needs of users and improve the actual value of the communication system to network users.

[0003] From the perspective of information quality, the "size of the value of the received information" replaces the "amount of data received per second" in the past service quality perspective to consider network design problems and improve the service capability of the network. Rich sensing means and transmission needs have led to an unprecedented increase in the amount of information, but the limited capacity of the network, the hardware condition restrictions, and the changes in the environment cannot guarantee the transmission of large-scale data in the network, and only high-quality, high-value information is the effective data required by users and nodes.

[0004] Through effective evaluation of information value and optimization of data distribution strategy, the network can transmit valuable and needed information to users, and the information value evaluation, user information demand and network transmission capability can be combined to optimize the design of information distribution strategy from the actual demand and real feasibility of resources, thereby reducing the network burden, improving user satisfaction, and improving the utilization efficiency of communication network resources. SUMMARY

[0005] Therefore, the present application proposes a data distribution method based on information value in a mobile communication network to solve the problems in the background art, which establishes an information value evaluation model and designs a data distribution strategy based on information value to maximize the information value obtained by users, thereby improving the service quality of the mobile communication network.

[0006] The technical solution adopted by the present application is:

[0007] A data distribution method based on information value in a mobile communication network comprises the following steps:

[0008] Step 1: constructing a data distribution network architecture for service quality in mobile communication, including one base station and multiple users, the base station sends the buffered data packets to the users;

[0009] Step 2: establishing an information value evaluation function based on the multi-dimensional attributes of information to evaluate the information value of the data packets sent at a certain time;

[0010] Step 3: establishing a data distribution optimization model for maximizing the information value of all users with the goal of maximizing the sum of the information value obtained by all users;

[0011] Step 4: solving the data distribution optimization model to obtain the optimal data distribution strategy;

[0012] Step 5: distributing data according to the optimal data distribution strategy obtained by solving.

[0013] Further, the specific process of step 1 comprises:

[0014] Constructing a data distribution network architecture for service quality based on the "buffer-compute-distribute" strategy in a mobile communication network, including one base station with storage and computing capabilities and M users U={u1, u2,…u M}, the base station buffers N data packets to be sent, of which n i data packets are to be sent to user u i , i.e.:

[0015]

[0016] Further, the specific process of step 2 comprises:

[0017] Let the initial time of the base station to distribute data packets be t0, and send one data packet at each time, then at time t, the base station sends the jth data packet to user i, denoted as p i,j , where t0<t≤t0+N, i∈{1,2,…,M}, j∈{1,2,…,n i}; then the information value of data packet p i,j at time t is:

[0018]

[0019] in, For data packet p i,j The value of information at the initial moment, and satisfy and Respectively represent the number of data packets to be received by users i1 and i2; For data packet p i,j The attenuation factor,

[0020] Furthermore, the data distribution optimization model for maximizing the value of all user information in step 3 is expressed as:

[0021] max V(x1,x2,…,x N )

[0022]

[0023]

[0024] in, t l The data packets sent at any time, represents the initial information value, represents the information attenuation factor; I i,j (x k ) is an indicative function, indicating whether the data packet sent during the base station service period is required by the user. If it is required by the user, then I i,j (x k )=1, otherwise I i,j (x k )=0.

[0025] Furthermore, the specific process of step 4 includes:

[0026] When the decay factors of all users are the same, the optimal distribution strategy x * for:

[0027]

[0028] in, represents the optimal strategy x * The i-th component of express The initial information value of

[0029] When the attenuation factors of all users are different, the information value-based data distribution algorithm, namely the IVMD algorithm, is used to solve the optimal solution of the data distribution optimization model.

[0030] Furthermore, the specific steps of the IVMD algorithm are as follows:

[0031] Step 4021, initialize the parameters, the number of iterations Item = 1; vectorize N data packets to obtain

[0032]

[0033] Step 4022, sort the initial information values ​​of the N data packets from large to small to obtain Arrange the attenuation factors of N data packets from small to large, and we get in, Indicates the kth i The initial information value of a data packet, Indicates the first i Attenuation factor for each data packet;

[0034] Step 4023, let k = Item, calculate f[V1(k)] and f[V2(k)], where f() is the information number operation corresponding to the component, and V1(k) and V2(k) correspond to the k-th value of V1 and V2;

[0035] Step 4024, when f[V1(k)]=f[V2(k)], there is When f[V1(k)]≠f[V2(k)], calculate

[0036]

[0037] Then we get

[0038]

[0039] Among them, t k is the k moment;

[0040] Step 4025, delete x in V1 and V2 * (k) Elements, sort the remaining elements in V1 and V2 from large to small and from small to large respectively;

[0041] Step 4026, update Item=Item+1;

[0042] Step 4027: If Item≤N, return to step 4023; otherwise, end the iteration.

[0043] Step 4028, output the optimal strategy x *, and calculate the information value of all users:

[0044]

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. This invention proposes for the first time a data distribution model based on information value for mobile communication networks.

[0047] 2. The present invention proposes an information value evaluation function mechanism and optimizes the data distribution strategy based on the user's information value, thereby improving user satisfaction.

[0048] 3. The present invention designs a data distribution algorithm based on information value. This algorithm has low complexity and strong optimization capability, which effectively improves data distribution efficiency and enhances the overall performance of the mobile communication network system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a data distribution method based on information value in an embodiment of the present invention.

[0050] Figure 2 Schematic diagram of a mobile communication network data distribution system in an embodiment of the present invention.

[0051] Figure 3 This is a simulation verification diagram of the data distribution strategy under the same attenuation factors in an embodiment of the present invention.

[0052] Figure 4 This is a simulation verification diagram of the data distribution strategy under different attenuation factors in an embodiment of the present invention.

[0053] Figure 5 This is a simulation verification diagram of different data distribution quantities in an embodiment of the present invention.

[0054] Figure 6 This is a diagram comparing the complexity of data distribution strategies in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0056] A data distribution method based on information value in mobile communication networks first constructs a data distribution network architecture for service quality in mobile communication. Second, an information value evaluation function is established to characterize the value of information received by users based on the multidimensional attributes of information. Next, a data distribution optimization model is established based on the data distribution model and the information value evaluation function. Then, based on the optimization model, an information value-based data distribution (IVMD) algorithm is proposed. Finally, the mobile communication network distributes data according to the IVMD strategy to improve the overall system performance and user satisfaction.

[0057] The specific steps of this method are as follows Figure 1 As shown, the specific steps include:

[0058] Step 1: Build a data distribution network architecture oriented towards quality of service in mobile communications.

[0059] The present invention aims to improve the data distribution efficiency of the mobile communication network system and enhance user satisfaction. Therefore, a data distribution network architecture based on the new strategy of "cache-computation-distribution" is constructed. Figure 2 As shown. There is a base station with storage and computing capabilities and M users U={u1,u2,…u M}, there are N data packets to be sent in the base station, of which n i (0≤n i ≤N) data packets to be sent to user u i , that is, satisfying:

[0060]

[0061] Step 2: Establish an evaluation function of information value based on the multidimensional attributes of information.

[0062] Information is time-sensitive. When the same information reaches a user at different times, the user experience is different. Furthermore, information has identity attributes. When the same information reaches different users at the same time, each user's experience is also different. This paper establishes an information value evaluation function based on the multidimensional attributes of information.

[0063] Assume that the initial time when the base station distributes data packets is t0, and a data packet is sent at each time. <t≤t0+N),基站向用户i发送第j个数据包p i,j (i∈{1,2,…,M},j∈{1,2,…,n i}). Define the time t packet p i,j The information value is:

[0064]

[0065] in, For data packet pi,j The value of information at the initial moment, and satisfy and Respectively represent the number of data packets to be received by users i1 and i2; For data packet p i,j The attenuation factor,

[0066] Step 3: Establish a data distribution optimization model that maximizes the value of all user information.

[0067] To improve user satisfaction, optimize the data distribution order, and maximize the information value of all users is the ultimate goal of this invention. Therefore, the data distribution optimization model that maximizes the information value of all users is expressed as:

[0068] max V(x1,x2,…,x N )

[0069]

[0070] in, t l The data packets sent at any time, represents the initial information value, represents the information attenuation factor; I i,j (x k ) is an indicative function, indicating whether the data packet sent during the base station service period is required by the user. If it is required by the user, then I i,j (x k )=1, otherwise I i,j (x k )=0.

[0071] Step 4: Propose an information value based message delivery (IVMD) strategy, solve the data distribution optimization model, and obtain the optimal data distribution strategy.

[0072] In order to obtain the optimal data distribution strategy for mobile communication networks, the present invention proposes a data distribution algorithm based on information value with low complexity and strong optimization capability. The specific process of the algorithm is as follows:

[0073] When the decay factors of all users are the same, the optimal distribution strategy x * for:

[0074]

[0075] in, represents the optimal strategy x * the i-th component of x represents the initial information value of

[0076] The formula derivation process of the above optimal strategy is as follows:

[0077] Step 4011, assuming that {α1,α2,…,α N} and {β1,β2,…,β N} are respectively a permutation and combination of the set {c1,c2,…,c N}(c i >0,i=1,2,…,N), and for any i,j=1,2,…,N, when i≤j, α i ≥α j . Let For any k=1,2,…,N, the following is true:

[0078]

[0079] Step 4012, let Since there is It can be concluded that:

[0080]

[0081] Step 4013, further true:

[0082]

[0083] Step 4014, therefore:

[0084]

[0085] When the attenuation factors of all users are different, the data distribution optimization model is solved by using an information value-based data distribution algorithm (IVMD algorithm). The specific steps of the IVMD algorithm for solving are as follows:

[0086] Step 4021, initialize parameters, iteration number Item=1; vectorize the N data packets to obtain

[0087]

[0088] Step 4022, arrange the initial information values of the N data packets from large to small to obtain Arrange the attenuation factors of the N data packets from small to large to obtain wherein, the k-th data packet is the k-th data packet​i initial information value of each data packet, representing the l i decay factor of the l

[0089] Step 4023, let k = Item, calculate f[V1(k)] and f[V2(k)], wherein f() is an operation of taking the information number corresponding to the component, V1(k) and V2(k) correspond to the kth value of V1 and V2;

[0090] Step 4024, when f[V1(k)] = f[V2(k)], there is When f[V1(k)] ≠ f[V2(k)], calculate

[0091]

[0092] Then get

[0093]

[0094] Wherein, t k is the kth moment;

[0095] Step 4025, delete the x * (k) element in V1 and V2, and arrange the remaining elements in V1 and V2 from large to small and from small to large respectively;

[0096] Step 4026, update Item = Item + 1;

[0097] Step 4027, if Item ≤ N, return to step 4023, otherwise end the iteration;

[0098] Step 4028, output the optimal strategy x * , and calculate the information value of all users:

[0099]

[0100] Step 5, the mobile communication network system performs data distribution according to the optimal data distribution strategy solved.

[0101] Simulation experiment:

[0102] The present application designs four numerical experiments to verify the effectiveness of the proposed data distribution strategy IVMD algorithm in mobile communication network from different angles. Among them, MAX represents taking the maximum information value at each moment for sending during data distribution; EN represents selecting the data arrangement combination with the maximum total information value after enumerating all data distribution sequences, that is, the optimal solution; RD represents randomly selecting a data for sending at each moment; and ST represents performing data distribution according to the first-in-first-out principle.

[0103] First, verify the performance of the IVMD algorithm when each user is in the same environment, that is, when the attenuation factor is the same, as shown in Figure 3 As shown in the figure. The initial information value of user data is the same, and the attenuation factor is set from 0.1 to 0.9 for a total of 9 cases. It can be seen that the total information value of the IVMD algorithm is the same as that of the EN and MAX methods. Secondly, the performance of the IVMD algorithm is verified under different environments of each user, that is, under different attenuation factors, as shown in the figure. Figure 4 As shown. The initial information value of user data is the same, and the attenuation factors under different user environments are randomly generated. A total of 10 cases are taken. It can be seen that the IVMD algorithm proposed in this invention is second only to the enumeration method. Next, 20 sets of initial information value of distribution data and 20 sets of attenuation factors are randomly generated, the two sets are randomly combined, and the number of distribution data is set to change from 3 to 10. The average value is calculated, and the results are shown as follows: Figure 5 As shown in . It can be seen that the IVMD algorithm is second only to the EN optimal solution and is higher than the sum of the information values ​​of other algorithms. Finally, the complexity of the IVMD algorithm is verified, as shown in Figure 6 As shown in the figure, although the EN algorithm can obtain the optimal solution, the time consumed increases exponentially with the increase in the amount of distributed data. Although the IVMD algorithm consumes more time than other methods, it is negligible. Since the solution obtained by the IVMD algorithm is second only to the optimal solution, the effectiveness of the algorithm proposed in this paper can be verified.

Claims

1. A data distribution method based on information value in a mobile communication network, characterized in that: The following steps are involved: Step 1: Build a data distribution network architecture for quality of service in mobile communications, including a base station and multiple users. The base station sends cached data packets to the corresponding users. Step 2: Establish an information value evaluation function based on the multi-dimensional attributes of information to evaluate the information value of the data packet sent at a certain moment; Step 3: With the goal of maximizing the total value of information obtained by all users, establish a data distribution optimization model that maximizes the value of all user information; Step 4: Solve the data distribution optimization model to obtain the optimal data distribution strategy; Step 5: Distribute data according to the optimal data distribution strategy obtained; The specific process of step 4 includes: When the decay factors of all users are the same, the optimal data distribution strategy x * for: in, represents the optimal strategy x * The i-th component of express The initial information value, N is the number of data packets to be sent; When the decay factors of all users are different, the optimal data distribution strategy x * The solution steps are: Step 4021, initialize the parameters, the number of iterations Item = 1; vectorize N data packets to obtain Where i = 1, 2, ..., M, M is the number of users, n i is the number of data packets to be sent to the user; Step 4022, sort the initial information values ​​of the N data packets from large to small to obtain Arrange the attenuation factors of N data packets from small to large, and we get in, Indicates the kth i The initial information value of a data packet, Indicates the first i Attenuation factor for each data packet; Step 4023, let k = Item, calculate f[V1(k)] and f{V2(k)], where f() is the information number operation corresponding to the component, and V1(k) and V2(k) correspond to the k-th value of V1 and V2; Step 4024, when f[V1(k)]=f[V2(k)], there is When f[V1(k)]≠f[V2(k)], calculate Then we get Among them, t k is the k moment; Step 4025, delete x in V1 and V2 * (k) Elements, sort the remaining elements in V1 and V2 from large to small and from small to large respectively; Step 4026, update Item=Item+1; Step 4027: If Item≤N, return to step 4023; otherwise, end the iteration. Step 4028: Output the optimal data distribution strategy x * , and calculate the information value of all users:

2. The data distribution method based on information value in a mobile communication network according to claim 1, characterized in that: The specific process of step 1 includes: Construct a data distribution network architecture for quality of service in mobile communication networks, including a base station with storage and computing capabilities and M users U = {u1,u2,…u M }, the base station has N packets to be sent, of which n i Data packets to be sent to user u i ,Right now:

3. The data distribution method based on information value in a mobile communication network according to claim 2, characterized in that: The specific process of step 2 includes: Assume that the initial time when the base station distributes data packets is t0, and a data packet is sent at each time. Then at time t, the base station sends the jth data packet to user i, which means p i,j , where t0 <t≤t0+N,i∈{1,2,…,M},j∈{1,2,…,n i }; then at time t the data packet p i,j The information value is: in, For data packet p i,j The value of information at the initial moment, and satisfy and Respectively represent the number of data packets to be received by users i1 and i2; For data packet p i,j The attenuation factor, 4. The data distribution method based on information value in a mobile communication network according to claim 3, characterized in that: The data distribution optimization model that maximizes the value of all user information in step 3 is expressed as: max V(x1,x2,…,x N ) in, t l The data packets sent at any time, represents the initial information value, represents the information attenuation factor; I i,j (x k ) is an indicative function, indicating whether the data packet sent during the base station service period is required by the user. If it is required by the user, then I i,j (x k )=1, otherwise I i,j (x k )=0.

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