Wireless network optimization method, device, apparatus and computer storage medium

Abstract

Embodiments of the present application relate to the field of communication technology, and disclose a wireless network optimization method, device and equipment, and a computer storage medium. The method comprises the following steps: determining a traffic matrix of a cell cluster in a preset time period; the traffic matrix comprises intra-cell traffic of each cell in the cell cluster and migration traffic from each cell to other cells in the cell cluster; determining a network performance characteristic matrix of a wireless network in which the cell cluster is located; determining total consumed traffic of the cell cluster according to the network performance characteristic matrix and the traffic matrix; taking the minimum total consumed traffic as a target and the sum of all intra-cell traffics in the cell cluster in the preset time period as a boundary condition, iteratively solving the network performance characteristic matrix to obtain an optimal solution; and adjusting the wireless network according to the optimal solution. In the above manner, the embodiments of the present application improve the efficiency and accuracy of wireless network optimization.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of communication technology, specifically to a wireless network optimization method, apparatus, device, and computer storage medium. Background Technology

[0002] Cells are one of the important network units in wireless communication networks. Adjusting parameters such as cell antennas, resources, and frequencies is an important means of network optimization. In mobile communication networks, due to the continuous increase in base stations and wireless resources, simply adjusting a single cell cannot achieve the goal of improving the overall system performance. Therefore, it is necessary to form a cell cluster of several cells with continuous coverage, traffic, and quality to conduct overall analysis and optimization.

[0003] In the process of implementing this invention, the inventors discovered that current network optimization for cell clusters is generally performed from a single dimension, such as the capacity, coverage, or frequency of each cell in the cell cluster, without considering the wireless network as a whole. This results in poor optimization performance of current wireless networks. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a wireless network optimization method, apparatus, device, and computer storage medium to solve the problem of poor wireless network optimization performance in the prior art.

[0005] According to one aspect of the present invention, a wireless network optimization method is provided, the method comprising:

[0006] Determine the traffic matrix of the cell cluster within a preset time period; the traffic matrix includes the intra-cell traffic of each cell in the cell cluster and the migration traffic from each cell to other cells in the cell cluster;

[0007] Determine the network performance characteristic matrix of the wireless network to which the cell cluster belongs;

[0008] The total traffic consumption of the cell cluster is determined based on the network performance characteristic matrix and the traffic matrix.

[0009] With the goal of minimizing the total traffic consumption, and taking the condition that the sum of traffic in all cells of the cell cluster remains unchanged within the preset time period as the boundary condition, the network performance feature matrix is ​​iteratively solved to obtain the optimal solution;

[0010] The wireless network is adjusted based on the optimal solution.

[0011] In an alternative approach, the method further includes:

[0012] The network performance characteristic matrix is ​​differentiated based on the total traffic consumption to obtain the adjustment term;

[0013] With the goal of minimizing the total traffic consumption, the network performance feature matrix is ​​iteratively solved according to the adjustment terms to obtain the optimal solution.

[0014] In an alternative approach, the method further includes:

[0015] The adjustment item is determined according to a first formula, which is:

[0016]

[0017] in, The adjustment term is defined as follows: Q is the total traffic consumption; H is the network performance characteristic matrix at any given time; Q(c,t0) is the traffic matrix corresponding to cell cluster c at time t0; P T V is a probability matrix; max The maximum transmission rate of each cell is determined; t is any time within a preset time period; H(t0) is the network performance characteristic matrix corresponding to time t0.

[0018] In an alternative approach, the method further includes:

[0019] The optimal solution is determined according to the second formula, which is:

[0020]

[0021] Among them, H n Let λ be the network performance feature matrix after the nth iteration, and λ be the preset adjustment step size.

[0022] In an alternative approach, the method further includes:

[0023] Determine the transmission rate information for each of the aforementioned cells;

[0024] Determine the traffic migration probability information between each of the aforementioned cells;

[0025] The network performance characteristic matrix is ​​determined based on the transmission rate information and the traffic migration probability information.

[0026] In one alternative approach, the transmission rate information includes a rate matrix; the rate matrix includes the maximum transmission rate of each of the cells and the maximum traffic migration rate from each of the cells to other cells in the cell cluster; the method further includes:

[0027] Obtain the network configuration parameters of each of the aforementioned cells;

[0028] The congestion-time traffic value, congestion-time guaranteed rate, and maximum transmission rate of each cell are determined based on the network configuration parameters.

[0029] The rate matrix is ​​determined based on the congestion-time traffic value, the congestion-time guaranteed rate, and the maximum transmission rate.

[0030] In one alternative approach, the traffic migration probability information includes a probability matrix; the probability matrix includes the traffic migration probability from each of the cells to other cells in the cell cluster; the method further includes:

[0031] Obtain the network configuration parameters of each of the aforementioned cells;

[0032] The coverage quality information and coverage overlap information of each cell are determined based on the network configuration parameters.

[0033] The probability matrix is ​​determined based on the coverage quality information and the coverage overlap information.

[0034] According to another aspect of the present invention, a wireless network optimization apparatus is provided, comprising:

[0035] The first determining module is used to determine the traffic matrix of the cell cluster within a preset time period; the traffic matrix includes the intra-cell traffic of each cell in the cell cluster and the migration traffic from each cell to other cells in the cell cluster.

[0036] The second determining module is used to determine the network performance characteristic matrix of the wireless network to which the cell cluster is located;

[0037] The third determining module is used to determine the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix.

[0038] The solution module is used to iteratively solve the network performance feature matrix with the goal of minimizing the total traffic consumption and the boundary condition that the sum of traffic in all cells of the cell cluster remains unchanged within the preset time period, in order to obtain the optimal solution.

[0039] An adjustment module is used to adjust the wireless network based on the optimal solution.

[0040] According to another aspect of the present invention, a wireless network optimization device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0041] The memory is used to store at least one executable instruction that causes the processor to perform operations as described in the wireless network optimization method.

[0042] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction that causes a wireless network optimization device to perform the operation of the wireless network optimization method described below.

[0043] This invention, in its embodiments, determines the overall traffic demand of a cell cluster by determining a traffic matrix for a cell cluster within a preset time period. This traffic matrix includes intra-cell traffic in each cell of the cell cluster and migration traffic from each cell to other cells within the cluster. It also determines the network performance characteristic matrix of the wireless network to which the cell cluster resides. This network performance characteristic matrix is ​​obtained by comprehensively considering various network parameters of the wireless network, thus characterizing the overall transmission performance of the wireless network. Finally, it determines the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix. The network performance characteristic matrix can influence the additional traffic required to meet the overall traffic demand. The redundant traffic consumed is minimized. With the goal of minimizing the total traffic consumption, and taking the condition that the sum of traffic within all cells in the cell cluster remains constant within the preset time period as a boundary condition, the network performance characteristic matrix is ​​iteratively solved to obtain the optimal solution. This means that, while maintaining the overall traffic demand, the excessive redundant traffic consumption caused by poor network performance is minimized. Finally, the wireless network is adjusted based on the optimal solution. This allows for the overall adjustment of the wireless network based on the optimized network performance characteristic matrix while continuously reducing redundant traffic. This differs from existing wireless network optimization schemes that optimize from a single dimension. The embodiments of this invention improve the effectiveness of wireless network optimization.

[0044] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0045] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0046] Figure 1 A flowchart illustrating the wireless network optimization method provided in an embodiment of the present invention is shown;

[0047] Figure 2 A schematic diagram of traffic migration in a cellular network provided by an embodiment of the present invention is shown;

[0048] Figure 3A schematic diagram illustrating the relationship between traffic and speed in a cell provided by an embodiment of the present invention is shown;

[0049] Figure 4 This diagram illustrates the relationship between total traffic consumption and the network performance characteristic matrix provided in an embodiment of the present invention.

[0050] Figure 5 A schematic diagram of the structure of the wireless network optimization device provided in an embodiment of the present invention is shown;

[0051] Figure 6 A schematic diagram of the structure of the wireless network optimization device provided in an embodiment of the present invention is shown. Detailed Implementation

[0052] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0053] Figure 1 A flowchart of a wireless network optimization method provided in an embodiment of the present invention is shown. This method is executed by a computer processing device. The computer processing device may include a mobile phone, a laptop computer, etc. Figure 1 As shown, the method includes the following steps:

[0054] Step 101: Determine the traffic matrix of the cell cluster within the preset time period.

[0055] In one embodiment of the present invention, a cell cluster represents several cells related to frequency, traffic, coverage, or commands. The division of cell clusters can be based on frequency reuse, i.e., finding N cells that can use a set of frequencies based on the cell's frequency information, or grouping several cells related to traffic, coverage, and commands into a cell cluster based on the cell's traffic volume and handover data. The preset time period can be a period of relatively stable user traffic demand, such as morning and evening peak hours, off-peak hours, or the period when a certain crowd gathering event occurs, such as the opening ceremony of a sporting event or a gala.

[0056] In one embodiment of the present invention, the traffic matrix represents the traffic maintained within each cell in the cell cluster and the traffic as users migrate between different cells. Since the user traffic demand is relatively stable during the preset time period, the sum of the intra-cell traffic of all cells in the cell cluster during the preset time period can be regarded as a constant.

[0057] The traffic migration process can be referenced here. Figure 2Based on the dynamic service characteristics of mobile cellular networks, users continuously migrate within neighboring areas, and the transmitted service q moves with the user and is continuously transmitted in adjacent areas, with its unit being bits. Traffic migrates between cells within the cell cluster as the user moves, but the total traffic demanded by the user within the cell cluster remains constant. Therefore, the intra-cell traffic of each cell within the cell cluster is equal to the difference between its inflow and outflow.

[0058] In another embodiment of the present invention, the traffic matrix includes intra-cell traffic of each cell in the cell cluster and migration traffic from each cell to other cells in the cell cluster, which can be specifically represented as follows:

[0059]

[0060] Where Q(t) represents the flow matrix of the cell cluster at time t; q ii (t) represents the intra-cell flow maintained in cell i, q ij (t) represents the migration flow from cell i to cell j; x represents the number of cells in the cluster.

[0061] Step 102: Determine the network performance characteristic matrix of the wireless network to which the cell cluster is located.

[0062] In one embodiment of the present invention, the network performance of a cell cluster is determined by multiple network configuration parameters. For example, the cell's transmit power affects its signal coverage, while the cell's antenna azimuth angle affects the migration rate and probability between the cell and neighboring cells. When a cell's signal coverage is poor, its traffic transmission rate will slow down. When the vertical angle of the cell's antenna is small, its overlapping coverage area with neighboring cells is larger, thus increasing the probability and rate of traffic migration between the cell and neighboring cells. Slow cell transmission rates or slow migration rates between cells and neighboring cells will negatively impact the network perception of migrating users.

[0063] Optimizing the network solely based on the single-dimensional network parameters of a single cell is insufficient for improving the overall network performance of the cell cluster, given the network mechanism where traffic migrates continuously between cells within the cluster. Therefore, in one embodiment of this invention, the network performance feature matrix includes parameters characterizing the transmission performance of a single cell and network configuration parameters characterizing the transmission performance of migration traffic between cells. By optimizing the entire network performance feature matrix and then mapping the optimized results back to the network configuration parameters, adjustments to the network configuration parameters are achieved.

[0064] Therefore, in one embodiment of the present invention, step 102 further includes:

[0065] Step 1021: Determine the transmission rate information of each of the cells.

[0066] In one embodiment of the present invention, transmission rate information is used to characterize the rate performance of network resource transmission within a cell and between cells.

[0067] In another embodiment of the present invention, the transmission rate information includes a rate matrix; the rate matrix includes the maximum transmission rate of each of the cells and the maximum traffic migration rate from each of the cells to other cells in the cell cluster; step 1021 further includes:

[0068] Step 211: Obtain the network configuration parameters for each cell.

[0069] In one embodiment of the present invention, network configuration parameters may include configuration parameters such as cell carrier, time slot, code channel, and power.

[0070] Step 212: Determine the congestion-time traffic value, congestion-time guaranteed rate, and maximum transmission rate for each cell based on the network configuration parameters.

[0071] In one embodiment of the present invention, during the migration of a user between cells, the rate V also migrates with the user and is continuously transmitted in adjacent areas, with the unit being bps. Here, V represents the available or achievable rate when data transmission is possible, rather than the average rate of service over a period of time. When the rate falls below a threshold, cell congestion occurs. The congestion-time traffic value refers to the value at which congestion occurs within the cell. The congestion-time guaranteed rate refers to the minimum transmission rate that the cell can guarantee to achieve when congestion occurs, and the maximum transmission rate refers to the maximum transmission rate that the cell can achieve.

[0072] Step 213: Determine the rate matrix based on the congestion-time flow value, the congestion-time guaranteed rate, and the maximum transmission rate.

[0073] In one embodiment of the present invention, the rate is correlated with the traffic volume during congestion, the guaranteed rate during congestion, and the maximum transmission rate, and the resulting rate matrix can be specifically represented as follows:

[0074]

[0075] Where V is the rate matrix corresponding to the cell cluster, v ii v represents the maximum transmission rate of cell i in the cell cluster. ij This represents the maximum traffic migration rate from cell i to cell j, where cells i to j are all located in the same cell cluster.

[0076] The rate matrix is ​​obtained by expanding the dimensions based on the relationship between traffic and rate in a single cell, with the number of cells as the dimension. The relationship between traffic and rate in a single cell j can be found by referring to... Figure 3 .

[0077] like Figure 3 As shown, when q jj ≤q jj1 hour, When q jj >q jj1 ,v jj =v jj1 Among them, v jj1 It is the congestion-guaranteed rate of cell j, v jj_max This is the maximum transmission rate of cell j. Depending on the actual situation and different scenarios, v... jj_max q jj1 and v jj1 They are different.

[0078] Extending the above two equations to matrix form based on the number of cells, we get:

[0079] V = V max +((V1-V max ). / Q1) T Q

[0080] Where V is the rate matrix, . / represents point division, the congestion-time flow matrix Q1 is a matrix composed of the congestion-time flow values ​​of each cell, the threshold matrix V1 is the minimum rate matrix between cells during congestion, and the maximum rate matrix V max This represents the maximum transmission rate between cells. Where V1 and V... max Q1 represents the communication performance mechanism and has a mapping relationship with the actual network configuration parameters.

[0081] Step 1022: Determine the traffic migration probability information between each of the cells.

[0082] In one embodiment of the present invention, traffic migration probability information is used to characterize the selection probability of traffic migration between cells. The higher the probability, the higher the proportion of outflow traffic from that cell to other cells in the cell cluster corresponding to that probability. That is, traffic migration probability can be equated with traffic migration proportion.

[0083] In another embodiment of the present invention, the traffic migration probability information includes a probability matrix; the probability matrix includes the traffic migration probability from each of the cells to other cells in the cell cluster.

[0084] Step 1022 also includes: Step 221: Obtain the network configuration parameters of each cell.

[0085] In one embodiment of the present invention, step 221 is similar to the aforementioned step 211, and will not be described again.

[0086] Step 222: Determine the coverage quality information and coverage overlap information of each cell based on the network configuration parameters.

[0087] In one embodiment of the present invention, coverage quality information can be determined based on network configuration parameters related to signal quality, such as the RSRP level of a cell, and coverage overlap information can be determined based on network configuration parameters related to the coverage area, such as the antenna angle of each cell.

[0088] Step 223: Determine the probability matrix based on the coverage quality information and the coverage overlap information.

[0089] In one embodiment of the present invention, the probability matrix can be represented as follows:

[0090]

[0091] Where, p ij is the traffic migration probability from cell i to cell j, and x is the number of cells in the cell cluster.

[0092] Step 1023: Determine the network performance feature matrix based on the transmission rate information and the traffic migration probability information.

[0093] In one embodiment of the present invention, the network performance feature matrix (denoted as H) is determined according to the following formula:

[0094] H = P T (V1-V max ). / Q1) T ;

[0095] Where P is the aforementioned probability matrix, V1, V max The meanings of , . / and Q1 have been explained in step 213 above and will not be repeated here.

[0096] Step 103: Determine the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix.

[0097] In one embodiment of the present invention, referring to the Euler gas flow equation and expanding the dimensions according to the number of cells, the relationship between total consumption flow and the network performance characteristic matrix and the flow matrix is ​​obtained as follows:

[0098] DE:Q t +(V) c =0; t o <t<To

[0099]

[0100]

[0101] Among them, D.E is the dynamic equation, B.C is the boundary condition, and I.C is the initial condition. Among them, D.E represents the performance characteristics and service characteristics of the communication system, B.C indicates that the total service demand in the cell cluster remains unchanged, and I.C represents the traffic at the initial moment in each cell of the cell cluster.

[0102] Among them, Q t represents the integral of the total consumed traffic Q with respect to time t; (*) c = P T × (*); V is the aforementioned rate matrix; t o < t < To represents the preset time period [t o , To]; q j represents the intra-cell traffic of cell j; The intra-cell traffic of cell j is expressed as the difference between the traffic flowing into this cell from other cells in the cell cluster and the traffic flowing out of this cell to other cells.

[0103] Step 104: Taking the minimum of the total consumed traffic as the objective and the sum of all the intra-cell traffic in the cell cluster within the preset time period remaining unchanged as the boundary condition, perform iterative solution on the network performance characteristic matrix to obtain the optimal solution.

[0104] In an embodiment of the present invention, by solving the dynamic equation in step 103, the following can be obtained:

[0105]

[0106] Among them, Q(t, H) represents the total consumed traffic. It can be seen that Q is a function of time t and mechanism H. Considering the transmission principle of the cellular network, in order to ensure the correct transmission of the data to be transmitted, additional information such as retransmitted data and redundant information is introduced. Denote this additional traffic as Q″. Therefore, the total consumed traffic in the cell cluster can be divided into two parts. The first part is the original user demand data of all users in the cell, denoted as Q′, which can be regarded as unchanged within the preset time period as described above, and the other part is the aforementioned Q″. Therefore, Q(t, H) can also be written as Q′(t) + Q″(H).

[0107] Considering that redundant information inevitably exists in any communication system, therefore, Q(t, H) is Figure 4 the convex function shown in, so H when Q takes the minimum value is obtained as the optimal solution. When finding the minimum value, iterative differentiation of Q with respect to H can be used to gradually approximate as Figure 4The optimal solution shown corresponds to H.

[0108] Therefore, in another embodiment of the present invention, step 104 further includes:

[0109] Step 1041: Differentiate the network performance feature matrix based on the total traffic consumption to obtain the adjustment term.

[0110] In one embodiment of the present invention, combined with Figure 4 The differential method gradually approaches the minimum total traffic consumption, and each time the distance between the current network performance feature matrix and the optimal solution is shortened according to the adjustment term.

[0111] In yet another embodiment of the present invention, step 1041 further includes:

[0112] The adjustment item is determined according to a first formula, which is:

[0113]

[0114] in, The adjustment term is defined as follows: Q is the total traffic consumption; H is the network performance characteristic matrix at any given time; Q(c,t0) is the traffic matrix corresponding to cell cluster c at time t0; P T V is a probability matrix; max The maximum transmission rate of each cell is determined; t is any time within a preset time period; H(t0) is the network performance characteristic matrix corresponding to time t0.

[0115] In one embodiment of the present invention, the process of obtaining the above first formula by differentiating Q with respect to H is as follows:

[0116]

[0117] Step 1042: With the goal of minimizing the total traffic consumption, iteratively solve the network performance feature matrix according to the adjustment terms to obtain the optimal solution.

[0118] In one embodiment of the present invention, the adjustment term is processed according to a certain adjustment step size and added to the network performance feature matrix obtained in the previous iteration to obtain the iterated network performance feature matrix. The process of iterative approximation is repeated multiple times until the optimal solution is obtained.

[0119] In yet another embodiment of the invention, combined with Figure 4 Step 1042 also includes:

[0120] The optimal solution is determined according to the second formula, which is:

[0121]

[0122] Among them, H n Let be the network performance feature matrix after the nth iteration, and λ be the preset adjustment step size. Specifically, λ is usually small, such as 0.01.

[0123] Substituting the first formula into the second formula, the relationship between the iterated network performance feature matrix and Q(c,t0) is as follows:

[0124]

[0125] Through multiple iterations, H n It will continuously approach the optimal solution. In one embodiment of the present invention, when the number of iterations reaches a preset threshold or H... n When the convergence gradually stops changing, then the H obtained in the current iteration is... n This is determined to be the optimal solution.

[0126] Step 105: Adjust the wireless network according to the optimal solution.

[0127] In one embodiment of the present invention, after determining the network performance matrix corresponding to the minimum total traffic consumption, i.e. the optimal solution, the network configuration parameters are coordinated according to the mapping relationship between the network performance matrix and the network configuration parameters described in step 102, i.e., all network configuration parameters are adjusted together, thereby optimizing the overall performance of the entire network.

[0128] The wireless network optimization method provided in this invention determines the overall traffic demand of a cell cluster by determining the traffic matrix of a cell cluster within a preset time period. The traffic matrix includes intra-cell traffic in each cell of the cell cluster and migration traffic from each cell to other cells in the cell cluster. It also determines the network performance characteristic matrix of the wireless network to which the cell cluster resides, where the network performance characteristic matrix is ​​obtained by comprehensively considering various network parameters of the wireless network, thus characterizing the overall transmission performance of the wireless network. Finally, it determines the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix. The network performance characteristic matrix can influence the additional traffic required to meet the overall traffic demand. The method aims to minimize the total traffic consumption, using the condition that the sum of traffic within all cells in the cell cluster remains constant during the preset time period as a boundary condition. It iteratively solves the network performance characteristic matrix to obtain the optimal solution. This means minimizing excessive redundant traffic consumption caused by poor network performance while maintaining overall traffic demand. Finally, the wireless network is adjusted based on the optimal solution. This allows for the overall adjustment of the wireless network based on the optimized network performance characteristic matrix while continuously reducing redundant traffic. This differs from existing wireless network optimization schemes that optimize from a single dimension. The wireless network optimization method provided in this embodiment improves the effectiveness of wireless network optimization.

[0129] Figure 5 A schematic diagram of the wireless network optimization device provided in an embodiment of the present invention is shown. Figure 5 As shown, the device 200 includes: a first determining module 201, a second determining module 202, a third determining module 203, a solving module 204, and an adjusting module 205, wherein,

[0130] The first determining module 201 is used to determine the traffic matrix of a cell cluster within a preset time period; the traffic matrix includes the intra-cell traffic of each cell in the cell cluster and the migration traffic from each cell to other cells in the cell cluster.

[0131] The second determining module 202 is used to determine the network performance characteristic matrix of the wireless network where the cell cluster is located;

[0132] The third determining module 203 is used to determine the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix.

[0133] The solution module 204 is used to iteratively solve the network performance feature matrix with the goal of minimizing the total traffic consumption and the boundary condition that the sum of traffic in all cells of the cell cluster remains unchanged within the preset time period, in order to obtain the optimal solution.

[0134] Adjustment module 205 is used to adjust the wireless network according to the optimal solution.

[0135] In an alternative embodiment, the second determining module 203 is further configured to:

[0136] Determine the transmission rate information for each of the aforementioned cells;

[0137] Determine the traffic migration probability information between each of the aforementioned cells;

[0138] The network performance characteristic matrix is ​​determined based on the transmission rate information and the traffic migration probability information.

[0139] In one alternative embodiment, the transmission rate information includes a rate matrix; the rate matrix includes the maximum transmission rate of each of the cells and the maximum traffic migration rate from each of the cells to other cells in the cell cluster; the second determining module 203 is further configured to:

[0140] Obtain the network configuration parameters of each of the aforementioned cells;

[0141] The congestion-time traffic value, congestion-time guaranteed rate, and maximum transmission rate of each cell are determined based on the network configuration parameters.

[0142] The rate matrix is ​​determined based on the congestion-time traffic value, the congestion-time guaranteed rate, and the maximum transmission rate.

[0143] In one optional embodiment, the traffic migration probability information includes a probability matrix; the probability matrix includes the traffic migration probability from each of the cells to other cells in the cell cluster; the second determining module 203 is further configured to:

[0144] Obtain the network configuration parameters for each cell;

[0145] The coverage quality information and coverage overlap information of each cell are determined based on the network configuration parameters.

[0146] The probability matrix is ​​determined based on the coverage quality information and the coverage overlap information.

[0147] In an alternative approach, the solver module 204 is also used for:

[0148] The network performance characteristic matrix is ​​differentiated based on the total traffic consumption to obtain the adjustment term;

[0149] With the goal of minimizing the total traffic consumption, the network performance feature matrix is ​​iteratively solved according to the adjustment terms to obtain the optimal solution.

[0150] In an alternative approach, the solver module 204 is also used for:

[0151] The adjustment item is determined according to a first formula, which is:

[0152]

[0153] in, The adjustment term is defined as follows: Q is the total traffic consumption; H is the network performance characteristic matrix at any given time; Q(c,t0) is the traffic matrix corresponding to cell cluster c at time t0; P T V is a probability matrix; max The maximum transmission rate of each cell is determined; t is any time within a preset time period; H(t0) is the network performance characteristic matrix corresponding to time t0.

[0154] In an alternative approach, the solver module 204 is also used for:

[0155] The optimal solution is determined according to the second formula, which is:

[0156]

[0157] Among them, H n Let λ be the network performance feature matrix after the nth iteration, and λ be the preset adjustment step size.

[0158] The wireless network optimization device provided in this embodiment of the invention determines the overall traffic demand of a cell cluster by determining the traffic matrix of a cell cluster within a preset time period. The traffic matrix includes intra-cell traffic of each cell in the cell cluster and migration traffic from each cell to other cells in the cell cluster. It also determines the network performance characteristic matrix of the wireless network to which the cell cluster resides, where the network performance characteristic matrix is ​​obtained by comprehensively considering various network parameters of the wireless network, thus characterizing the overall transmission performance of the wireless network. Finally, it determines the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix. The network performance characteristic matrix can influence the additional traffic required to meet the overall traffic demand. The redundant traffic consumed is minimized. With the goal of minimizing the total traffic consumption, and taking the condition that the sum of traffic within all cells in the cell cluster remains constant within the preset time period as a boundary condition, the network performance characteristic matrix is ​​iteratively solved to obtain the optimal solution. This means that, while maintaining the overall traffic demand, the excessive redundant traffic consumption caused by poor network performance is minimized. Finally, the wireless network is adjusted based on the optimal solution. This allows for the overall adjustment of the wireless network based on the optimized network performance characteristic matrix while continuously reducing redundant traffic. This differs from existing wireless network optimization schemes that optimize from a single dimension. The wireless network optimization device provided in this embodiment of the invention improves the effectiveness of wireless network optimization.

[0159] Figure 6 The diagram shows a structural schematic of a wireless network optimization device provided in an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the wireless network optimization device.

[0160] like Figure 6 As shown, the wireless network optimization device may include: a processor 302, a communications interface 304, a memory 306, and a communications bus 308.

[0161] The processor 302, communication interface 304, and memory 306 communicate with each other via communication bus 308. Communication interface 304 is used to communicate with other network elements, such as clients or other servers. The processor 302 executes program 310, specifically performing the relevant steps described in the embodiments of the wireless network optimization method.

[0162] Specifically, program 310 may include program code, which includes computer-executable instructions.

[0163] Processor 302 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The wireless network optimization device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0164] Memory 306 is used to store program 310. Memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0165] Specifically, program 310 can be called by processor 302 to cause the wireless network optimization device to perform the following operations:

[0166] Determine the traffic matrix of the cell cluster within a preset time period; the traffic matrix includes the intra-cell traffic of each cell in the cell cluster and the migration traffic from each cell to other cells in the cell cluster;

[0167] Determine the network performance characteristic matrix of the wireless network to which the cell cluster belongs;

[0168] The total traffic consumption of the cell cluster is determined based on the network performance characteristic matrix and the traffic matrix.

[0169] With the goal of minimizing the total traffic consumption, and taking the condition that the sum of traffic within all cells in the cell cluster remains unchanged during the preset time period as the boundary condition, the network performance feature matrix is ​​iteratively solved to obtain the optimal solution;

[0170] The wireless network is adjusted based on the optimal solution.

[0171] In an alternative manner, the program 310 is invoked by the processor 302 to cause the wireless network optimization device to perform the following operations:

[0172] The network performance characteristic matrix is ​​differentiated based on the total traffic consumption to obtain the adjustment term;

[0173] With the goal of minimizing the total traffic consumption, the network performance feature matrix is ​​iteratively solved according to the adjustment terms to obtain the optimal solution.

[0174] In an alternative manner, the program 310 is invoked by the processor 302 to cause the wireless network optimization device to perform the following operations:

[0175] The adjustment item is determined according to a first formula, which is:

[0176]

[0177] in, The adjustment term is defined as follows: Q is the total traffic consumption; H is the network performance characteristic matrix at any given time; Q(c,t0) is the traffic matrix corresponding to cell cluster c at time t0; P T V is a probability matrix; max The maximum transmission rate of each cell is determined; t is any time within a preset time period; H(t0) is the network performance characteristic matrix corresponding to time t0.

[0178] In an alternative manner, the program 310 is invoked by the processor 302 to cause the wireless network optimization device to perform the following operations:

[0179] The optimal solution is determined according to the second formula, which is:

[0180]

[0181] Among them, H n Let λ be the network performance feature matrix after the nth iteration, and λ be the preset adjustment step size.

[0182] In an alternative manner, the program 310 is invoked by the processor 302 to cause the wireless network optimization device to perform the following operations:

[0183] Determine the transmission rate information for each of the aforementioned cells;

[0184] Determine the traffic migration probability information between each of the aforementioned cells;

[0185] The network performance feature matrix is ​​determined based on the transmission rate information and the traffic migration probability information.

[0186] In one alternative approach, the transmission rate information includes a rate matrix; the rate matrix includes the maximum transmission rate of each of the cells and the maximum traffic migration rate from each of the cells to other cells in the cell cluster; the program 310 is invoked by the processor 302 to cause the wireless network optimization device to perform the following operations:

[0187] Obtain the network configuration parameters of each of the aforementioned cells;

[0188] The congestion-time traffic value, congestion-time guaranteed rate, and maximum transmission rate of each cell are determined based on the network configuration parameters.

[0189] The rate matrix is ​​determined based on the congestion-time traffic value, the congestion-time guaranteed rate, and the maximum transmission rate.

[0190] In one alternative approach, the traffic migration probability information includes a probability matrix; the probability matrix includes the traffic migration probability from each of the cells to other cells in the cell cluster; the program 310 is invoked by the processor 302 to cause the wireless network optimization device to perform the following operations:

[0191] Obtain the network configuration parameters for each cell;

[0192] The coverage quality information and coverage overlap information of each cell are determined based on the network configuration parameters.

[0193] The probability matrix is ​​determined based on the coverage quality information and the coverage overlap information.

[0194] The wireless network optimization device provided in this embodiment of the invention determines the overall traffic demand of a cell cluster by determining the traffic matrix of a cell cluster within a preset time period. The traffic matrix includes intra-cell traffic of each cell in the cell cluster and migration traffic from each cell to other cells in the cell cluster. It also determines the network performance characteristic matrix of the wireless network to which the cell cluster resides, where the network performance characteristic matrix is ​​obtained by comprehensively considering various network parameters of the wireless network, thus characterizing the overall transmission performance of the wireless network. Finally, it determines the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix. The network performance characteristic matrix can influence the additional traffic required to meet the overall traffic demand. The redundant traffic consumed is minimized. With the goal of minimizing the total traffic consumption, and taking the condition that the sum of traffic within all cells in the cell cluster remains constant within the preset time period as a boundary condition, the network performance characteristic matrix is ​​iteratively solved to obtain the optimal solution. This means that, while maintaining the overall traffic demand, the excessive redundant traffic consumption caused by poor network performance is minimized. Finally, the wireless network is adjusted based on the optimal solution. This allows for the overall adjustment of the wireless network based on the optimized network performance characteristic matrix while continuously reducing redundant traffic. This differs from existing wireless network optimization schemes that optimize from a single dimension. The wireless network optimization device provided in this embodiment of the invention improves the effectiveness of wireless network optimization.

[0195] This invention provides a computer-readable storage medium storing at least one executable instruction that, when executed on a wireless network optimization device, causes the wireless network optimization device to perform the wireless network optimization method in any of the above method embodiments.

[0196] Specifically, the executable instructions can be used to cause the wireless network optimization device to perform the following operations:

[0197] Determine the traffic matrix of the cell cluster within a preset time period; the traffic matrix includes the intra-cell traffic of each cell in the cell cluster and the migration traffic from each cell to other cells in the cell cluster;

[0198] Determine the network performance characteristic matrix of the wireless network to which the cell cluster belongs;

[0199] The total traffic consumption of the cell cluster is determined based on the network performance characteristic matrix and the traffic matrix.

[0200] With the goal of minimizing the total traffic consumption, and taking the condition that the sum of traffic within all cells in the cell cluster remains unchanged during the preset time period as the boundary condition, the network performance feature matrix is ​​iteratively solved to obtain the optimal solution;

[0201] The wireless network is adjusted based on the optimal solution.

[0202] In one alternative approach, the executable instructions cause the wireless network optimization device to perform the following operations:

[0203] The network performance characteristic matrix is ​​differentiated based on the total traffic consumption to obtain the adjustment term;

[0204] With the goal of minimizing the total traffic consumption, the network performance feature matrix is ​​iteratively solved according to the adjustment terms to obtain the optimal solution.

[0205] In one alternative approach, the executable instructions cause the wireless network optimization device to perform the following operations:

[0206] The adjustment item is determined according to a first formula, which is:

[0207]

[0208] in, The adjustment term is defined as follows: Q is the total traffic consumption; H is the network performance characteristic matrix at any given time; Q(c,t0) is the traffic matrix corresponding to cell cluster c at time t0; P T V is a probability matrix; max The maximum transmission rate of each cell is determined; t is any time within a preset time period; H(t0) is the network performance feature matrix corresponding to time t0.

[0209] In one alternative approach, the executable instructions cause the wireless network optimization device to perform the following operations:

[0210] The optimal solution is determined according to a second formula, which is:

[0211]

[0212] Among them, H n Let λ be the network performance feature matrix after the nth iteration, and λ be the preset adjustment step size.

[0213] In one alternative approach, the executable instructions cause the wireless network optimization device to perform the following operations:

[0214] Determine the transmission rate information for each of the aforementioned cells;

[0215] Determine the traffic migration probability information between each of the aforementioned cells;

[0216] The network performance feature matrix is ​​determined based on the transmission rate information and the traffic migration probability information.

[0217] In one alternative approach, the transmission rate information includes a rate matrix; the rate matrix includes the maximum transmission rate of each of the cells and the maximum traffic migration rate from each of the cells to other cells in the cell cluster; the executable instructions cause the wireless network optimization device to perform the following operations:

[0218] Obtain the network configuration parameters of each of the aforementioned cells;

[0219] The congestion-time traffic value, congestion-time guaranteed rate, and maximum transmission rate of each cell are determined based on the network configuration parameters.

[0220] The rate matrix is ​​determined based on the congestion-time traffic value, the congestion-time guaranteed rate, and the maximum transmission rate.

[0221] In one alternative approach, the traffic migration probability information includes a probability matrix; the probability matrix includes the traffic migration probability from each of the cells to other cells in the cell cluster; the executable instructions cause the wireless network optimization device to perform the following operations:

[0222] Obtain the network configuration parameters for each cell;

[0223] The coverage quality information and coverage overlap information of each cell are determined based on the network configuration parameters.

[0224] The probability matrix is ​​determined based on the coverage quality information and the coverage overlap information.

[0225] The computer storage medium provided in this embodiment of the invention determines the overall traffic demand of a cell cluster by determining a traffic matrix of a cell cluster within a preset time period; wherein the traffic matrix includes intra-cell traffic of each cell in the cell cluster and migration traffic from each cell to other cells in the cell cluster; determining a network performance characteristic matrix of the wireless network to which the cell cluster is located, wherein the network performance characteristic matrix is ​​obtained by comprehensively considering various network parameters of the wireless network, and characterizes the overall transmission performance of the wireless network; and determining the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix, wherein the network performance characteristic matrix can influence the additional traffic required to meet the overall traffic demand. The redundant traffic consumed is minimized. With the goal of minimizing the total traffic consumption, and taking the condition that the sum of traffic within all cells in the cell cluster remains constant within the preset time period as a boundary condition, the network performance characteristic matrix is ​​iteratively solved to obtain the optimal solution. This means minimizing the consumption of excessive redundant traffic caused by poor network performance while keeping the overall traffic demand constant. Finally, the wireless network is adjusted based on the optimal solution. This allows for the overall adjustment of the wireless network based on the optimized network performance characteristic matrix while continuously reducing redundant traffic. This differs from existing wireless network optimization schemes that optimize from a single dimension. The computer storage medium provided in this embodiment of the invention improves the effectiveness of wireless network optimization.

[0226] This invention provides a wireless network optimization device for performing the above-described wireless network optimization method.

[0227] This invention provides a computer program that can be called by a processor to cause a wireless network optimization device to execute the wireless network optimization method in any of the above method embodiments.

[0228] This invention provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions that, when executed on a computer, cause the computer to perform the wireless network optimization method in any of the above method embodiments.

[0229] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0230] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0231] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0232] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0233] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A wireless network optimization method, characterized in that, The method includes: Determine the traffic matrix of the cell cluster within a preset time period; the traffic matrix includes the intra-cell traffic of each cell in the cell cluster and the migration traffic from each cell to other cells in the cell cluster; Determine the network performance characteristic matrix of the wireless network to which the cell cluster belongs; wherein, determine the transmission rate information of each cell; determine the traffic migration probability information between each cell; and determine the network performance characteristic matrix based on the transmission rate information and the traffic migration probability information; The total traffic consumption of the cell cluster is determined based on the network performance characteristic matrix and the traffic matrix. With the goal of minimizing the total traffic consumption, and taking the condition that the sum of traffic within all cells in the cell cluster remains unchanged during the preset time period as the boundary condition, the network performance feature matrix is ​​iteratively solved to obtain the optimal solution; The wireless network is adjusted based on the optimal solution.

2. The method according to claim 1, characterized in that, The optimal solution is obtained by iteratively solving the network performance feature matrix with the objective of minimizing the total traffic consumption and the boundary condition that the sum of traffic within all cells in the cell cluster remains unchanged during the preset time period. This includes: The network performance characteristic matrix is ​​differentiated based on the total traffic consumption to obtain the adjustment term; With the goal of minimizing the total traffic consumption, the network performance feature matrix is ​​iteratively solved according to the adjustment terms to obtain the optimal solution.

3. The method according to claim 2, characterized in that, The step of differentiating the network performance feature matrix based on the total traffic consumption to obtain the adjustment term includes: The adjustment item is determined according to a first formula, which is: ； in, The adjustment item is Q; Q is the total flow consumption. Let be the network performance feature matrix at any given time. For cluster c in The flow matrix corresponding to the given time; It is a probability matrix; Determined based on the maximum transmission rate of each cell; t is any time within a preset time period; for The network performance feature matrix corresponding to the given time.

4. The method according to claim 3, characterized in that, The process of obtaining the optimal solution by iteratively solving the network performance feature matrix based on the adjustment term, with the goal of minimizing the total traffic consumption, includes: The optimal solution is determined according to the second formula, which is: ； in, This is the network performance feature matrix after the nth iteration. This is the preset adjustment step size.

5. The method according to claim 1, characterized in that, The transmission rate information includes a rate matrix; the rate matrix includes the maximum transmission rate of each cell and the maximum traffic migration rate from each cell to other cells in the cell cluster. Determining the transmission rate information of each of the cells includes: Obtain the network configuration parameters of each of the aforementioned cells; The congestion-time traffic value, congestion-time guaranteed rate, and maximum transmission rate of each cell are determined based on the network configuration parameters. The rate matrix is ​​determined based on the congestion-time traffic value, the congestion-time guaranteed rate, and the maximum transmission rate.

6. The method according to claim 1, characterized in that, The traffic migration probability information includes a probability matrix; the probability matrix includes the traffic migration probability from each cell to other cells in the cell cluster; Determining the traffic migration probability information between each of the cells includes: Obtain the network configuration parameters for each cell; The coverage quality information and coverage overlap information of each cell are determined based on the network configuration parameters. The probability matrix is ​​determined based on the coverage quality information and the coverage overlap information.

7. A wireless network optimization device, characterized in that, The device includes: The first determining module is used to determine the traffic matrix of the cell cluster within a preset time period; the traffic matrix includes the intra-cell traffic of each cell in the cell cluster and the migration traffic from each cell to other cells in the cell cluster. The second determining module is used to determine the network performance characteristic matrix of the wireless network to which the cell cluster is located; wherein, the transmission rate information of each cell is determined; the traffic migration probability information between each cell is determined; and the network performance characteristic matrix is ​​determined based on the transmission rate information and the traffic migration probability information. The third determining module is used to determine the total traffic consumption of the cell cluster based on the network performance characteristic matrix and the traffic matrix. The solution module is used to iteratively solve the network performance feature matrix with the goal of minimizing the total traffic consumption and the boundary condition that the sum of traffic in all cells of the cell cluster remains unchanged within the preset time period, in order to obtain the optimal solution. An adjustment module is used to adjust the wireless network based on the optimal solution.

8. A wireless network optimization device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the wireless network optimization method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the wireless network optimization device, causes the wireless network optimization device to perform the operation of the wireless network optimization method as described in any one of claims 1-6.

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