Cell capacity evaluation and inter-cell load balancing method, device and electronic equipment

Abstract

The present application discloses a method, device and electronic device for evaluating cell capacity and load balancing between cells, which are used to achieve accurate evaluation of cell capacity. The cell capacity evaluation method includes: obtaining historical sampling data of the cell to be evaluated, wherein the cell to be evaluated is a cell in a large-scale multi-input multi-output MassiveMIMO system, and the historical sampling data includes sampling point data and the number of physical resource blocks (PRBs) measured at the sampling points after the cell to be evaluated adds a multi-user multi-input multi-output (MU‑MIMO) mode; based on the historical sampling data, determining the PRB normal distribution model parameters of the cell to be evaluated, the PRB normal distribution model parameters include the expected value of the number of PRBs and the standard deviation of the number of PRBs; based on the PRB normal distribution model parameters, evaluating the capacity of the cell to be evaluated.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device and electronic equipment for evaluating cell capacity and balancing load between cells. Background Art

[0002] Massive Multiple-Input Multiple-Output (Massive MIMO) is one of the core technologies in wireless networks such as 4G and 5G, and is mainly used to improve system capacity and spectrum utilization. Massive MIMO technology is widely used in macro base station systems such as 4G and 5G. With the help of multi-beamforming technology of large-scale array lines, uplink and downlink multi-stream multiplexing and multiple-user-multiple-input-multiple-output (MU-MIMO) are realized.

[0003] At present, when evaluating the capacity of a Massive MIMO cell, the utilization rate of the physical resource block (PRB) of the cell is mainly used for evaluation. For example, the service flow generated by the Massive MIMO cell in the existing network when the PRB utilization rate is the busiest is compared with the service flow flow generated by the non-Massive MIMO cell under the same PRB utilization rate, and it is determined that the Massive MIMO cell is equivalent to multiple non-Massive MIMO cells. Therefore, the capacity of the Massive MIMO cell is evaluated based on the capacity of the non-Massive MIMO cell.

[0004] However, due to the multi-stream multiplexing gain of the cell brought by MU-MIMO technology, the PRB utilization rate in the network management indicators only counts the occupancy of the largest layer of time-frequency resources among the 16 layers of data streams in the cell, and the data traffic and PRB occupancy of other layers are negligible in the indicator presentation. Therefore, the PRB utilization rate cannot accurately reflect the capacity of the cell. In addition, the existing cell capacity assessment method only supports a rough and fuzzy assessment of 4G, while 5G is limited by the insufficient development of network and business scale, and therefore cannot accurately assess the capacity of the cell. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method, device and electronic device for evaluating cell capacity and load balancing between cells, so as to achieve accurate evaluation of cell capacity.

[0006] In order to solve the above technical problems, the embodiments of the present application adopt the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a cell capacity assessment method, including:

[0008] Acquire historical sampling data of a cell to be evaluated, wherein the cell to be evaluated is a cell in a massive multiple-input multiple-output Massive MIMO system, and the historical sampling data includes sampling point data and the number of physical resource blocks (PRBs) of the cell to be evaluated measured at the sampling point after a multi-user multiple-input multiple-output (MU-MIMO) mode is added;

[0009] Determine, based on the historical sampling data, PRB normal distribution model parameters of the cell to be evaluated, wherein the PRB normal distribution model parameters include an expected value of the number of PRBs and a standard deviation of the number of PRBs;

[0010] Based on the PRB normal distribution model parameters, the capacity of the cell to be evaluated is evaluated.

[0011] In a second aspect, an embodiment of the present application provides a cell load balancing method, including:

[0012] Obtain the capacity and PRB occupancy information of cells in the entire network;

[0013] Determine a source cell and a target cell based on the PRB occupancy information of the cells in the entire network, wherein the source cell is a cell in a Massive MIMO system, and the target cell is a cell in a non-Massive MIMO system, or the source cell is a cell in a non-Massive MIMO system, and the target cell is a cell in a Massive MIMO system;

[0014] Determine, based on the PRB occupancy information and capacity of the source cell and the target cell, respectively, a target number of PRBs balanced from the source cell to the target cell;

[0015] Based on the target PRB quantity, load balancing processing is performed on the source cell and the target cell.

[0016] In a third aspect, an embodiment of the present application provides a cell capacity assessment device, including:

[0017] A first acquisition unit is used to acquire historical sampling data of a cell to be evaluated, wherein the cell to be evaluated is a cell in a massive multiple-input multiple-output Massive MIMO system, and the historical sampling data includes sampling point data and the number of physical resource blocks (PRBs) of the cell to be evaluated measured at the sampling point after a multi-user multiple-input multiple-output (MU-MIMO) mode is added;

[0018] A model parameter determination unit, configured to determine the PRB normal distribution model parameters of the cell to be evaluated based on the historical sampling data, wherein the PRB normal distribution model parameters include an expected value of the number of PRBs and a standard deviation of the number of PRBs;

[0019] A capacity evaluation unit is used to evaluate the capacity of the cell to be evaluated based on the PRB normal distribution model parameters.

[0020] In a fourth aspect, an embodiment of the present application provides a cell load balancing device, including:

[0021] A second acquisition unit is used to acquire the capacity and PRB occupancy information of cells in the entire network;

[0022] a cell determination unit, configured to determine a source cell and a target cell based on PRB occupancy information of cells in the entire network, wherein the source cell is a cell in a Massive MIMO system, and the target cell is a cell in a non-Massive MIMO system, or the source cell is a cell in a non-Massive MIMO system, and the target cell is a cell in a Massive MIMO system;

[0023] A PRB quantity determination unit, configured to determine a target PRB quantity balanced from the source cell to the target cell based on the PRB occupancy information and capacity of the source cell and the target cell respectively;

[0024] A balancing processing unit is used to perform load balancing processing on the source cell and the target cell based on the target PRB number.

[0025] In a fifth aspect, an embodiment of the present application provides an electronic device, characterized in that it includes:

[0026] processor;

[0027] a memory for storing instructions executable by the processor;

[0028] The processor is configured to execute the instructions to implement the method as described in the first aspect or the second aspect.

[0029] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the method described in the first aspect or the second aspect.

[0030] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0031] The PRB normal distribution model parameters of the cell to be evaluated are obtained based on the historical sampling data of the cell to be evaluated, and the capacity of the cell to be evaluated is further evaluated based on the PRB normal distribution model parameters. Not only the number of PRBs of the cell in the Massive MIMO system but also the normal distribution characteristics of the number of PRBs of the cell after adding the MU-MIMO mode are considered, so that the accuracy of the evaluation can be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 A schematic diagram of a flow chart of a cell capacity evaluation method provided by an exemplary embodiment of the present application;

[0034] Figure 2 A schematic diagram of a PRB normal distribution model of a cell to be evaluated provided by an exemplary embodiment of the present application;

[0035] Figure 3 A flow chart of a method for load balancing between cells provided by an exemplary embodiment of the present application;

[0036] Figure 4 A schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present application;

[0037] Figure 5 A schematic diagram of the structure of a cell capacity assessment device provided by an exemplary embodiment of the present application;

[0038] Figure 6 A schematic diagram of the structure of an electronic device provided for another exemplary embodiment of the present application;

[0039] Figure 7 A schematic structural diagram of an inter-cell load balancing device provided as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0041] The inventor of this case found that the number of PRBs in a large number of cells in the existing Massive MIMO system after adding the MU-MIMO mode showed a normal distribution when studying the number of PRBs in the cells in the Massive MIMO system after adding the MU-MIMO mode, and proved this through the χ2 goodness of fit test method. Based on the above research, the inventor of this case found that the normal distribution characteristics of the above number of PRBs can reflect the capacity of the Massive MIMO cell.

[0042] In order to solve the problem of inaccurate evaluation results in the existing cell capacity evaluation methods, one or more embodiments of the present application provide a cell capacity evaluation method, which utilizes the normal distribution characteristics of the number of PRBs in the Massive MIMO cell after adding the MU-MIMO mode to determine the PRB normal distribution model parameters of the Massive MIMO cell, and further evaluates the capacity of the Massive MIMO cell based on the PRB normal distribution model parameters, so as to obtain accurate cell capacity evaluation results, so as to fully support the capacity expansion planning and optimization of the Massive cell. On this basis, one or more embodiments of the present application also provide a method for load balancing between cells, which realizes load balancing between Massive MIMO cells and non-Massive MIMO cells based on the capacity and PRB occupancy information of the Massive MIMO cells and non-Massive MIMO cells in the entire network, thereby improving the accuracy of load balancing optimization.

[0043] It should be understood that the execution entities of the cell capacity assessment method and the inter-cell load balancing method provided in the embodiments of the present application may include but are not limited to servers, computers, etc. that can be configured to execute at least one of the methods provided in the embodiments of the present application.

[0044] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0045] Please refer to Figure 1 , is a flow chart of a cell capacity evaluation method provided by an exemplary embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0046] S102, obtaining historical sampling data of a cell to be evaluated, wherein the cell to be evaluated is a cell in a Massive MIMO system, and the historical sampling data includes sampling point data and the number of PRBs measured at the sampling points after the cell to be evaluated is added with a MU-MIMO mode.

[0047] The historical sampling data of the cell to be evaluated may be the historical sampling data of the cell to be evaluated within a specified historical time period. In practical applications, the specified historical time period may be set according to actual needs, for example, the specified time period may be within a week before the current moment.

[0048] The method for obtaining the above historical sampling data can be various existing methods, which will not be described in detail here.

[0049] S104: Determine the PRB normal distribution model parameters of the cell to be evaluated based on the historical sampling data of the cell to be evaluated.

[0050] The PRB normal distribution model parameters include the expected value of the PRB quantity and the standard deviation of the PRB quantity.

[0051] Specifically, the number of sampling points corresponding to different PRB numbers can be determined based on the number of PRBs measured at each sampling point. Then, with the measured number of PRBs as the horizontal coordinate and the number of sampling points as the vertical coordinate, the PRB normal distribution model of the cell to be evaluated can be obtained by fitting the number of sampling points corresponding to different PRB numbers, wherein the PRB normal distribution model is used to characterize the distribution of sampling points of the cell to be evaluated at different PRB numbers. Finally, based on the PRB normal distribution model of the cell to be evaluated, the overall PRB distribution model parameters of the cell to be evaluated can be determined.

[0052] It should be noted that the implementation process of fitting the number of sampling points corresponding to different PRB numbers and determining the PRB overall distribution model parameters such as the expected value of the PRB number and the standard deviation of the PRB number based on the fitted PRB normal distribution model can be implemented by any method commonly used in the art, which will not be elaborated here.

[0053] For example, Figure 2 An example of a PRB normal distribution model of a cell to be evaluated is shown, wherein the horizontal axis represents the number of PRBs after adding the MU-MIMO mode, and the vertical axis represents the number of sampling points. The PRB normal distribution model reflects the normal distribution characteristic curve (such as the normal distribution curve (such as the normal distribution curve (such as the normal distribution curve (such as the normal distribution curve (such as the normal distribution curve (such as the normal distribution curve ((( Figure 2 ). Further, the parameters of the PRB normal distribution model can be determined, including the expected number MEAN of PRBs and the standard deviation STD of PRBs.

[0054] S106: Evaluate the capacity of the cell to be evaluated based on the PRB normal distribution model parameters.

[0055] Specifically, based on the expected value of the number of PRBs and the standard deviation of the number of PRBs of the cell to be evaluated, the maximum number of PRBs of the cell to be evaluated in the MU-MIMO mode can be determined, and the maximum number of PRBs is used as the capacity of the cell to be evaluated.

[0056] More specifically, based on the expected value of the number of PRBs and the standard deviation of the number of PRBs of the cell to be evaluated, a symmetric interval centered on the expected value of the number of PRBs and satisfying the first preset area condition can be determined, and the upper limit value of the symmetric interval can be determined as the maximum number of PRBs for the cell to be evaluated in MU-MIMO mode, wherein the first preset area condition includes that under the distribution curve indicated by the PRB normal distribution model of the cell to be evaluated, the area corresponding to the above-mentioned symmetric interval accounts for a proportion of the total area under the distribution curve that exceeds a preset area threshold.

[0057] In practical applications, the preset area threshold can be set according to the congestion loss that the network covered by the cell to be evaluated can tolerate. For example, assuming that the congestion loss that the network covered by the cell to be evaluated can tolerate is 1%, the preset area threshold can be determined to be 99%.

[0058] Still Figure 2 Taking the PRB normal distribution model shown in FIG. 1 as an example, by analyzing the PRB normal distribution model, it is possible to determine a symmetrical interval formed by extending 2.58 times the standard deviation STD of the PRB number on both sides with the expected value MEAN of the PRB number as the center, and the corresponding area under the distribution curve indicated by the PRB normal distribution model (i.e. Figure 2 The area of ​​region A in the distribution curve accounts for the total area under the distribution curve (i.e. Figure 2 The ratio of the sum of the areas of the three regions A, B, and C in the symmetric interval to the maximum number of PRBs in the cell to be evaluated in MU-MIMO mode is 99%. Therefore, the upper limit value of the symmetric interval MEAN+2.58×STD can be determined as the maximum number of PRBs in the cell to be evaluated in MU-MIMO mode X max .

[0059] It can be understood that by obtaining the PRB normal distribution model parameters of the cell to be evaluated based on the historical sampling data of the cell to be evaluated, and supplemented by the ratio of the areas corresponding to different PRB number intervals under the distribution curve indicated by the PRB normal distribution model to the total area under the distribution curve, the maximum number of PRBs for the cell to be evaluated in MU-MIMO mode can be accurately and quickly determined, and the maximum number of PRBs can be further used as the capacity of the cell to be evaluated, so that the capacity evaluation result is more accurate and can better meet the actual needs of cell capacity expansion.

[0060] The cell capacity evaluation method provided in the embodiment of the present application is adopted to obtain the PRB normal distribution model parameters of the cell to be evaluated based on the historical sampling data of the cell to be evaluated, and the capacity of the cell to be evaluated is further evaluated based on the PRB normal distribution model parameters. Not only the number of PRBs of the cell in the Massive MIMO system is considered, but also the normal distribution characteristics of the number of PRBs of the cell after adding the MU-MIMO mode are considered, so that the accuracy of the evaluation can be significantly improved.

[0061] Please refer to Figure 3 , is a flow chart of a method for load balancing between cells provided by an exemplary embodiment of the present application. Figure 3 As shown, the method comprises the following steps:

[0062] S302, obtaining the capacity and PRB occupancy information of cells in the entire network.

[0063] The cells in the entire network include cells in a Massive MIMO system and cells in a non-Massive MIMO system.

[0064] It should be noted that the capacity of the cell in the above-mentioned Massive MIMO system can be obtained through the relevant steps in the cell capacity evaluation method provided in one or more embodiments of the present application, which will not be repeated here. The PRB occupancy information of each cell and the capacity of the cell in the non-Massive MIMO system can be obtained by various existing methods.

[0065] S304: Determine a source cell and a target cell based on PRB occupancy information of cells in the entire network.

[0066] The source cell refers to the cell that needs to balance the PRB, and the target cell refers to the cell that needs to receive the PRB. To achieve load balancing between cells in the Massive MIMO system and cells in the non-Massive MIMO system, if the source cell is a cell in the Massive MIMO system, the target cell is a cell in the non-Massive MIMO system, or, if the source cell is a cell in the non-Massive MIMO system, the target cell is a cell in the Massive MIMO system.

[0067] The PRB occupancy information of a cell refers to information used to characterize the PRB occupancy of the cell, which may specifically include but is not limited to PRB utilization, the number of occupied PRBs, etc. In an optional implementation, a cell whose PRB utilization in the Massive MIMO system of the entire network exceeds a first preset utilization may be determined as a source cell, and a cell whose PRB utilization in the non-Massive MIMO system of the entire network is less than a second preset utilization may be determined as a target cell, wherein the first preset utilization is greater than or equal to the second preset utilization; or, a cell whose PRB utilization in the Massive MIMO system of the entire network is less than a third preset utilization may be determined as a target cell, and a cell whose PRB utilization in the non-Massive MIMO system of the entire network is greater than a fourth preset utilization may be determined as a source cell, wherein the third preset utilization is less than or equal to the fourth preset utilization.

[0068] S306: Determine the target number of PRBs to be balanced from the source cell to the target cell based on the PRB occupancy information and capacity of the source cell and the target cell, respectively.

[0069] To prevent the number of PRBs of different types of target cells from exceeding their capacity after equalization, in an optional implementation, the number of target PRBs equalized from the source cell to the target cell may be determined according to the types of the target cell and the source cell.

[0070] Specifically, if the source cell is a cell in a Massive MIMO system and the target cell is a cell in a non-Massive MIMO system, that is, a certain number of PRBs are balanced from the cell in the Massive MIMO system to the cell in the non-Massive MIMO system, then the target PRB utilization balanced from the source cell to the target cell can be determined based on the PRB occupancy information of the source cell and the target cell respectively, and the target PRB utilization, the PRB normal distribution model parameters and capacity of the source cell, the target number of PRBs balanced from the source cell to the target cell can be determined.

[0071] More specifically, the equal PRB utilization of the source cell and the target cell after equalization can be taken as a goal, and the target PRB utilization equalized from the source cell to the target cell can be determined based on the PRB utilization of each of the two before equalization. Since the source cell is a cell in a Massive MIMO system, its PRB number exhibits a normal distribution characteristic, and therefore the minimum PRB number that satisfies the second preset area condition can be determined based on the PRB normal distribution model parameters, and the difference between the capacity of the source cell and the minimum PRB number is determined as the target PRB number equalized from the source cell to the target cell, wherein the second preset area condition includes: the area of ​​the target area exceeds the total area under the distribution curve indicated by the PRB normal distribution model of the source cell by a ratio equal to the target PRB utilization, and the target area is the area where the number of PRBs under the distribution curve exceeds the minimum number of PRBs.

[0072] For example, assuming the target PRB utilization is a%, we still use Figure 2 The PRB normal distribution model shown, the target area is Figure 2 The C region shown in FIG. 1 can be determined based on the PRB normal distribution model parameters so that the area of ​​the C region occupies the total area under the distribution curve indicated by the PRB normal distribution model (i.e. Figure 2 The ratio of the sum of the areas of the three regions A, B and C shown in the figure) is equal to the minimum number of PRBs X for the target PRB utilization rate a%. a , we can further determine the target number of PRBs that the source cell equalizes to the target cell = X max -X a .

[0073] It should be noted that the PRB normal distribution model parameters of the cells in the above-mentioned Massive MIMO system can all be obtained through the relevant steps in the cell capacity evaluation method provided in one or more embodiments of the present application, which will not be repeated here.

[0074] If the source cell is a cell in a non-Massive MIMO system and the target cell is a cell in a Massive MIMO system, that is, a certain number of PRBs are balanced from the cell in the non-Massive MIMO system to the cell in the Massive MIMO system, the maximum number of receivable PRBs of the target cell can be determined based on the capacity and PRB occupancy information of the target cell, and the target number of PRBs balanced from the source cell to the target cell can be determined based on the maximum number of receivable PRBs and the PRB occupancy information of the source cell.

[0075] More specifically, the maximum receivable PRB number of the target cell can be determined as the difference between the capacity of the target cell and the number of currently occupied PRBs, as the maximum receivable PRB number of the target cell, and then, the number of PRBs equalized from the source cell to the target cell does not exceed the maximum receivable PRB number, and the above target PRB number is determined based on the number of PRBs currently occupied by the source cell. As a result, the number of PRBs in the target cell after equalization will not exceed its capacity.

[0076] S308: Perform load balancing on the source cell and the target cell based on the target PRB quantity.

[0077] Specifically, PRBs of the target number of PRBs may be balanced out from the PRBs occupied by the source cell to the target cell, thereby achieving load balancing between the source cell and the target cell.

[0078] By adopting the inter-cell load balancing method provided in the embodiment of the present application, load balancing is achieved between Massive MIMO cells and non-Massive MIMO cells based on the capacity and PRB occupancy information of Massive MIMO cells and non-Massive MIMO cells in the entire network, thereby improving the accuracy of load balancing optimization.

[0079] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0080] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 4 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0081] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0082] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0083] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a cell capacity evaluation device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0084] Acquire historical sampling data of a cell to be evaluated, wherein the cell to be evaluated is a cell in a massive multiple-input multiple-output Massive MIMO system, and the historical sampling data includes sampling point data and the number of physical resource blocks (PRBs) of the cell to be evaluated measured at the sampling point after a multi-user multiple-input multiple-output (MU-MIMO) mode is added;

[0085] Determine, based on the historical sampling data, PRB normal distribution model parameters of the cell to be evaluated, wherein the PRB normal distribution model parameters include an expected value of the number of PRBs and a standard deviation of the number of PRBs;

[0086] Based on the PRB normal distribution model parameters, the capacity of the cell to be evaluated is evaluated.

[0087] The above application Figure 1The method performed by the cell capacity assessment device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in software form. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0088] The electronic device may also perform Figure 1 The method is implemented in a cell capacity evaluation device. Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0089] Of course, in addition to software implementation methods, the electronic device of the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0090] The embodiment of the present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to execute Figure 1 The method of the embodiment shown is specifically used to perform the following operations:

[0091] Acquire historical sampling data of a cell to be evaluated, wherein the cell to be evaluated is a cell in a massive multiple-input multiple-output Massive MIMO system, and the historical sampling data includes sampling point data and the number of physical resource blocks (PRBs) of the cell to be evaluated measured at the sampling point after a multi-user multiple-input multiple-output (MU-MIMO) mode is added;

[0092] Determine, based on the historical sampling data, PRB normal distribution model parameters of the cell to be evaluated, wherein the PRB normal distribution model parameters include an expected value of the number of PRBs and a standard deviation of the number of PRBs;

[0093] Based on the PRB normal distribution model parameters, the capacity of the cell to be evaluated is evaluated.

[0094] Figure 5 This is a schematic diagram of the structure of a cell capacity evaluation device according to an embodiment of the present application. Figure 5 In a software implementation, the cell capacity evaluation 500 device may include:

[0095] A first acquisition unit 510 is configured to acquire historical sampling data of a cell to be evaluated, wherein the cell to be evaluated is a cell in a massive multiple-input multiple-output Massive MIMO system, and the historical sampling data includes sampling point data and the number of physical resource blocks (PRBs) of the cell to be evaluated measured at the sampling point after a multi-user multiple-input multiple-output (MU-MIMO) mode is added;

[0096] A model parameter determination unit 520 is used to determine the PRB normal distribution model parameters of the cell to be evaluated based on the historical sampling data, wherein the PRB normal distribution model parameters include an expected value of the number of PRBs and a standard deviation of the number of PRBs;

[0097] The capacity evaluation unit 530 is configured to evaluate the capacity of the cell to be evaluated based on the PRB normal distribution model parameters.

[0098] In one embodiment, the capacity evaluation unit 530 includes:

[0099] A maximum PRB number determination subunit, configured to determine a maximum PRB number of the cell to be evaluated in MU-MIMO mode based on the expected value of the PRB number and the standard deviation of the PRB number;

[0100] The capacity evaluation subunit is used to use the maximum number of PRBs as the capacity of the cell to be evaluated.

[0101] In one embodiment, the maximum PRB number determination subunit is specifically used for:

[0102] Based on the expected value of the number of PRBs and the standard deviation of the number of PRBs, determine a symmetric interval centered on the expected value of the number of PRBs and satisfying a first preset area condition, wherein the first preset area condition is that a proportion of an area corresponding to a distribution curve indicated by a PRB normal distribution model of the cell to be evaluated to a total area under the distribution curve exceeds a preset area threshold;

[0103] The upper limit value of the symmetric interval is determined as the maximum number of PRBs of the cell to be evaluated in the MU-MIMO mode.

[0104] In one embodiment, the model parameter determination unit 520 includes:

[0105] A sampling point number determination subunit, used to determine the number of sampling points corresponding to different PRB numbers based on the number of PRBs measured at each sampling point;

[0106] A distribution curve determination subunit, configured to perform fitting processing based on the number of sampling points corresponding to different PRB numbers to determine a PRB normal distribution model of the cell to be evaluated;

[0107] The model parameter determination subunit is used to determine the PRB normal distribution model parameters of the cell to be evaluated based on the PRB normal distribution model.

[0108] The inter-cell load balancing device 500 can achieve Figure 1 The method of the method embodiment shown in the figure can be specifically referred to Figure 1 The cell capacity evaluation method of the illustrated embodiment will not be described in detail here.

[0109] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 6 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0110] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0111] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0112] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an inter-cell load balancing device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0113] Obtain the capacity and PRB occupancy information of cells in the entire network;

[0114] Determine a source cell and a target cell based on the PRB occupancy information of the cells in the entire network, wherein the source cell is a cell in a Massive MIMO system, and the target cell is a cell in a non-Massive MIMO system, or the source cell is a cell in a non-Massive MIMO system, and the target cell is a cell in a Massive MIMO system;

[0115] Determine, based on the PRB occupancy information and capacity of the source cell and the target cell, respectively, a target number of PRBs balanced from the source cell to the target cell;

[0116] Based on the target PRB quantity, load balancing processing is performed on the source cell and the target cell.

[0117] The above application Figure 3The method performed by the inter-cell load balancing device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in software form. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0118] The electronic device may also perform Figure 3 The method is used to realize the load balancing device between cells. Figure 3 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0119] Of course, in addition to software implementation, the electronic device of the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0120] The embodiment of the present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to execute Figure 1 The method of the embodiment shown is specifically used to perform the following operations:

[0121] Obtain the capacity and PRB occupancy information of cells in the entire network;

[0122] Determine a source cell and a target cell based on the PRB occupancy information of the cells in the entire network, wherein the source cell is a cell in a Massive MIMO system, and the target cell is a cell in a non-Massive MIMO system, or the source cell is a cell in a non-Massive MIMO system, and the target cell is a cell in a Massive MIMO system;

[0123] Determine, based on the PRB occupancy information and capacity of the source cell and the target cell, respectively, a target number of PRBs balanced from the source cell to the target cell;

[0124] Based on the target PRB quantity, load balancing processing is performed on the source cell and the target cell.

[0125] Figure 7 This is a schematic diagram of the structure of a load balancing device between cells in an embodiment of the present application. Figure 7 In a software implementation, the inter-cell load balancing 700 device may include:

[0126] The second acquisition unit 710 is used to acquire the capacity and PRB occupancy information of cells in the entire network;

[0127] A cell determination unit 720 is configured to determine a source cell and a target cell based on the PRB occupancy information of the cells in the entire network, wherein the source cell is a cell in a Massive MIMO system, and the target cell is a cell in a non-Massive MIMO system, or the source cell is a cell in a non-Massive MIMO system, and the target cell is a cell in a Massive MIMO system;

[0128] A PRB quantity determination unit 730 is configured to determine a target PRB quantity balanced from the source cell to the target cell based on the PRB occupancy information and capacity of the source cell and the target cell respectively;

[0129] The balancing processing unit 740 is configured to perform load balancing processing on the source cell and the target cell based on the target PRB number.

[0130] In one embodiment, the PRB quantity determining unit 730 includes:

[0131] A first PRB quantity determination subunit, configured to, if the source cell is a cell in a Massive MIMO system and the target cell is a cell in a non-Massive MIMO system, determine a target PRB utilization rate balanced from the source cell to the target cell based on the PRB occupancy information of the source cell and the target cell respectively, and determine a target PRB quantity balanced from the source cell to the target cell based on the target PRB utilization rate, a PRB normal distribution model parameter and a capacity of the source cell;

[0132] The second PRB number determination subunit is used to determine the maximum number of receivable PRBs of the target cell based on the capacity and PRB occupancy information of the target cell if the source cell is a cell in a non-Massive MIMO system and the target cell is a cell in a Massive MIMO system, and determine the target number of PRBs balanced from the source cell to the target cell based on the maximum number of receivable PRBs and the PRB occupancy information of the source cell.

[0133] In one embodiment, the first PRB quantity determination subunit is specifically used to:

[0134] Determine, based on the PRB normal distribution model parameters of the source cell, the minimum number of PRBs that meets the second preset area condition, wherein the second preset area condition includes: the ratio of the area of ​​the target area exceeding the total area under the distribution curve indicated by the PRB normal distribution model of the source cell is equal to the target PRB utilization rate, and the target area is the area where the number of PRBs under the distribution curve exceeds the minimum number of PRBs;

[0135] The difference between the capacity of the source cell and the minimum number of PRBs is determined as the target number of PRBs that the source cell balances to the target cell.

[0136] The inter-cell load balancing device 700 can achieve Figure 3 The method of the method embodiment shown in the figure can be specifically referred to Figure 3 The inter-cell load balancing method of the illustrated embodiment will not be described in detail here.

[0137] In short, the above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0138] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0139] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0140] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0141] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A cell capacity assessment method, characterized in that: include: Acquire historical sampling data of a cell to be evaluated, wherein the cell to be evaluated is a cell in a massive multiple-input multiple-output Massive MIMO system, and the historical sampling data includes sampling point data and the number of physical resource blocks (PRBs) of the cell to be evaluated measured at the sampling point after a multi-user multiple-input multiple-output (MU-MIMO) mode is added; Determine, based on the historical sampling data, PRB normal distribution model parameters of the cell to be evaluated, wherein the PRB normal distribution model parameters include an expected value of the number of PRBs and a standard deviation of the number of PRBs; Based on the expected value of the number of PRBs and the standard deviation of the number of PRBs, determine a symmetric interval centered on the expected value of the number of PRBs and satisfying a first preset area condition, wherein the first preset area condition includes that a proportion of an area corresponding to a distribution curve indicated by a normal distribution model of the PRBs of the cell to be evaluated to a total area under the distribution curve exceeds a preset area threshold; The upper limit value of the symmetric interval is determined as the capacity of the cell to be evaluated.

2. The method according to claim 1, characterized in that The determining, based on the historical sampling data, the PRB normal distribution model parameters of the cell to be evaluated includes: Determine the number of sampling points corresponding to different numbers of PRBs based on the number of PRBs measured at each sampling point; Perform fitting processing based on the number of sampling points corresponding to different PRB numbers to determine the PRB normal distribution model of the cell to be evaluated; Based on the PRB normal distribution model, a PRB normal distribution model parameter of the cell to be evaluated is determined.

3. A method for load balancing between cells, characterized in that: include: Obtain the capacity and PRB occupancy information of cells in the entire network; Determine a source cell and a target cell based on the PRB occupancy information of the cells in the entire network, wherein the source cell is a cell in a Massive MIMO system, and the target cell is a cell in a non-Massive MIMO system, or the source cell is a cell in a non-Massive MIMO system, and the target cell is a cell in a Massive MIMO system; Determine, based on the PRB occupancy information and capacity of the source cell and the target cell, respectively, a target number of PRBs balanced from the source cell to the target cell; Based on the target PRB number, load balancing is performed on the source cell and the target cell; The determining, based on the PRB occupancy information and capacity of the source cell and the target cell, respectively, of the target PRB number balanced from the source cell to the target cell includes: If the source cell is a cell in a Massive MIMO system and the target cell is a cell in a non-Massive MIMO system, determining a target PRB utilization rate balanced from the source cell to the target cell based on the PRB occupancy information of the source cell and the target cell, and determining a target number of PRBs balanced from the source cell to the target cell based on the target PRB utilization rate, a PRB normal distribution model parameter and a capacity of the source cell; If the source cell is a cell in a non-Massive MIMO system and the target cell is a cell in a Massive MIMO system, determining a maximum number of receivable PRBs of the target cell based on the capacity and PRB occupancy information of the target cell, and determining a target number of PRBs balanced from the source cell to the target cell based on the maximum number of receivable PRBs and the PRB occupancy information of the source cell; The determining, based on the target PRB utilization rate, the PRB normal distribution model parameters and the capacity of the source cell, the target PRB quantity balanced from the source cell to the target cell comprises: Determine, based on the PRB normal distribution model parameters of the source cell, the minimum number of PRBs that meets the second preset area condition, wherein the second preset area condition includes: the ratio of the area of ​​the target area exceeding the total area under the distribution curve indicated by the PRB normal distribution model of the source cell is equal to the target PRB utilization rate, and the target area is the area where the number of PRBs under the distribution curve exceeds the minimum number of PRBs; The difference between the capacity of the source cell and the minimum number of PRBs is determined as the target number of PRBs that the source cell balances to the target cell.

4. A cell capacity assessment device, characterized in that: include: A first acquisition unit is used to acquire historical sampling data of a cell to be evaluated, wherein the cell to be evaluated is a cell in a massive multiple-input multiple-output Massive MIMO system, and the historical sampling data includes sampling point data and the number of physical resource blocks (PRBs) of the cell to be evaluated measured at the sampling point after a multi-user multiple-input multiple-output (MU-MIMO) mode is added; A model parameter determination unit, configured to determine the PRB normal distribution model parameters of the cell to be evaluated based on the historical sampling data, wherein the PRB normal distribution model parameters include an expected value of the number of PRBs and a standard deviation of the number of PRBs; A capacity evaluation unit is used to determine, based on the expected value of the number of PRBs and the standard deviation of the number of PRBs, a symmetric interval centered on the expected value of the number of PRBs and satisfying a first preset area condition, wherein the first preset area condition includes that the proportion of the area corresponding to the distribution curve indicated by the PRB normal distribution model of the cell to be evaluated to the total area under the distribution curve exceeds a preset area threshold; and the upper limit value of the symmetric interval is determined as the capacity of the cell to be evaluated.

5. A load balancing device between cells, characterized in that: include: A second acquisition unit is used to acquire the capacity and PRB occupancy information of cells in the entire network; a cell determination unit, configured to determine a source cell and a target cell based on PRB occupancy information of cells in the entire network, wherein the source cell is a cell in a Massive MIMO system, and the target cell is a cell in a non-Massive MIMO system, or the source cell is a cell in a non-Massive MIMO system, and the target cell is a cell in a Massive MIMO system; A PRB quantity determination unit, configured to determine a target PRB quantity balanced from the source cell to the target cell based on the PRB occupancy information and capacity of the source cell and the target cell respectively; A balancing processing unit, configured to perform load balancing processing on the source cell and the target cell based on the target PRB number; The PRB quantity determination unit is specifically used for: If the source cell is a cell in a Massive MIMO system and the target cell is a cell in a non-Massive MIMO system, a target PRB utilization rate balanced from the source cell to the target cell is determined based on the PRB occupancy information of the source cell and the target cell respectively, and a target PRB number balanced from the source cell to the target cell is determined based on the target PRB utilization rate, the PRB normal distribution model parameters and the capacity of the source cell; the target PRB number is determined in the following manner: based on the PRB normal distribution model parameters of the source cell, a minimum PRB number that satisfies a second preset area condition is determined, wherein the second preset area condition includes: a ratio of an area of ​​a target area exceeding a total area under a distribution curve indicated by a PRB normal distribution model of the source cell is equal to the target PRB utilization rate, and the target area is an area where the number of PRBs under the distribution curve exceeds the minimum number of PRBs; a difference between the capacity of the source cell and the minimum number of PRBs is determined as the target PRB number balanced from the source cell to the target cell; If the source cell is a cell in a non-Massive MIMO system and the target cell is a cell in a Massive MIMO system, the maximum number of receivable PRBs of the target cell is determined based on the capacity and PRB occupancy information of the target cell, and the target number of PRBs balanced from the source cell to the target cell is determined based on the maximum number of receivable PRBs and the PRB occupancy information of the source cell.

6. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 3.

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