Load adjustment method, server and storage medium

Abstract

The present invention discloses a load adjustment method, a server, and a storage medium, which belong to the field of communications. The method includes: obtaining an index value of a first performance index of a Massive MIMO cell of a large-scale antenna array; determining the load state of the Massive MIMO cell according to the index value of the first performance index; determining and executing a load adjustment strategy that matches the load state; wherein the load adjustment strategy includes one of the following: migrating the first user of a neighboring cell of the Massive MIMO cell into the Massive MIMO cell, migrating the second user in the Massive MIMO cell out to the neighboring cell, migrating the first user of the neighboring cell into the Massive MIMO cell and migrating the second user in the Massive MIMO cell out to the neighboring cell, and maintaining the status quo. The technical solution of the present invention provides a possibility for improving the traffic of Massive MIMO cells.

Description

Technical Field

[0001] The present invention relates to the field of communications, and in particular to a load adjustment method, a server and a storage medium. Background Art

[0002] Massive MIMO (Massive Multiple-Input-Multiple-Output, also known as Large Scale MIMO) is a key technology for improving system capacity and spectrum utilization in the fifth generation of mobile communications (5G) by configuring hundreds or thousands of antenna arrays at the base station end to communicate with multiple users simultaneously on the same time-frequency resources, thereby greatly improving spectrum efficiency. Massive MIMO cells do not need to increase system bandwidth, base stations or cells. With the help of Massive MIMO's beamforming and multi-user space allocation capabilities, they can show several times the traffic gain compared to ordinary macro stations.

[0003] However, some Massive MIMO cells cannot fully utilize the beamforming and multi-user space allocation capabilities of Massive MIMO, resulting in limited improvement in the traffic of Massive MIMO cells. Summary of the invention

[0004] The main purpose of the embodiments of the present invention is to propose a load adjustment method, a server and a storage medium, aiming to provide a possibility for improving the flow of Massive MIMO cells.

[0005] To achieve the above-mentioned purpose, an embodiment of the present invention provides a load adjustment method, including: obtaining an index value of a first performance indicator of a Massive MIMO cell of a massive antenna array; determining the load state of the Massive MIMO cell according to the index value of the first performance indicator; determining and executing a load adjustment strategy that matches the load state; wherein the load adjustment strategy includes one of the following: migrating a first user of a neighboring cell of the Massive MIMO cell into the Massive MIMO cell, migrating a second user in the Massive MIMO cell out to the neighboring cell, migrating the first user of the neighboring cell into the Massive MIMO cell and migrating the second user in the Massive MIMO cell out to the neighboring cell, and maintaining the status quo.

[0006] To achieve the above-mentioned purpose, an embodiment of the present invention also proposes a server, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the load adjustment method described above.

[0007] To achieve the above objective, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above load adjustment method when executed by a processor.

[0008] The load adjustment method, server and storage medium proposed in the present invention first obtain the index value of the first performance index of the Massive MIMO cell of the massive antenna array, and then accurately determine the load state of the Massive MIMO cell according to the index value of the first performance index. Since the load state can reflect the busyness of the Massive MIMO cell, and the load adjustment strategy includes one of the following: migrating the first user of the neighboring cell of the Massive MIMO cell into the Massive MIMO cell, migrating the second user in the Massive MIMO cell to the neighboring cell, migrating the first user of the neighboring cell into the Massive MIMO cell and migrating the second user in the Massive MIMO cell to the neighboring cell, maintaining the status quo, that is, the busyness of the Massive MIMO cell can be adjusted by the load adjustment strategy, and the load adjustment strategy determined to match the load state is matched with the current busyness, so executing the load adjustment strategy matching the load state is conducive to adjusting the busyness of the Massive MIMO cell to a better state, thereby facilitating the use of the advantages of the Massive MIMO cell and providing services for the Massive MIMO cell. This provides the possibility of improving the MIMO cell traffic, and further provides the possibility of improving the total traffic of all cells in the area where the Massive MIMO cell is located. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a flow chart of a method for load adjustment according to a first embodiment of the present invention;

[0010] Figure 2 is a flowchart of determining a first user or a second user according to a first embodiment of the present invention;

[0011] Figure 3 is a flow chart of a method for load adjustment according to a second embodiment of the present invention;

[0012] Figure 4is a flowchart of a specific implementation method of step 204 in the second embodiment of the present invention;

[0013] Figure 5 is a flow chart of a method for load adjustment according to a third embodiment of the present invention;

[0014] Figure 6 is a flowchart of a specific implementation method of step 305 in the third embodiment of the present invention;

[0015] Figure 7 4 is a schematic diagram of the structure of a server according to the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0016] To make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present invention, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can be implemented. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined and referenced with each other without contradiction.

[0017] The first embodiment of the present invention relates to a load adjustment method, which is applied to a device, such as a base station. The specific process is as follows: Figure 1 As shown, including:

[0018] Step 101: Obtain an indicator value of a first performance indicator of a massive MIMO cell.

[0019] Specifically, the device can automatically obtain the index value of the first performance indicator of the Massive MIMO cell within the most recent preset time, and the preset time can be set according to actual needs, for example, the preset time is 15 minutes. The device can also pre-set a trigger event, and when the trigger event occurs, the index value of the first performance indicator of the Massive MIMO cell is obtained. The first performance indicator can be a type of performance indicator, for example, the first performance indicator is the resource block PRB utilization; the first performance indicator can also be multiple types of performance indicators, then the index values ​​of multiple types of performance indicators are obtained respectively, for example, the first performance indicator is the PRB utilization, the control channel unit CCE power and the CCE position utilization, and the index value of the PRB utilization, the index value of the CCE power and the index value of the CCE position utilization are obtained respectively.

[0020] Step 102: Determine the load status of the Massive MIMO cell according to the metric value of the first performance metric.

[0021] In one example, different load statuses of the Massive MIMO cell can be represented by relatively idle state, relatively busy state, and congested state. Both the relatively idle state and the relatively busy state do not have a congestion situation, while the congested state means there is a congestion situation. In one example, different load statuses of the Massive MIMO cell can also be represented by numerical values. Different numerical values represent different load statuses. It can be predefined that the higher the numerical value, the busier the Massive MIMO cell is. The largest numerical value represents the occurrence of a congestion situation, the smallest numerical value represents no congestion situation and relatively idle, and other numerical values represent no congestion situation and relatively busy. For example, S = 0, 1, 2, 3 respectively refer to different load statuses. When S = 0, there is no congestion situation and it is relatively idle. When S = 1, 2, there is no congestion situation and it is relatively busy. When S = 3, there is a congestion situation.

[0022] The device can preset the rule for determining the load status of the Massive MIMO cell according to the metric value of the first performance metric. In one example, the correspondence between the interval where the metric value of the first metric is located and the load status of the Massive MIMO cell can be set. For example: The load status is represented by relatively idle state, relatively busy state, and congested state. When 0% <= PRB utilization <= 60%, it is in the relatively idle state. When 60% < PRB utilization <= 80%, it is in the relatively busy state. When 80% < PRB utilization <= 100%, it is in the congested state. For example: The load status is represented by numerical values. When 0% <= PRB utilization <= 80% and 0% <= CCE power and CCE location utilization <= 60%, it is S = 0. When 80% < PRB utilization <= 100% and 0% <= CCE power and CCE location utilization <= 60%, it is S = 1. When 0% <= PRB utilization <= 80% and 60% < PRB utilization <= 100%, it is S = 2. When 80% < PRB utilization <= 100% and 60% < PRB utilization <= 100%, it is S = 3.

[0023] In an example, the load state of the Massive MIMO cell can be determined based on the load state within the last preset time and the load state conversion rule. For example, the load state is represented by a relatively idle state, a relatively busy state, and a congested state. The load conversion rule is: when the load state within the last preset time is a relatively idle state and the PRB utilization rate is greater than 80%, the load state is converted to a relatively busy state; when the load state within the last preset time is a relatively busy state, the PRB utilization rate is greater than 95%, and the CCE power or CCE position utilization rate is greater than 70%, the load state is converted to a congested state, and when the PRB utilization rate is less than 70%, the load state is converted to a relatively idle state; when the load state within the last preset time is a congested state, the PRB utilization rate is less than 90%, or the CCE power and CCE position utilization rate are less than 60%, the load state is converted to a relatively busy state.

[0024] Step 103, determine and execute a load adjustment strategy that matches the load status; wherein the load adjustment strategy includes one of the following: migrating a first user of a neighboring cell of the Massive MIMO cell into the Massive MIMO cell, migrating a second user in the Massive MIMO cell to a neighboring cell, migrating a first user in a neighboring cell into the Massive MIMO cell and migrating a second user in the Massive MIMO cell to a neighboring cell, or maintaining the status quo.

[0025] Specifically, the first user and the second user are different users. When the load adjustment strategy is to migrate the first user of the neighboring cell into the Massive MIMO cell and migrate the second user in the Massive MIMO cell out to the neighboring cell, the first user of the neighboring cell can be migrated into the Massive MIMO cell first, and then the second user in the Massive MIMO cell can be migrated out to the neighboring cell; the second user in the Massive MIMO cell can be migrated out to the neighboring cell first, and then the first user of the neighboring cell can be migrated into the Massive MIMO cell; the first user of the neighboring cell can also be migrated into the Massive MIMO cell and the second user in the Massive MIMO cell can be migrated out to the neighboring cell at the same time. This embodiment is not specifically limited.

[0026] In one example, the first user or the second user refers to any user. In one example, a first user in a neighboring cell is moved into a Massive MIMO cell, the neighboring cell is recorded as the current cell, and the Massive MIMO cell is recorded as the target cell; a second user in the Massive MIMO cell is moved out to a neighboring cell, the Massive MIMO cell is recorded as the current cell, and the neighboring cell is recorded as the target cell. The first user or the second user is determined in the following manner, and the flowchart for determining the first user or the second user is as shown in FIG. Figure 2 As shown:

[0027] Step 1001, select N users from the current cell; wherein N≥1 and N is preset.

[0028] Specifically, the specific value of N can be preset according to actual conditions, and this embodiment does not make a specific limitation. If the number of users in the cell is less than N, all users are selected. For example, if N is preset to 5, if there are 10 users in the cell, any 5 users are selected; if there are 4 users in the cell, 4 users are selected.

[0029] In one example, a first user in a neighboring cell is moved into a Massive MIMO cell, and N users are selected from the current cell, including: selecting N users whose downlink data volume is greater than a preset data volume from the current cell; a second user in the Massive MIMO cell is moved out to a neighboring cell, and N users are selected from the current cell, including: selecting N users with the smallest spectrum efficiency SE values ​​from users in the current cell whose number of spatial division scheduling is greater than a preset number and whose spatial division scheduling ratio is greater than a preset threshold.

[0030] Specifically, users whose downlink data volume is greater than the preset data volume are called large BSR users. If the number of large BSR users is less than N, all large BSR users are selected. For example: within 1 second, users whose downlink data volume is greater than 1kbits are large BSR users. If N is preset to 5, if there are 10 large BSR users in this cell, then any 5 large BSR users are selected. If there are 4 large BSR users in this cell, then 4 large BSR users are selected. Select N users with the smallest spectrum efficiency SE value from users whose number of spatial division scheduling in this cell is greater than the preset number and whose spatial division scheduling ratio is greater than the preset threshold. If the number of users who meet the conditions is less than N, then all users are selected. , the preset time and the preset threshold can be set according to actual needs, and this embodiment does not make a specific limitation. The space division scheduling ratio is equal to the number of space division scheduling times of the user divided by the total number of scheduling times of the user. For example: N is preset to 5, the preset number of times is 5 times, and the preset threshold is 20%. If there are 10 users in this cell, first select users whose space division scheduling times are greater than 5 times and whose space division scheduling ratio is greater than 20%, and then select 5 users with the smallest SE value from the users whose space division scheduling times are greater than 5 times and whose space division scheduling ratio is greater than 20%. If there are 4 users who meet the conditions, select 4 users. By such a method, for migrating the first user of the neighboring cell to the Massive MIMO cell, a condition is set in the selection process, that is, the N users selected are users whose downlink data volume is greater than the preset data volume, which further improves the user quality of the first user migrating to the Massive MIMO cell, thereby further improving the possibility of improving the traffic of the neighboring cell. For migrating the second user of the Massive MIMO cell out to the neighboring cell, a condition is set in the selection process, that is, the N users selected are N users with the smallest SE value selected from users whose number of spatial division scheduling is greater than the preset number and whose spatial division scheduling ratio is greater than the preset threshold, thereby further improving the user quality of the second user migrating out to the neighboring cell, thereby further improving the possibility of improving the traffic of the neighboring cell, and further improving the possibility of improving the total traffic of all cells in the area where the Massive MIMO cell is located.

[0031] Step 1002: measure a second performance indicator of the selected user in the target cell to obtain an indicator value of the second performance indicator.

[0032] Specifically, the device measures the second performance indicator of the selected user in the target cell to obtain the indicator value of the second performance indicator of the user in the target cell. The second performance indicator is set in advance, and can be a performance indicator of one type or multiple types of performance indicators. In an example, the second performance indicator is a reference signal received power RSRP. In this embodiment and the following embodiments, the second performance indicator is described as RSRP, but it is not limited to this.

[0033] Step 1003: If the index value of the second performance index is greater than the preset index value of the target cell, the selected user is used as the first user or the second user.

[0034] Specifically, the preset index value of the target cell can be set according to the actual situation, and this embodiment does not make specific restrictions. For example, the preset index value RSRP of the target cell is -90dBm, and the RSRP values ​​of the selected users a, b, c, d, and e in the target cell are measured, and the RSRP values ​​are -88dBm, -85dBm, -95dBm, -100dBm, and -80dBm, respectively. Then a, b, and e are the first user or the second user, and a, b, and e of this cell are moved to the target cell.

[0035] In one example, the second performance indicator of the selected user in the target cell and the current cell is measured respectively to obtain the indicator value of the second performance indicator of the user in the target cell and the indicator value of the second performance indicator of the user in the current cell. If the indicator value of the second performance indicator of the user in the target cell is greater than the preset indicator value of the target cell, and the difference between the indicator value of the second performance indicator of the user in the target cell and the indicator value of the second performance indicator of the user in the current cell is not less than the preset threshold, the selected user is taken as the first user or the second user. For example: the preset index value RSRP of the target cell is -90dBm, the preset threshold is -5dBm, and the RSRP of the selected users a, b, c, d, and e in the target cell and in the current cell are measured respectively, and the RSRP of the users in the target cell and the RSRP of the users in the current cell are obtained to be -88dBm and -85dBm, -85dBm and -100dBm, -95dBm and -88dBm, -100dBm and -90dBm, -80dBm and -83dBm respectively. Then a and e are the first users or the second users, and a and e of the current cell are moved to the target cell.

[0036] Through such a method, the user quality of the first user who migrates to the Massive MIMO cell is improved, thereby increasing the possibility of improving the traffic of the Massive MIMO cell, or the user quality of the second user who migrates to the neighboring cell is improved, thereby increasing the possibility of improving the traffic of the neighboring cell, thereby increasing the possibility of improving the total traffic of all cells in the area where the Massive MIMO cell is located.

[0037] In one example, after determining and executing a load adjustment strategy that matches the load state, the method further includes: obtaining an indicator value of a third performance indicator; and updating a preset indicator value of the target cell according to the load state and the indicator value of the third performance indicator.

[0038] Specifically, the device obtains the index value of the third performance indicator within a preset time before executing the load adjustment strategy and within a preset time after executing the load adjustment strategy. The third performance indicator can be a performance indicator of one type or a performance indicator of multiple types. Updating the preset index value of the target cell means that for any load adjustment strategy, after executing the load adjustment strategy, according to the load state and the index value of the third performance indicator, the adjustment range is obtained according to the preset rules, and the preset index value of the target cell is kept unchanged, or the preset index value is increased, or the preset index value is decreased. For example: the load status before and after the execution of the load adjustment strategy is the status before adjustment and the status after adjustment, and the third performance indicator includes: the traffic of the Massive MIMO cell, the traffic of the Massive MIMO cell within a preset time before the execution of the load adjustment strategy and within a preset time after the execution of the load adjustment strategy are respectively recorded as the MM traffic before adjustment and the MM traffic after adjustment; the total traffic of the area where the Massive MIMO cell is located, the total traffic of the area within a preset time before the execution of the load adjustment strategy and within a preset time after the execution of the load adjustment strategy are respectively recorded as the domain traffic before adjustment and the regional traffic after adjustment; the number of wireless resource control RRC connected users, the number of RRC connected users within a preset time before the execution of the load adjustment strategy and within a preset time after the execution of the load adjustment strategy are respectively recorded as the RRC before adjustment and the RRC after adjustment. If the load adjustment strategy is to migrate the first user of the neighboring cell to the Massive MIMO cell, if the state before adjustment is equal to the state after adjustment, and the adjusted RRC-RRC before adjustment<=5, and the MM traffic before adjustment<=MM traffic after adjustment, and the regional traffic before adjustment<=regional traffic after adjustment, and the preset index value RSRP of the Massive MIMO cell is greater than -105dBm, then the preset index value RSRP of the Massive MIMO cell is reduced by 5dB; if the state before adjustment is equal to the state after adjustment, and the adjusted RRC-RRC before adjustment>=20, and the preset index value RSRP of the Massive MIMO cell is less than -90dBm, then the preset index value RSRP of the Massive MIMO cell is increased by 5dB; if the state before adjustment is equal to the state after adjustment, and the adjusted RRC-RRC before adjustment>5, and the MM traffic before adjustment>MM traffic after adjustment or the regional traffic before adjustment>regional traffic after adjustment, and the preset index value RSRP of the Massive MIMO cell is less than -90dBm, then the preset index value RSRP of the Massive MIMO cell is increased by 5dB. The preset index value RSRP of the MIMO cell increases by 5dB; if the state before adjustment is not equal to the state after adjustment, and the preset index value RSRP of the neighboring cell is not equal to -90dBm, the preset index value RSRP of the neighboring cell is adjusted to -90dBm.By updating the preset index value of the target cell, the preset index value of the target cell is made more reasonable, and subsequently the selected user is determined as the first user or the second user according to the updated preset index value of the target cell, so that the determined first user or second user is more reasonable.

[0039] In this embodiment, determining and executing a load adjustment strategy that matches the load state includes: according to a preset correspondence relationship between the load state and the load adjustment strategy, taking the load adjustment strategy corresponding to the load state as the load adjustment strategy that matches the load state and executing it. Specifically, the correspondence relationship between the load state and the load adjustment strategy is preset in the device, and the number of types of load states is the same as the number of load adjustment strategies. When the load state of the Massive MIMO cell is determined, the correspondence relationship is searched, the load adjustment strategy corresponding to the load state is taken as the load adjustment strategy that matches the load state, and the load adjustment strategy is executed. By presetting the correspondence relationship between the load state and the load adjustment strategy, after the load state is determined, the load adjustment strategy that matches the load state can be determined and executed relatively quickly, which is simple and easy.

[0040] In one example, if the load state is that the Massive MIMO cell is not congested, the corresponding load adjustment strategy is to migrate the first user of the neighboring cell into the Massive MIMO cell; if the load state is that the Massive MIMO cell is congested, the corresponding load adjustment strategy is to migrate the second user in the Massive MIMO cell to the neighboring cell. For example: the load states are preset as no congestion and relatively idle, no congestion and relatively busy, and congestion. When no congestion and relatively idle, no congestion and relatively busy, the corresponding load adjustment strategy is to migrate the first user of the neighboring cell into the Massive MIMO cell. When congestion occurs, the corresponding load adjustment strategy is to migrate the second user in the Massive MIMO cell to the neighboring cell. The corresponding relationship between the load state and the load adjustment strategy is shown in the following table:

[0041] Load status Load adjustment strategy No congestion and relatively idle Migrate the first user in the neighboring cell to the Massive MIMO cell No congestion and busy Migrate the first user in the neighboring cell to the Massive MIMO cell Congestion occurs Migrate the second user in the Massive MIMO cell to a neighboring cell

[0042] When the Massive MIMO cell is not congested, it means that the Massive MIMO cell can still access more users. In this case, users of neighboring cells are migrated into the Massive MIMO cell, which provides a possibility for improving the traffic of the Massive MIMO cell. When the Massive MIMO cell is congested, it means that the Massive MIMO cell cannot meet the needs of users. The second user in the Massive MIMO cell is migrated to the neighboring cell, which provides a possibility for improving the traffic of the neighboring cell, thereby providing a possibility for improving the total traffic of all cells in the area where the Massive MIMO cell is located.

[0043] In one example, selecting N users whose downlink data volume is greater than a preset data volume from the current cell includes: if the load state is that the Massive MIMO cell is not congested and is busy, selecting N users whose downlink data volume is greater than a preset data volume from the current cell.

[0044] Specifically, if the load state is that the Massive MIMO cell is not congested and is relatively idle, the N users selected from the cell are not restricted and can be any N users, or N users whose downlink data volume is greater than the preset data volume. If the load state is that the Massive MIMO cell is not congested and is relatively busy, N users whose downlink data volume is greater than the preset data volume are selected from the cell. If the load state is that the Massive MIMO cell is congested, N users with the smallest spectrum efficiency SE value are selected from the users whose number of spatial division scheduling in the cell is greater than the preset number and whose spatial division scheduling ratio is greater than the preset threshold. By using this method, when the Massive MIMO cell is not congested and is relatively busy, the selected users are N users whose downlink data volume is greater than the preset data volume, thereby improving the user quality of the first user who migrates to the Massive MIMO cell, thereby improving the possibility of improving the traffic of the Massive MIMO cell.

[0045] In this embodiment, the index value of the first performance index of the Massive MIMO cell of the massive antenna array is first obtained, and then the load state of the Massive MIMO cell can be accurately determined according to the index value of the first performance index. Since the load state can reflect the busyness of the Massive MIMO cell, and the load adjustment strategy includes one of the following: migrating the first user of the neighboring cell of the Massive MIMO cell into the Massive MIMO cell, migrating the second user in the Massive MIMO cell to the neighboring cell, migrating the first user of the neighboring cell into the Massive MIMO cell and migrating the second user in the Massive MIMO cell to the neighboring cell, maintaining the status quo, that is, the busyness of the Massive MIMO cell can be adjusted by the load adjustment strategy, and the load adjustment strategy determined to match the load state is matched with the current busyness, so executing the load adjustment strategy matching the load state is conducive to adjusting the busyness of the Massive MIMO cell to a better state, thereby facilitating the advantages of the Massive MIMO cell, providing the possibility for improving the traffic of the Massive MIMO cell, and further improving the performance of the Massive MIMO cell. It provides the possibility of improving the total flow of all cells in the area where the MIMO cell is located; moreover, by pre-setting the corresponding relationship between the load state and the load adjustment strategy, after determining the load state, the load adjustment strategy matching the load state can be quickly determined and executed, which is simple and easy.

[0046] The second embodiment of the present invention relates to a load adjustment method. The second embodiment is substantially the same as the first embodiment, with the main difference being that the pre-set value is the merit of each candidate load adjustment strategy under the load state, and the load adjustment strategy matching the load state is determined and executed based on the merit. The specific flow chart is as follows: Figure 3 As shown, including:

[0047] Step 201: Obtain an indicator value of a first performance indicator of a massive MIMO cell.

[0048] Step 202: Determine the load state of the Massive MIMO cell according to the indicator value of the first performance indicator.

[0049] Steps 201-202 are similar to steps 101-102 and will not be described again here.

[0050] Step 203, searching for the superiority of each candidate load adjustment strategy under the load state; wherein the superiority refers to the degree of superiority of executing the load adjustment strategy under the load state.

[0051] Specifically, the load adjustment strategy is similar to that in the first embodiment, and will not be described in detail here. The quality can be expressed by a numerical value, or by various levels such as good or bad. In the following embodiments of this embodiment, the quality is expressed by a numerical value, and the larger the numerical value, the better, but it is not limited to this. When the device is initially running, under different load conditions, the initial value of the quality of each candidate load adjustment strategy can be 0, or different numerical values ​​can be assigned according to actual experience; therefore, the quality of each candidate load adjustment strategy corresponding to different load conditions may also be different, and it is necessary to find the quality of each candidate load adjustment strategy under the load condition. For example: S = 0, 1, 2, 3 refer to different load conditions, respectively, and a = 0, 1, 2, 3 refer to different load adjustment strategies, namely, respectively referring to moving the first user of the neighboring cell of the Massive MIMO cell into the Massive MIMO cell, moving the second user in the Massive MIMO cell out to the neighboring cell, moving the first user of the neighboring cell into the Massive MIMO cell and moving the second user in the Massive MIMO cell out to the neighboring cell, and maintaining the status quo. Under different load conditions, the advantages and disadvantages of each candidate load adjustment strategy are shown in the following table:

[0052] a＝0 a＝1 a＝2 a＝3 s＝0 1 2 5 3 s＝1 4 0 3 6 s＝2 3 2 5 1 s＝3 2 4 3 1

[0053] Step 204 , based on the quality of each candidate load adjustment strategy found, a candidate load adjustment strategy is selected as the load adjustment strategy matching the load state and executed.

[0054] In one example, according to the merits of each candidate load adjustment strategy found, selecting a candidate load adjustment strategy includes: directly selecting the load adjustment strategy with the highest merit from the merits of each candidate load adjustment strategy found, or selecting any one of the two load adjustment strategies with the highest merit from the merits of each candidate load adjustment strategy found, etc. As described in the above example, when the load state is s=2, the merits of each candidate load adjustment strategy are 3, 2, 5, and 1 respectively, and the load adjustment strategy a=2 with the highest merit is directly selected.

[0055] In one example, according to the quality of each candidate load adjustment strategy found, a candidate load adjustment strategy is selected as the load adjustment strategy matching the load state and executed as shown in the flowchart. Figure 4 As shown, including:

[0056] Step 2041, based on the ε-greedy algorithm, determine the selection method of the candidate load adjustment strategy; wherein the selection method is random selection or selection based on merit.

[0057] Specifically, a random number x is generated first, where 0≤x≤1; if x is less than ε, the candidate load adjustment strategy is selected randomly; if x is not less than ε, the candidate load adjustment strategy is selected according to the merits; where, The load adjustment method is executed periodically, and T is the current execution number. For example: T = 255, ε = 0.067, if x is 0.02, the selection method is random selection, and if x is 0.08, the selection method is based on the degree of superiority.

[0058] Step 2042: According to the selection method, a load adjustment strategy matching the load state is determined and selected from the candidate load adjustment strategies and executed.

[0059] Specifically, if the selection method is based on the merits, the candidate value with the highest merit is selected from the candidate load adjustment strategies. For example, when the load state is s=2, the merits of the candidate load adjustment strategies are 3, 2, 5, and 1, respectively, then the load adjustment strategy with the highest merit a=2 is selected. If the selection method is random selection, any candidate value is selected.

[0060] The ε-greedy algorithm can accurately determine the selection method of candidate load adjustment strategies, and then select the load adjustment strategy that matches the load state according to different selection methods. In this way, the appropriate load adjustment strategy that matches the load state can be selected more accurately.

[0061] In this embodiment, through such a method, the load adjustment strategy determined to match the load state is a better load adjustment strategy under the load state, that is, the load adjustment strategy selected in this way is more closely matched with the current busyness of the Massive MIMO cell, thereby increasing the possibility of improving the traffic of the Massive MIMO cell.

[0062] The third embodiment of the present invention relates to a load adjustment method. The third embodiment is substantially the same as the second embodiment, and the main difference is that the quality of the load adjustment strategy matched with the load state determined under the load state is updated. Figure 5 As shown, including:

[0063] Step 301: Obtain an indicator value of a first performance indicator of a massive MIMO cell.

[0064] Step 302: Determine the load state of the Massive MIMO cell according to the indicator value of the first performance indicator.

[0065] Step 303, searching for the superiority of each candidate load adjustment strategy under the load state; wherein the superiority refers to the degree of superiority of executing the load adjustment strategy under the load state.

[0066] Step 304 , based on the quality of each candidate load adjustment strategy found, a candidate load adjustment strategy is selected as the load adjustment strategy matching the load state and executed.

[0067] Steps 301-304 are similar to steps 201-204 and will not be described again here.

[0068] Step 305: updating the quality of the load adjustment strategy matched with the load state determined under the load state.

[0069] In one example, an adjustment value of the goodness can be preset, and the adjustment value of the goodness can be added to the original goodness to obtain a new goodness, and the new goodness can be updated to the goodness of the load adjustment strategy that matches the load state and is determined under the load state. For example, if the preset adjustment value of the goodness is 2, and the original goodness is 3, then the new goodness is 5, and the new goodness 5 is updated to the goodness of the load adjustment strategy that matches the load state and is determined under the load state.

[0070] In one example, a new merit may be preset, and the new merit is directly updated to the merit of the load adjustment strategy that matches the load state and is determined under the load state. For example, if the new merit is preset to 5, the new merit 5 is updated to the merit of the load adjustment strategy that matches the load state and is determined under the load state.

[0071] In one example, a flowchart of updating the quality of a load adjustment strategy that matches the load state determined under the load state is as follows: Figure 6 As shown, including:

[0072] Step 3051: Obtain an indicator value of a fourth performance indicator; wherein the fourth performance indicator includes a fourth performance indicator of the Massive MIMO cell and a fourth performance indicator of a neighboring cell.

[0073] Step 3052: Update the quality of the load adjustment strategy that matches the load state and is determined under the load state according to the indicator value of the fourth performance indicator.

[0074] Specifically, the fourth performance indicator can be a performance indicator of one type or a plurality of types. According to the indicator value of the fourth performance indicator, the value of the fourth performance indicator is substituted into a preset formula to calculate an adjustment value of the merit, and the merit of the load adjustment strategy that matches the load state determined under the load state is updated according to the adjustment value. Since when updating the merit of the load adjustment strategy that matches the load state determined under the load state, not only the fourth performance indicator of the Massive MIMO cell is considered, but also the fourth performance indicator of the neighboring cell is considered, the accuracy of the updated merit is further improved.

[0075] In one example, the fourth performance indicator includes the traffic payload of the Massive MIMO cell. 本 Traffic payload of neighboring cells 邻 . Substitute the fourth index value into the following formula:

[0076] Formula 1:

[0077] Formula 2:

[0078] in, is the adjustment value of the quality, The traffic of the MassiveMIMO cell obtained within the most recent preset time. The traffic of the Massive MIMO cell obtained within the next preset time. is the traffic of the neighboring cell with index value n obtained within the most recent preset time. is the total traffic of the neighboring cell with index value n obtained within the next preset time, and the evaluation weight β of the Massive MIMO cell MM =0.5, evaluation weight β of the area where the Massive MIMO cell is located Cluster =0.5, The maximum value of the merits of the candidate values ​​of the target operating parameters of the equipment in the current state for the next 15 minutes is initialized with a learning rate of α = 0.1 and a discount rate of γ = 0.9.

[0079] In this embodiment, the quality of the load adjustment strategy that matches the load state determined under different load states is continuously updated to make the quality more accurate, and each time the load adjustment strategy that matches the load state is determined, the quality is based on the most recently updated quality, thereby improving the rationality of the load adjustment strategy that matches the load state.

[0080] A fourth embodiment of the present invention relates to a server, such as Figure 7As shown, it includes at least one processor 402; and a memory 401 that is communicatively connected to the at least one processor; wherein the memory 401 stores instructions that can be executed by the at least one processor 402, and the instructions are executed by the at least one processor 402 so that the at least one processor 402 can execute an embodiment of the above-mentioned load adjustment method.

[0081] The memory 401 and the processor 402 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 402 and the memory 401 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 402 is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor 402.

[0082] The processor 402 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 401 can be used to store data used by the processor 402 when performing operations.

[0083] A fifth embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0084] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0085] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present invention, and that in actual applications, various changes may be made in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A load adjustment method, characterized in that: include: Obtaining an indicator value of a first performance indicator of a massive MIMO cell; determining a load state of the Massive MIMO cell according to an indicator value of the first performance indicator; Determine and execute a load adjustment strategy that matches the load state; wherein the load adjustment strategy includes one of the following: migrate a first user of a neighboring cell of the Massive MIMO cell into the Massive MIMO cell, record the neighboring cell as the current cell and the Massive MIMO cell as the target cell; migrate a second user in the Massive MIMO cell out to the neighboring cell, record the Massive MIMO cell as the current cell and the neighboring cell as the target cell; migrate a first user in the neighboring cell into the Massive MIMO cell and migrate a second user in the Massive MIMO cell out to the neighboring cell; maintain the status quo; Wherein, the determining and executing a load adjustment strategy matching the load state includes: Finding the pros and cons of each candidate load adjustment strategy under the load state; wherein the pros and cons refers to the degree of pros and cons of executing the load adjustment strategy under the load state; According to the quality of each of the candidate load adjustment strategies found, one of the candidate load adjustment strategies is selected as the load adjustment strategy matching the load state and executed; The first user or the second user is determined by: Selecting N users from the local cell; wherein N≥1 and N is preset; Measuring a second performance indicator of the selected user in the target cell to obtain an indicator value of the second performance indicator; If the indicator value of the second performance indicator is greater than the preset indicator value of the target cell, the selected user is used as the first user or the second user.

2. The load adjustment method according to claim 1, characterized in that: The step of migrating the first user of the neighboring cell into the Massive MIMO cell and selecting N users from the local cell include: Selecting N users whose downlink data volume is greater than a preset data volume from the local cell; The migrating the second user in the Massive MIMO cell to the neighboring cell, and the selecting N users from the current cell, include: N users with the smallest spectrum efficiency SE values ​​are selected from the users whose number of space division scheduling in the cell is greater than a preset number and whose space division scheduling ratio is greater than a preset threshold.

3. The load adjustment method according to claim 1, characterized in that: After determining and executing a load adjustment strategy matching the load state, the method further includes: Obtaining an indicator value of a third performance indicator; The preset indicator value of the target cell is updated according to the load state and the indicator value of the third performance indicator.

4. The load adjustment method according to any one of claims 1 to 3, characterized in that: The determining and executing a load adjustment strategy matching the load state includes: According to the preset correspondence between the load state and the load adjustment strategy, the load adjustment strategy corresponding to the load state is taken as the load adjustment strategy matching the load state and executed.

5. The load adjustment method according to claim 4, characterized in that: The correspondence between the preset load state and the load adjustment strategy includes: If the load state is that the Massive MIMO cell is not congested, the corresponding load adjustment strategy is to migrate the first user of the neighboring cell into the Massive MIMO cell; If the load status is that the Massive MIMO cell is congested, the corresponding load adjustment strategy is to migrate the second user in the Massive MIMO cell to the neighboring cell.

6. The load adjustment method according to claim 5, characterized in that: The selecting N users whose downlink data volume is greater than a preset data volume from the current cell includes: If the load status is that the Massive MIMO cell is not congested and is relatively busy, N users whose downlink data volume is greater than a preset data volume are selected from the cell.

7. The load adjustment method according to claim 3, characterized in that: After selecting one of the candidate load adjustment strategies as the load adjustment strategy matching the load state and executing the strategy according to the merits of the candidate load adjustment strategies found, the method further includes: The quality of the load adjustment strategy determined under the load state and matching the load state is updated.

8. The load adjustment method according to claim 7, characterized in that: The updating of the quality of the load adjustment strategy matched with the load state determined under the load state includes: Obtaining an indicator value of a fourth performance indicator; wherein the fourth performance indicator includes a fourth performance indicator of the Massive MIMO cell and a fourth performance indicator of the neighboring cell; The merit of the load adjustment strategy matched with the load state determined under the load state is updated according to the indicator value of the fourth performance indicator.

9. The load adjustment method according to claim 1, characterized in that: The selecting, according to the quality of each of the candidate load adjustment strategies found, one of the candidate load adjustment strategies as the load adjustment strategy matching the load state and executing the strategy comprises: Based on the ε-greedy algorithm, determine a selection method for the candidate load adjustment strategy; wherein the selection method is random selection or selection based on the merits; According to the selection method, a load adjustment strategy matching the load state is selected from the candidate load adjustment strategies and executed.

10. A server, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the load adjustment method according to any one of claims 1 to 9.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the load adjustment method according to any one of claims 1 to 9 is implemented.

Images