An energy-saving and emission-reducing method based on a 5G base station application scenario

Abstract

The application provides a kind of energy saving and emission reduction method based on 5G base station application scene, comprising: step 1, parameter assignment is carried out to 5G base station;Step 2, according to initial condition, the maximum allowable path loss of base station to user side is calculated;Step 3, according to the distribution of base station in scene, the establishment system coverage model of target area of coverage;Step 4, according to the performance adjustment coordinate system formed by coverage scene and performance adjustment parameter set of coverage scene, the performance control index E of coverage scene is dynamically adjusted according to adjustment parameter set within the allowable range, and the balance point is reached to achieve the purpose of controlling energy consumption.The application comprehensively considers the actual use scene of user under 5G base station, combines the use of frequency band under 5G network, user quantity, the distance between user and base station, user traffic demand, network quality condition, user value condition and other factors, and formulates corresponding base station equipment intelligent control means, so as to solve the problem of energy saving and emission reduction of 5G base station at equipment side.

Description

Technical Field

[0001] This invention belongs to the field of mobile communication technology, and specifically relates to an energy-saving and emission-reduction method based on 5G base station application scenarios. Background Technology

[0002] Because 5G base stations consume 3-4 times more energy than 4G, operators not only invest heavily in deploying 5G base stations but also have to pay huge electricity bills for the high energy consumption of 5G networks. Statistics show that communication network energy consumption accounts for 80%-90% of operators' total energy consumption, with wireless base stations accounting for 70%-80% of total energy consumption. Therefore, it is crucial to promote the green and high-quality development of new infrastructure represented by 5G in an orderly manner, contributing to the achievement of carbon peaking and carbon neutrality goals.

[0003] Current energy-saving and emission-reduction technologies for 5G base stations mainly control the power supply system, backup power system, equipment room environment monitoring, and fresh air system of the base station main equipment, or start and stop the base station equipment according to time. Such solutions lack certain intelligent means and are difficult to resolve the contradiction between user experience and wireless network coverage quality.

[0004] The main drawbacks of existing technologies include the following aspects:

[0005] 1. Existing 5G base station energy-saving technologies mainly use timed devices, which shut down the equipment within a specified time period by combining software and hardware, making it difficult to achieve energy-saving effects more precisely.

[0006] 2. Due to the wide coverage of 5G networks, the large number of users, and the different needs and usage times of various users, existing technologies mainly rely on network management data to uniformly formulate energy-saving strategies, making it difficult to solve the balance between user needs and network capabilities.

[0007] 3. The problem of excessive power consumption in 5G networks has not yet been properly resolved. This invention provides a solution from another perspective. Summary of the Invention

[0008] Purpose of the invention: The technical problem to be solved by the present invention is to address the shortcomings of the existing technology by providing an energy-saving and emission-reduction method based on 5G base station application scenarios. The present invention establishes behavioral habit models of different users in different scenarios and actual network demand models of users, and studies the contribution of new technologies to energy saving and carbon reduction. The aim is to reduce the energy consumption of base stations from the source without reducing the quality of operator communication services and the user experience of 5G communication, thereby achieving the purpose of saving energy and reducing the electricity costs of operators.

[0009] The method of the present invention includes the following steps:

[0010] Step 1: Assign parameter values ​​to the 5G base station;

[0011] Step 2: Calculate the maximum allowable path loss from the base station to the user based on the initial conditions;

[0012] Step 3: Establish a system coverage model based on the distribution of base stations within the scenario and the target coverage area;

[0013] Step 4: Based on the coverage scenario and performance adjustment coordinate system, form a set of performance adjustment parameters for the coverage scenario. Within the allowable range, the performance control index E of the coverage scenario is dynamically adjusted according to the adjustment parameter set to reach a balance point and achieve the purpose of controlling energy consumption.

[0014] Step 1 includes the following steps:

[0015] Step 1-1: Assign scenario values ​​S to the 5G base stations based on their coverage targets. i , i∈[1,N]; the value of i is assigned according to the preset scenario;

[0016] Steps 1-2: Input initial system conditions to establish the initial condition parameter set for the 5G base station energy efficiency evaluation model.

[0017] In step 1-1, when i = 1, the covered scenario is the university scenario;

[0018] When i=2, the covered scenario is the subway scenario;

[0019] When i=3, the covered scenario is the high-speed rail station scenario;

[0020] When i=4, the coverage scenario is a residential area scenario;

[0021] When i=5, the coverage scenario is supermarkets and shopping malls;

[0022] When i=N, the covered scenario is the user-defined scenario N.

[0023] In steps 1-2, the initial condition parameter set includes: the actual height h of the user terminal. UT The actual height of the base station, h BS Center frequency f c The straight-line distance d between the base station and the user terminal 3D Horizontal distance d between base station and user terminal 2D The average street width W and the average building height h.

[0024] In step 2, the maximum permissible path loss PL from the base station to the user side is calculated using the following formula. 3D-UMa-NLOs :

[0025]

[0026] Step 3 includes: retrieving the average number of connected users R during the cell's self-busy-time RRC (Radio Connection) and the cell-level 5G downlink traffic G of the base station covering the target area within the scenario, and establishing a performance adjustment coordinate system based on R and G, defining the maximum number of connectable users in the coordinate system as R. max The average number of users is defined as R0, and the maximum capacity is defined as G. max The average flow rate is defined as G0.

[0027] In step 3, the four parameter values ​​R max R0, G max G0 is the value of the 5G base station coverage scenario S. i Confirmed after retrieving network management data.

[0028] Step 3 also includes: determining the region where the value (R, G) is located in the coordinate system at the current time point within the current scene (this can be done manually or by software) to obtain the load-bearing discrimination value C. i , i∈[1,4], C1 indicates that the current base station is located in the region where the number of users is higher than the average number of users R0 and the average traffic value G0, which indicates that the base station is in a high load region; C2 indicates that the current base station is located in the region where the number of users is higher than the average number of users R0 and the average traffic value G0, which indicates that the base station is in a medium load region; C3 indicates that the current base station is located in the region where the number of users is lower than the average number of users R0 and the average traffic value G0, which indicates that the base station is in a medium load region; C4 indicates that the current base station is located in the region where the number of users is lower than the average number of users R0 and the average traffic value G0, which indicates that the base station is in a low load region.

[0029] Step 4 includes: determining the bearer discrimination value C under the 5G base station scenario. i And the pre-adjustment value (a percentage of the typical power consumption of the base station equipment, which can be set as needed), combined with the maximum allowable path loss PL 3D-UMa-NLOs The system makes a judgment. If the maximum allowable path loss is exceeded, the system returns the pre-adjustment value. If the maximum allowable path loss is not exceeded, the power consumption of the base station equipment is adjusted, and the network operation status after adjustment is detected. When the next preset custom time point arrives, a new round of adjustment is started (the adjustment method repeats the previous steps). In this way, dynamic adjustment is performed to reduce the power consumption of the equipment.

[0030] The present invention also provides a storage medium storing a computer program or instructions, which, when the computer program or instructions are run, implement the energy-saving and emission-reduction method based on a 5G base station application scenario.

[0031] Beneficial effects: Current energy-saving and emission-reduction technical solutions for 5G base stations mainly control the power supply system, backup power system, equipment room environment monitoring, and fresh air system of the base station main equipment, or start and stop the base station equipment according to time. Such solutions lack certain intelligent means and are difficult to resolve the contradiction between user experience and wireless network coverage quality.

[0032] Based on the above reasons, this invention comprehensively considers the actual usage scenarios of users under 5G base stations, and combines factors such as the usage of frequency bands under 5G networks, the number of users, the distance between users and base stations, user traffic demand, network quality, and user value to formulate corresponding intelligent control methods for base station equipment, thereby solving the problem of energy conservation and emission reduction on the equipment side of 5G base stations. Attached Figure Description

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0034] Figure 1 This is a schematic diagram of the initial condition parameters for the 5G base station energy efficiency assessment model.

[0035] Figure 2 This is a schematic diagram of the system coverage model.

[0036] Figure 3 This is a schematic diagram of the performance adjustment coordinate system.

[0037] Figure 4 It is a flowchart for dynamically adjusting performance indicators. Detailed Implementation

[0038] This invention provides an energy-saving and emission-reduction method based on 5G base station application scenarios, comprising the following steps:

[0039] Step 1: Assign parameter values ​​to the 5G base station:

[0040] Step 1-1: Assign scenario values ​​S to the 5G base stations according to their coverage targets, as shown in Table 1. i , i∈[1,N]. The value of i is assigned according to the preset scenario, which is shown in Table 1.

[0041] Table 1

[0042] Scene value (Si) Coverage scenarios i＝1 University Scene i＝2 subway scene i＝3 High-speed rail station scene i＝4 Residential area scene i＝5 Supermarket scene i = N User-defined scenario N

[0043] Taking a real-world example, consider a scenario where a certain operator's 5G coverage extends to a supermarket, with the scenario value being S5.

[0044] Steps 1-2: Input the initialization system conditions, such as... Figure 1As shown, the initial condition parameter set for establishing the 5G base station energy efficiency evaluation model includes: the actual height h of the user terminal (UE). UT Unit: meters; actual height of base station: h BS Antenna height, in meters; center frequency f c Unit: GHz; Straight-line distance d between the base station and the user terminal (UE) 3D The unit is meters; the horizontal distance d between the base station and the user terminal. 2D Units: meters; average street width W; average building height h.

[0045] Step 2: Based on the initial conditions, calculate the maximum allowable path loss from the base station to the user side. The formula refers to the 3D-UMa NLOS channel model for 5G in the 0.5–100 GHz frequency range of 3GPP, as follows:

[0046]

[0047] Taking a practical example: In this supermarket scenario, under the 3.5G frequency band, by inputting various parameters and calculating, the PL can be obtained. 3D-UMa-NLOS It is 131.08 dB.

[0048] Step 3: Based on the distribution of base stations within the scenario and the target coverage area, establish a system coverage model, such as... Figure 2 As shown, Figure 2 In this context, gNB represents a 5G base station, DL represents the downlink, UL represents the uplink, and UE represents the user terminal. The system retrieves the average number of connected users R during the cell's busy-hour RRC (Radio Connections) and the cell-level 5G downlink traffic G from the base stations covering the target area within the scenario. A performance adjustment coordinate system is then established based on R and G, such as... Figure 3 As shown, the maximum number of users that can be connected in the coordinate system is defined as R. max The average number of users is defined as R0, and the maximum capacity is defined as G. max The average flow rate is defined as G0, and the region where the value (R, G) is located in the coordinate system at the current time point in the current scene is determined to obtain the carrying capacity discrimination value C. i , i∈[1,4], C1 indicates that the current base station is located in the region where the number of users is higher than the average number of users R0 and the average traffic value G0, which indicates that the base station is in a high load region; C2 indicates that the current base station is located in the region where the number of users is higher than the average number of users R0 and the average traffic value G0, which indicates that the base station is in a medium load region; C3 indicates that the current base station is located in the region where the number of users is lower than the average number of users R0 and the average traffic value G0, which indicates that the base station is in a medium load region; C4 indicates that the current base station is located in the region where the number of users is lower than the average number of users R0 and the average traffic value G0, which indicates that the base station is in a low load region.

[0049] Taking a real-world example: Data retrieved from the operator's network management system for this supermarket scenario shows that the average number of connected users (R) during busy hours in a certain 5G base station cell is 99, the cell-level 5G downlink traffic (G) is 10.55 GByte, the average number of users (R0) is 127.29, the average traffic value (G0) is 15.01 GByte, and the maximum number of users (R) is... max The maximum flow rate is 400, with a maximum flow rate of G. max With a capacity of 500 GByte (the maximum number of users and the maximum traffic value are set by the user based on the 5G device capabilities and user perception indicators), it can be determined that the 5G cell is located in the C4 area at this point in time, which is a low-load area.

[0050] Step 4, based on the coverage scenario S i and performance adjustment coordinate system C i A set of performance adjustment parameters for the coverage scenario is formed. The coverage scenario performance control index E is dynamically adjusted within the allowable range according to the adjustment parameter set to reach an equilibrium point and achieve the goal of controlling energy consumption. The adjustment process is as follows: Figure 4 As shown.

[0051] Taking a practical example, based on the bearer discrimination value C4 under scenario S5 of this 5G base station cell, the pre-adjustment value is set to 90%, combined with the maximum allowable path loss PL. 3D-UMa-NLOS =131.08dB was used for judgment. After adjustment, it did not exceed the maximum allowable path loss. The power consumption of the base station equipment was adjusted to 90%. The network operation status was monitored after the adjustment, and no abnormalities were found. After 1 minute (the adjustment time can be customized), according to the calculation in steps 1-3, the bearer discrimination value C4 under scenario S5 of this 5G base station cell was set to a pre-adjustment value of 80%, combined with the maximum allowable path loss PL. 3D-UMa-NLOS The power consumption was assessed at 131.08 dB. After adjustment, it did not exceed the maximum allowable path loss. The base station power consumption was then adjusted to 80%, and network operation was monitored afterward; no abnormalities were found. This adjustment process was repeated, and the base station power consumption was adjusted to 40%. Network operation was monitored again, and an abnormal alarm occurred. At this point, the adjustment was complete, and no further adjustments to the base station power consumption were made. This adjustment effectively reduced the base station power consumption by 50%.

[0052] This invention provides an energy-saving and emission-reduction method based on 5G base station application scenarios. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. An energy-saving and emission-reduction method based on 5G base station application scenarios, characterized in that, Includes the following steps: Step 1: Assign parameter values ​​to the 5G base station; Step 1 includes the following steps: Step 1-1: Assign scenario values ​​to 5G base stations based on their coverage targets. , ; Values ​​are assigned according to preset scenarios; Steps 1-2: Input initial system conditions to establish the initial condition parameter set for the 5G base station energy efficiency evaluation model; In step 1-1, when i=1, the covered scenario is the university scenario; When i=2, the covered scenario is the subway scenario; When i=3, the covered scenario is the high-speed rail station scenario; When i=4, the coverage scenario is a residential area scenario; When i=5, the coverage scenario is supermarket / shopping mall scenario; When i=N, the covered scenario is the user-defined scenario N; In steps 1-2, the initial condition parameter set includes: the actual height of the user terminal. Actual height of the base station Center frequency The straight-line distance between the base station and the user terminal Horizontal distance between base station and user terminal average street width and the average height of the building, h; Step 2: Calculate the maximum allowable path loss from the base station to the user based on the initial conditions; In step 2, the maximum permissible path loss from the base station to the user is calculated using the following formula. : ； Step 3: Based on the base station distribution within the scenario and the target coverage area, retrieve the average number of connected users R during the cell's self-busy period and the cell-level 5G downlink traffic G of the base stations covering the target coverage area within the scenario. Establish a performance adjustment coordinate system based on R and G, and define the maximum number of connectable users in the coordinate system as... Average number of users is defined as The maximum carrying capacity is defined as follows: The average flow rate is defined as follows: ; Four parameter values , , , Value of 5G base station coverage scenarios Determine this after retrieving network management data; For the values ​​in the coordinate system at the current time point within the current scene The location area is used to determine the load-bearing capacity discrimination value. , , This indicates that the current base station is carrying a higher number of users than the average. And higher than the average flow rate The area indicates that the base station is in a high-load zone; This indicates that the current base station is carrying a higher number of users than the average. And below the average flow rate The region indicates that the base station is in a medium-load area; This indicates that the current base station is carrying fewer than the average number of users. And higher than the average flow rate The region indicates that the base station is in a medium-load area; This indicates that the current base station is carrying fewer than the average number of users. And below the average flow rate The area indicates that the base station is in a low-load zone; Step 4: Based on the coverage scene and performance adjustment coordinate system, form a set of performance adjustment parameters and performance control indicators for the coverage scene. Within the allowable range, dynamic adjustments are made according to the set of adjustment parameters to reach a balance point and achieve the purpose of controlling energy consumption; Step 4 includes: determining the bearer discrimination value based on the 5G base station scenario. And the pre-adjustment value, combined with the maximum permissible path loss The system makes a judgment. If the maximum allowable path loss is exceeded, the system returns the pre-adjustment value. If the maximum allowable path loss is not exceeded, the power consumption of the base station equipment is adjusted, and the network operation status after adjustment is detected. When the next preset custom time point arrives, a new round of adjustment is started.

2. A storage medium, characterized in that, It stores a computer program or instructions that, when executed, implement the method as described in claim 1.

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