Method, device and storage medium for evaluating energy efficiency of wireless network

Abstract

The application provides a wireless network energy efficiency evaluation method, device and storage medium, and is applied to the field of communication. The method comprises the following steps: acquiring network performance data of a base station, energy consumption data of a device, and a service type corresponding to a wireless network service; determining a corresponding quality factor based on the service type, determining a service output based on the performance data, and determining effective energy consumption of the device based on the energy consumption data; and determining a target energy efficiency corresponding to the service type based on a ratio of a product of the quality factor and the service output to the effective energy consumption. The method of the application achieves the technical effect of improving the accuracy of energy efficiency evaluation.

Description

Technical Field

[0001] This application relates to the field of communications, and more particularly to a method, device, and storage medium for evaluating the energy efficiency of wireless networks. Background Technology

[0002] With the popularization of wireless networks and the increase in energy consumption of network equipment, society has become concerned about energy consumption issues, and the assessment of wireless network energy efficiency has become an important direction for the development of Internet technology.

[0003] The 3GPP protocol defines three main service types for 5G networks: eMBB, uRLLC, and mMTC. For eMBB, energy efficiency is assessed by measuring the amount of data transmitted and the spectrum resources used. For uRLLC, energy efficiency is assessed by measuring energy consumption and the amount of data transmitted. For mMTC, energy efficiency is assessed by measuring the number of supported devices and the area per unit area.

[0004] In existing energy efficiency calculations, for eMBB services, energy efficiency calculations primarily consider the traffic volume dimension, neglecting the goals of wireless network construction and ignoring the output effect of base stations built for coverage purposes. Based on traffic volume, energy efficiency calculations weaken the actual effective output of coverage-oriented base stations. Furthermore, existing energy efficiency calculations add the calculation of effective energy consumption of renewable resources, reducing the accuracy of wireless network energy efficiency calculations. In energy efficiency calculations for different service types, a comprehensive energy efficiency assessment is not conducted across multiple service scenarios and construction goals, resulting in a simplistic and unsystematic assessment method. Therefore, existing energy efficiency assessments suffer from low accuracy. Summary of the Invention

[0005] This application provides a method, device, and storage medium for evaluating the energy efficiency of wireless networks, in order to solve the technical problem of low accuracy in existing energy efficiency evaluation methods.

[0006] In a first aspect, this application provides a method for evaluating the energy efficiency of a wireless network, including:

[0007] Obtain network performance data of the base station, energy consumption data of the equipment, and the service type corresponding to the wireless network service;

[0008] The corresponding quality factor is determined based on the business type, the business output is determined based on performance data, and the effective energy consumption of the equipment is determined based on energy consumption data.

[0009] The target energy efficiency for each business type is determined by the ratio of the product of the quality factor and the business output to the effective energy consumption.

[0010] Optionally, the corresponding quality factor is determined based on the service type, the service output is determined based on performance data, and the effective energy consumption of the equipment is determined based on energy consumption data, including:

[0011] Determine whether the service type is eMBB; if so, determine the corresponding main scenario type and quality factor based on the wireless network service.

[0012] Determine business outputs based on main scenario type, quality factors, and performance data;

[0013] The preset renewable energy usage time ratio of the equipment is determined based on energy consumption data, and the first effective energy consumption of the equipment is determined based on the preset renewable energy usage time ratio.

[0014] Optionally, the corresponding main scenario type and quality factor are determined based on the wireless network service, including:

[0015] When the main scenario type is determined to be capacity-type based on wireless network services, the corresponding first quality factor is determined based on the uplink retransmission rate and downlink retransmission rate of the RLC layer corresponding to the eMBB service type.

[0016] When the main scenario type is determined to be coverage type based on wireless network services, the method for obtaining the coverage quality factor of the eMBB service type is obtained, and the second quality factor corresponding to the coverage type is determined based on the method of obtaining the coverage quality factor.

[0017] Optionally, business outputs are determined based on the main scenario type, quality factor, and performance data, including:

[0018] Based on performance data, the eMBB service type is determined. When the main scenario is capacity-based, the service volume of the RLC layer is determined, and the first service output is determined based on the service volume.

[0019] Based on performance data, determine the eMBB service type when the main scenario is coverage type, the corresponding base station signal coverage area, and determine the second service output based on the base station signal coverage area.

[0020] Optionally, determining the corresponding quality factor based on the service type, determining the service output based on performance data, and determining the effective energy consumption of the equipment based on energy consumption data also includes:

[0021] When the service type is uRLLC, determine the third quality factor corresponding to the uRLLC service type;

[0022] Based on performance data, determine the network reliability and end-to-end latency corresponding to the uRLLC service type, and determine the third service output corresponding to the uRLLC service type;

[0023] The network slicing method based on uRLLC service type determines the slice energy consumption corresponding to uRLLC service type, and the corresponding second effective energy consumption is determined based on the slice energy consumption.

[0024] Optionally, determining the corresponding quality factor based on the business type and the business output based on performance data also includes:

[0025] When the service type is mMTC, determine the fourth quality factor corresponding to the mMTC service type;

[0026] The number of supported connections corresponding to each mMTC service type is determined based on performance data, and the fourth service output corresponding to each mMTC service type is also determined.

[0027] The network slicing method based on mMTC service type determines the slice energy consumption corresponding to the mMTC service type, and the corresponding third effective energy consumption is determined based on the slice energy consumption.

[0028] Optionally, the energy efficiency corresponding to a service type is determined based on the ratio of the product of the quality factor and the service output to the effective energy consumption, including:

[0029] When the service type is eMBB, the first energy efficiency is determined based on the ratio of the product of the first quality factor and the first service output to the first effective energy consumption.

[0030] The second energy efficiency is determined based on the ratio of the product of the second quality factor and the second service output to the first effective energy consumption.

[0031] The target energy efficiency corresponding to the eMBB service type is determined based on the first energy efficiency and the second energy efficiency.

[0032] When the service type is uRLLC, the ratio of the product of the third quality factor and the third service output to the second effective energy consumption is determined as the third energy efficiency, and the third energy efficiency is determined as the target energy efficiency corresponding to the uRLLC service type.

[0033] When the service type is mMTC, the ratio of the product of the fourth quality factor and the fourth service output to the third effective energy consumption is determined as the fourth energy efficiency, and the fourth energy efficiency is determined as the target energy efficiency corresponding to the mMTC service type.

[0034] Optionally, after determining the target energy efficiency corresponding to the eMBB service type based on the first energy efficiency and the second energy efficiency, the following may also be included:

[0035] Compare the average first energy efficiency and the average second energy efficiency of eMBB service types in different wireless networks, and obtain the comparison results;

[0036] The average value of two first energy efficiency values ​​corresponding to the eMBB service type is determined based on any two wireless networks. The comparison coefficient is calculated based on the distribution of uplink and downlink traffic of the base stations corresponding to the two wireless networks. The average value of the two first energy efficiency values ​​is compared and calculated based on the comparison coefficient, and the comparison result is determined.

[0037] The unit energy efficiency of the first energy efficiency and the unit energy efficiency of the second energy efficiency are compared based on the preset weights of the eMBB service types in the same wireless network, and the comparison results are obtained.

[0038] Optionally, after determining the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and service output to the effective energy consumption, the method further includes:

[0039] Based on the preset planning data, the planning business output, planning quality factor and planning effective energy consumption are determined, and the predicted energy efficiency corresponding to the planning business output, planning quality factor and planning effective energy consumption is calculated based on the preset energy efficiency formula.

[0040] The fitting calculation is performed based on the preset planning data and predicted energy efficiency, and the fitting curve is determined.

[0041] Based on the fitted curve, the predicted service output is predicted and determined. Based on the predicted service output, the predicted energy consumption is calculated and determined.

[0042] Secondly, this application provides a wireless network energy efficiency assessment device, comprising:

[0043] The acquisition module is used to acquire network performance data of the base station, energy consumption data of the device, and the service type corresponding to the wireless network service;

[0044] The processing module is used to determine the corresponding quality factor based on the business type, determine the business output based on performance data, and determine the effective energy consumption of the equipment based on energy consumption data.

[0045] The determination module is used to determine the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption.

[0046] Optionally, the device is also used for:

[0047] Determine whether the service type is eMBB; if so, determine the corresponding main scenario type and quality factor based on the wireless network service.

[0048] Determine business outputs based on main scenario type, quality factors, and performance data;

[0049] The preset renewable energy usage time ratio of the equipment is determined based on energy consumption data, and the first effective energy consumption of the equipment is determined based on the preset renewable energy usage time ratio.

[0050] Optionally, the device is also used for:

[0051] When the main scenario type is determined to be capacity-type based on wireless network services, the corresponding first quality factor is determined based on the uplink retransmission rate and downlink retransmission rate of the RLC layer corresponding to the eMBB service type.

[0052] When the main scenario type is determined to be coverage type based on wireless network services, the method for obtaining the coverage quality factor of the eMBB service type is obtained, and the second quality factor corresponding to the coverage type is determined based on the method of obtaining the coverage quality factor.

[0053] Optionally, the device is also used for:

[0054] Based on performance data, the eMBB service type is determined. When the main scenario is capacity-based, the service volume of the RLC layer is determined, and the first service output is determined based on the service volume.

[0055] Based on performance data, determine the eMBB service type when the main scenario is coverage type, the corresponding base station signal coverage area, and determine the second service output based on the base station signal coverage area.

[0056] Optionally, the device is also used for:

[0057] When the service type is uRLLC, determine the third quality factor corresponding to the uRLLC service type;

[0058] Based on performance data, determine the network reliability and end-to-end latency corresponding to the uRLLC service type, and determine the third service output corresponding to the uRLLC service type;

[0059] The network slicing method based on uRLLC service type determines the slice energy consumption corresponding to uRLLC service type, and the corresponding second effective energy consumption is determined based on the slice energy consumption.

[0060] Optionally, the device is also used for:

[0061] When the service type is mMTC, determine the fourth quality factor corresponding to the mMTC service type;

[0062] The number of supported connections corresponding to each mMTC service type is determined based on performance data, and the fourth service output corresponding to each mMTC service type is also determined.

[0063] The network slicing method based on mMTC service type determines the slice energy consumption corresponding to the mMTC service type, and the corresponding third effective energy consumption is determined based on the slice energy consumption.

[0064] Optionally, the device is also used for:

[0065] When the service type is eMBB, the first energy efficiency is determined based on the ratio of the product of the first quality factor and the first service output to the first effective energy consumption.

[0066] The second energy efficiency is determined based on the ratio of the product of the second quality factor and the second service output to the first effective energy consumption.

[0067] The target energy efficiency corresponding to the eMBB service type is determined based on the first energy efficiency and the second energy efficiency.

[0068] When the service type is uRLLC, the ratio of the product of the third quality factor and the third service output to the second effective energy consumption is determined as the third energy efficiency, and the third energy efficiency is determined as the target energy efficiency corresponding to the uRLLC service type.

[0069] When the service type is mMTC, the ratio of the product of the fourth quality factor and the fourth service output to the third effective energy consumption is determined as the fourth energy efficiency, and the fourth energy efficiency is determined as the target energy efficiency corresponding to the mMTC service type.

[0070] Optionally, the device is also used for:

[0071] Compare the average first energy efficiency and the average second energy efficiency of eMBB service types in different wireless networks, and obtain the comparison results;

[0072] The average value of two first energy efficiency values ​​corresponding to the eMBB service type is determined based on any two wireless networks. The comparison coefficient is calculated based on the distribution of uplink and downlink traffic of the base stations corresponding to the two wireless networks. The average value of the two first energy efficiency values ​​is compared and calculated based on the comparison coefficient, and the comparison result is determined.

[0073] The unit energy efficiency of the first energy efficiency and the unit energy efficiency of the second energy efficiency are compared based on the preset weights of the eMBB service types in the same wireless network, and the comparison results are obtained.

[0074] Optionally, the device is also used for:

[0075] Based on the preset planning data, the planning business output, planning quality factor and planning effective energy consumption are determined, and the predicted energy efficiency corresponding to the planning business output, planning quality factor and planning effective energy consumption is calculated based on the preset energy efficiency formula.

[0076] The fitting calculation is performed based on the preset planning data and predicted energy efficiency, and the fitting curve is determined.

[0077] Based on the fitted curve, the predicted service output is predicted and determined. Based on the predicted service output, the predicted energy consumption is calculated and determined.

[0078] A third aspect of this application provides a wireless network energy efficiency assessment device, comprising:

[0079] Processor and memory;

[0080] The memory stores the instructions that the computer executes;

[0081] The processor executes computer execution instructions stored in memory, causing the wireless network energy efficiency assessment device to perform any of the wireless network energy efficiency assessment methods in the first aspect.

[0082] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the wireless network energy efficiency evaluation method as described in any of the first aspects.

[0083] This application provides a method, device, and storage medium for evaluating the energy efficiency of a wireless network. The method includes: acquiring network performance data of a base station, energy consumption data of a device, and the service type corresponding to the wireless network service; determining the corresponding quality factor based on the service type, determining the service output based on the performance data, and determining the effective energy consumption of the device based on the energy consumption data; and determining the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption. Based on the acquired network performance data and device energy consumption data, the corresponding service output, quality factor, and effective energy consumption of the wireless network are determined. According to different service types of the wireless network, the quality factor, service output, and effective energy consumption for energy efficiency calculation are determined for different service types, and the target energy efficiency for different service types is calculated. Based on the target energy efficiency, the energy efficiency of the wireless network is evaluated. Compared with existing technologies, this application considers the impact of renewable energy in the calculation of effective energy consumption, considers the energy efficiency under different main scenarios in the eMBB service type, and comprehensively evaluates the overall wireless network energy efficiency. For uRLLC and mMTC service types, the slice energy consumption of the wireless network is obtained, and the quality factor and service output are determined based on actual measurements, and the corresponding slice energy efficiency is calculated to achieve the evaluation of wireless network energy efficiency. By calculating the wireless network energy efficiency for different types of service output and different service types separately, the accuracy of energy efficiency evaluation is increased; thus, the technical effect of improving the accuracy of energy efficiency evaluation is achieved. Attached Figure Description

[0084] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0085] Figure 1 The flowchart of the wireless network energy efficiency evaluation method provided in the embodiments of this application Figure 1 ;

[0086] Figure 2 The flowchart of the wireless network energy efficiency evaluation method provided in the embodiments of this application Figure 2 ;

[0087] Figure 3 The flowchart of the wireless network energy efficiency evaluation method provided in the embodiments of this application Figure 3 ;

[0088] Figure 4 A flowchart of an energy consumption prediction method based on wireless network energy efficiency assessment provided in an embodiment of this application;

[0089] Figure 5 A schematic diagram of the structure of the wireless network energy efficiency evaluation device provided in the embodiments of this application;

[0090] Figure 6 This is a hardware structure diagram of the wireless network energy efficiency evaluation device provided in an embodiment of this application.

[0091] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0092] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0093] First, let me explain the terms used in this application:

[0094] Enhanced Mobile Broadband (eMBB service type): This is an application scenario in 5G networks, mainly targeting mobile broadband users, providing higher data transmission rates and lower latency to meet the needs of high-bandwidth applications such as high-definition video, virtual reality, and augmented reality.

[0095] Ultra-Reliable and Low Latency Communications (uRLLC) is an application scenario in 5G networks, primarily targeting applications with extremely high requirements for communication latency and reliability, such as autonomous driving, telemedicine, and industrial automation. uRLLC requires the network to have extremely low transmission latency and extremely high reliability to ensure real-time performance and reliability.

[0096] Massive Machine Type Communications (mMTC) is an application scenario in 5G networks, primarily targeting Internet of Things (IoT) applications such as smart homes, smart cities, and intelligent transportation. mMTC requires the network to support large-scale device connections and data transmission to meet the communication needs of the massive number of devices in the IoT.

[0097] Radio Resource Control (RRC) refers to the management, control, and scheduling of wireless resources through certain strategies and methods. Under the premise of meeting the requirements of quality of service, it makes full use of limited wireless network resources, ensures coverage of the planned area, and improves service capacity and resource utilization.

[0098] Radio Access Technology (RAT): A basic physical link method for wireless communication networks that can support multiple radio access technologies in a single device.

[0099] Radio Link Control (RLC) is a part of the data plane protocol in the wireless protocol architecture. It is used for, but is not limited to, data segmentation and reassembly, connection control, and flow control.

[0100] New Radio (NR) refers to 5G wireless networks, designed to provide higher data transmission rates, lower and better network capacity to meet future mobile communication needs.

[0101] 3GPP Long Term Evolution (LTE): is currently the mainstream mobile communication technology, offering high speeds and low latency.

[0102] The 3GPP protocol defines three main service types for 5G networks: eMBB, uRLLC, and mMTC. For eMBB, energy efficiency is assessed by measuring the amount of data transmitted and the spectrum resources used. For uRLLC, energy efficiency is assessed by measuring energy consumption and the amount of data transmitted. For mMTC, energy efficiency is assessed by measuring the number of supported devices and the area per unit area. In existing energy efficiency calculations, for eMBB services, energy efficiency calculations primarily consider the traffic volume dimension, neglecting the goals of wireless network construction and ignoring the output effect of base stations built for coverage purposes. Based on traffic volume, energy efficiency calculations weaken the actual effective output of coverage-oriented base stations. Furthermore, existing energy efficiency calculations add the calculation of effective energy consumption of renewable resources, reducing the accuracy of wireless network energy efficiency calculations. In energy efficiency calculations for different service types, a comprehensive energy efficiency assessment is not conducted across multiple service scenarios and construction goals, resulting in a simplistic and unsystematic assessment method. Therefore, existing energy efficiency assessments suffer from low accuracy.

[0103] This application provides a method, device, and storage medium for evaluating the energy efficiency of a wireless network. The method includes: acquiring network performance data of a base station, energy consumption data of a device, and the service type corresponding to the wireless network service; determining the corresponding quality factor based on the service type, determining the service output based on the performance data, and determining the effective energy consumption of the device based on the energy consumption data; and determining the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption. Based on the acquired network performance data and device energy consumption data, the corresponding service output, quality factor, and effective energy consumption of the wireless network are determined. According to different service types of the wireless network, the quality factor, service output, and effective energy consumption for energy efficiency calculation are determined for different service types, and the target energy efficiency for different service types is calculated. Based on the target energy efficiency, the energy efficiency of the wireless network is evaluated. Compared with existing technologies, this application considers the impact of renewable energy in the calculation of effective energy consumption, considers the energy efficiency under different main scenarios in the eMBB service type, and comprehensively evaluates the overall wireless network energy efficiency. For uRLLC and mMTC service types, the slice energy consumption of the wireless network is obtained, and the quality factor and service output are determined based on actual measurements. The corresponding slice energy efficiency is calculated to achieve the evaluation of wireless network energy efficiency. By calculating the wireless network energy efficiency for different types of service output and different service types, the accuracy of energy efficiency evaluation is increased. This achieves the technical effect of improving the accuracy of energy efficiency evaluation and solves the technical problem of low accuracy in energy efficiency evaluation in existing technologies.

[0104] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0105] Figure 1 The flowchart of the wireless network energy efficiency evaluation method provided in the embodiments of this application Figure 1 .like Figure 1 As shown in the embodiments of this application, the wireless network energy efficiency evaluation method includes:

[0106] S101. Obtain network performance data of the base station, energy consumption data of the equipment, and service types corresponding to wireless network services;

[0107] In this embodiment, the service types corresponding to the wireless network include: eMBB service type, uRLLC service type, and mMTC service type. In this embodiment, the network performance data of the base station is obtained through the device network management of the wireless network, and the device uploads the energy consumption data to the device network management. Among them, the network performance data of the base station refers to the various performance index data generated by the base station during operation, including the base station's traffic volume, latency, and RRC connection number. The energy consumption data of the device refers to the energy consumed by the device when using the wireless network, which may include data such as the device's power consumption, energy life, and device charging time. The energy consumption of the device needs to be differentiated according to different wireless access technologies. For multi-mode sites, the energy consumption of the device should be proportionally allocated among each RAT according to the configured radio frequency power transmitted by each RAT.

[0108] S102. Determine the corresponding quality factor based on the business type, determine the business output based on performance data, and determine the effective energy consumption of the equipment based on energy consumption data.

[0109] In this embodiment, the service output of the base station includes, but is not limited to: data communication, SMS service output, multimedia communication output, and location service output; the effective energy consumption of the device refers to the energy consumed by the device when performing a specific task or providing a specific function.

[0110] S103. Determine the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption.

[0111] In this embodiment, the energy efficiency calculation formula is as follows: Where EE stands for Energy Efficiency, TO for Traffic Output, QF for Quality Factor, and EC for Energy Consumption. According to the definition of 5G networks, the product of Traffic Output and Quality Factor is the network's effective output.

[0112] This application provides a method for evaluating the energy efficiency of a wireless network. The method includes: acquiring network performance data of a base station, energy consumption data of a device, and the service type corresponding to the wireless network service; determining the corresponding quality factor based on the service type, determining the service output based on the performance data, and determining the effective energy consumption of the device based on the energy consumption data; and determining the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption. Based on the acquired network performance data and device energy consumption data, the corresponding service output, quality factor, and effective energy consumption of the wireless network are determined. According to different service types of the wireless network, the quality factor, service output, and effective energy consumption for energy efficiency calculation are determined for different service types, and the target energy efficiency for different service types is calculated. Based on the target energy efficiency, the energy efficiency of the wireless network is evaluated. Compared with existing technologies, this application considers the impact of renewable energy in the calculation of effective energy consumption, considers the energy efficiency under different main scenarios in the eMBB service type, and comprehensively evaluates the overall wireless network energy efficiency. For uRLLC and mMTC service types, the slice energy consumption of the wireless network is obtained, and the quality factor and service output are determined based on actual measurements. The corresponding slice energy efficiency is calculated to achieve the evaluation of wireless network energy efficiency. By calculating the wireless network energy efficiency for different types of service output and different service types, the accuracy of energy efficiency evaluation is increased. This achieves the technical effect of improving the accuracy of energy efficiency evaluation and solves the technical problem of low accuracy in energy efficiency evaluation in existing technologies.

[0113] Figure 2 The flowchart of the wireless network energy efficiency evaluation method provided in the embodiments of this application Figure 2 , Figure 3 The flowchart of the wireless network energy efficiency evaluation method provided in the embodiments of this application Figure 3 , Figure 4 A flowchart illustrating the energy consumption prediction method based on wireless network energy efficiency assessment provided in this application embodiment. Figure 2 , Figure 3 and Figure 4 As shown in the embodiments of this application, the wireless network energy efficiency evaluation method includes:

[0114] S201. Obtain network performance data of the base station, energy consumption data of the equipment, and service types corresponding to wireless network services;

[0115] S202. Determine whether the service type is eMBB service type. If so, when the main scenario type is determined to be capacity type based on the wireless network service, determine the corresponding first quality factor based on the uplink retransmission rate and downlink retransmission rate of the RLC layer corresponding to the eMBB service type; when the main scenario type is determined to be coverage type based on the wireless network service, obtain the coverage quality factor acquisition method of the eMBB service type, and determine the second quality factor corresponding to the coverage type based on the acquisition method.

[0116] In this embodiment, the eMBB service type is applied to wide area networks, the uRLLC service type and the mMTC service type are applied to local area private networks or network slices. The uplink retransmission rate and downlink retransmission rate of the RLC layer are: when data transmitted by the base station is corrupted or lost during transmission, the RLC layer triggers a retransmission mechanism to retransmit the data to ensure reliable transmission. The uplink retransmission rate refers to the proportion of retransmissions that occur during uplink data transmission, where uplink data refers to data sent from the terminal device to the base station; the downlink retransmission rate refers to the proportion of retransmissions that occur during downlink data transmission at the RLC layer in wireless communication, where downlink data refers to data sent from the base station to the terminal device.

[0117] like Figure 3 As shown, in the first example, the traffic quality factor is the uplink retransmission rate or downlink retransmission rate of the 1-RLC layer, i.e., the first quality factor.

[0118] In this embodiment, when the main scenario type is coverage, the method for obtaining the coverage quality factor of the eMBB service type can be: collecting data based on indicators or performing traversal tests; wherein the traversal tests include: Coverage Quality Test (CQT) and Drive Test (DT).

[0119] like Figure 3 As shown in the second example, the coverage quality factor is obtained through CQT / DT traversal test. The coverage quality factor is the proportion of the Reference Signal Received Power (RSRP) that is greater than a preset threshold. In this example, the preset threshold is set to 105dB. The proportion that exceeds 105dB is the coverage quality factor, i.e., the second quality factor.

[0120] In the third example, the coverage quality factor is determined by collecting data from indicators. The coverage quality factor is equal to the product of the access coverage quality factor and the connection coverage factor. The access coverage factor is the RRC connection establishment success rate during the access process, and the connection coverage factor is the proportion of handover and redirection caused by coverage in the connection state.

[0121] The connection coverage factor is calculated as follows:

[0122]

[0123] Among them, the number of NR to LTE switching requests and the total number of 5G to 4G redirections are triggered by data coverage.

[0124]

[0125] Specifically, by subtracting the ratio of the number of NR handover LTE requests triggered by data reasons and the total number of 5G to 4G redirections from 1 to the number of NR handover LTE requests triggered by data coverage and the total number of 5G to 4G redirections, the final connection coverage factor is obtained, thereby determining the coverage quality factor and obtaining the second quality factor.

[0126] The "Number of Successful NR to LTE Handovers" refers to the number of successful handovers between NR and LTE; this measures the effectiveness and performance of the handover between 5G and LTE. The "Total Number of 5G to 4G Redirects" refers to the total number of times user equipment was forced to switch to the 4G network due to various reasons, including but not limited to weak signal or network congestion. This metric measures the stability of the 5G network and the signal strength required for devices to switch to 4G. The total number of redirects to 4G reflects the coverage, signal quality, and network load of the 5G network. The "Total Number of Data Coverage Triggers 5G Interoperation to 4G" refers to the number of times user equipment automatically switched to the 4G network for data transmission in areas not covered by the 5G network.

[0127] S203. Based on performance data, determine the eMBB service type. When the main scenario is capacity-type, determine the service volume of the RLC layer and determine the first service output based on the service volume. Based on performance data, determine the eMBB service type. When the main scenario is coverage-type, determine the base station signal coverage area corresponding to the coverage type and determine the second service output based on the base station signal coverage area. Based on energy consumption data, determine the preset renewable energy usage time ratio of the device and determine the first effective energy consumption of the device based on the preset renewable energy usage time ratio.

[0128] In this embodiment, when the main scenario is capacity-type, the uplink and downlink traffic volumes of the RLC layer are obtained as the first service output; when the main scenario is coverage-type, the area within a certain signal coverage range of the base station is used as the second service output; wherein the first effective energy consumption is obtained by subtracting the proportion of renewable energy from the energy consumption of the device.

[0129] S204. When the service type is eMBB, determine the first energy efficiency based on the ratio of the product of the first quality factor and the first service output to the first effective energy consumption; determine the second energy efficiency based on the ratio of the product of the second quality factor and the second service output to the first effective energy consumption; determine the target energy efficiency corresponding to the eMBB service type based on the first energy efficiency and the second energy efficiency.

[0130] In the fourth example, the formula for calculating the first energy efficiency is:

[0131]

[0132] in, First energy efficiency For RLC layer uplink traffic, For the uplink retransmission rate of layer 1-RLC, For RLC layer downlink traffic, For the downlink retransmission rate of layer 1-RLC, As the first effective energy consumption, For the effective energy consumption of the equipment, The proportion of input time for renewable energy; among which and Combined to form the first quality factor, and This combination forms the primary business output.

[0133] The formula for calculating the second energy efficiency rating is:

[0134]

[0135] in, For the second energy efficiency, This is the second service output, namely the base station signal coverage area; This is the second quality factor, i.e., the coverage quality factor; As the first effective energy consumption, For the effective energy consumption of the equipment, The percentage of time spent inputting renewable energy.

[0136] The overall energy efficiency of the network in the eMBB service type is obtained by weighting the first energy efficiency and the second energy efficiency.

[0137] When the primary scenario is coverage, if there are multiple RATs at the same site, only the low-frequency band power consumption is calculated. In the fifth example, there are 2.1GHz and 900MHz 5G base stations at the same site. The low-frequency band can cover a longer distance, and the coverage is mainly provided by the power output of the 900MHz base station.

[0138] S205. Compare the average value of the first energy efficiency and the average value of the second energy efficiency of the eMBB service type in different wireless networks, and obtain the comparison results; and / or, determine the average value of the two first energy efficiency values ​​corresponding to the eMBB service type based on any two wireless networks, calculate the comparison coefficient based on the distribution of uplink and downlink traffic of the base stations corresponding to the two wireless networks, and perform a comparison calculation on the average value of the two first energy efficiency values ​​based on the comparison coefficient, and determine the comparison results; and / or, compare the unit energy efficiency of the average value of the first energy efficiency and the unit energy efficiency of the average value of the second energy efficiency of the eMBB service type in the same wireless network based on preset weights, and obtain the comparison results.

[0139] The formula for calculating the average value of the first energy efficiency rating and the average value of the second energy efficiency rating is:

[0140]

[0141] in, This is the average value for the first energy efficiency rating. The first energy efficiency is given when the i-th main scenario is capacity-based. This is the first business output when the i-th main scenario is capacity-based; This is the average value for the second energy efficiency rating. The second energy efficiency is when the i-th main scene is coverage-type. This is the second business output when the i-th main scenario is coverage-type.

[0142] In the sixth example, the average value of two first energy efficiency values ​​corresponding to the eMBB service type is determined based on any two wireless network networks. A comparison coefficient is calculated based on the distribution of uplink and downlink traffic of the base stations corresponding to the two wireless network networks. The average value of the two first energy efficiency values ​​is then compared based on the comparison coefficient, and the comparison result can be:

[0143] Select the energy consumption of a base station during a certain period of time when all traffic is uplink and / or uplink traffic accounts for more than 80%. Energy consumption that is all downlink traffic and / or downlink traffic accounts for more than 80% ; Calculate the ratio .

[0144] in:

[0145] Uplink traffic share = RLC layer uplink traffic / (RLC layer uplink traffic + RLC layer downlink traffic)

[0146] Downlink traffic share = RLC layer downlink traffic / (RLC layer uplink traffic + RLC layer downlink traffic).

[0147] In the sixth example, the downlink traffic proportions of the two wireless networks are respectively those of the first wireless network. Second wireless network Since both wireless networks belong to the eMBB service type and their main scenario is capacity-based, the average first energy efficiency of the two wireless networks are respectively the first wireless network's... Second wireless network Based on the first wireless network, the average energy efficiency of the second wireless network is uniformly adjusted using the following formula:

[0148]

[0149] The average value of the first energy efficiency of the adjusted second wireless network is compared with the average value of the first energy efficiency of the first wireless network, and the comparison results are obtained.

[0150] in, This is the ratio of energy consumption for uplink traffic to energy consumption for downlink traffic. This represents the percentage of downlink traffic on the first wireless network. This represents the percentage of downlink traffic from the second wireless network. This represents the average energy efficiency of the second wireless network.

[0151] In the seventh example, the unit energy efficiency of the average first energy efficiency and the unit energy efficiency of the average second energy efficiency of the eMBB service type in the same wireless network are compared based on preset weights, and the comparison results are obtained as follows:

[0152] Obtain the average value of the first energy efficiency of the wireless network. The average value of the second energy efficiency The units are GB / kWh and km, respectively. 2 / Kwh Since it's impossible to form an overall energy efficiency benchmarking analysis within the same network, the derivatives are used to represent per GB and per km respectively. 2 The energy consumed is as follows:

[0153] ,

[0154] Use preset weight values The comparison is performed using the following formula:

[0155]

[0156] Among them, Energy consumption per GB and per km, respectively 2 Energy consumption weighting value For the comparison results of the calculation, This is the average value for the first energy efficiency rating. This is the average value for the second energy efficiency rating.

[0157] S206. When the service type is uRLLC, determine the third quality factor corresponding to the uRLLC service type; determine the network reliability and end-to-end latency corresponding to the uRLLC service type based on performance data, and determine the third service output corresponding to the uRLLC service type; determine the slice energy consumption corresponding to the uRLLC service type based on the network slicing method of the uRLLC service type, and determine the corresponding second effective energy consumption based on the slice energy consumption; determine the ratio of the product of the third quality factor and the third service output to the second effective energy consumption as the third energy efficiency, and determine the third energy efficiency as the target energy efficiency corresponding to the uRLLC service type.

[0158] In this embodiment, the main performance characteristics of the uRLLC service type are end-to-end latency and network reliability. The third service output, determined based on network reliability and end-to-end latency, is: network reliability / end-to-end latency, expressed as: / T; and since network reliability and end-to-end latency are both actual measured values, the third quality factor is 1.

[0159] In this embodiment, network slicing methods can be divided into three forms: 5QI, RB resource reservation, and carrier isolation; 5QI-based slicing is based on the proportion of slice traffic. Multiply by the equivalent slice energy consumption of the equipment energy consumption, based on the RB resource reservation, according to the proportion of reserved RB to total RB. Multiply by the device's energy consumption equivalent slice energy consumption, based on carrier isolation according to bandwidth ratio. Multiply by the equipment energy consumption equivalent to the slice energy consumption.

[0160] In this embodiment, the formula for calculating the slice energy efficiency of uRLLC is:

[0161]

[0162] in, This refers to the energy efficiency of the slice, also known as the third energy efficiency. / T is the third service output. As the first effective energy consumption, For the effective energy consumption of the equipment, The percentage of time spent using renewable energy; The energy consumption of a slice based on 5QI slices. For slice energy consumption based on RB resource reserved slices, The slice energy consumption is based on carrier isolation slicing, where slice energy consumption is the second effective energy consumption.

[0163] S207. When the service type is mMTC, determine the fourth quality factor corresponding to the mMTC service type; determine the number of supported connections corresponding to the mMTC service type based on performance data, and determine the fourth service output corresponding to the mMTC service type; determine the slice energy consumption corresponding to the mMTC service type based on the network slicing method of the mMTC service type, and determine the corresponding third effective energy consumption based on the slice energy consumption; determine the ratio of the product of the fourth quality factor and the fourth service output to the third effective energy consumption as the fourth energy efficiency, and determine the fourth energy efficiency as the target energy efficiency corresponding to the mMTC service type.

[0164] In this embodiment, the main characteristic of the mMTC service type is the number of supported connections. The number of supported connections is presented as the fourth service output, represented as follows: Since the number of supported connections is the actual measured value, the fourth quality factor corresponding to the mMTC service type is 1.

[0165] In this embodiment, the energy efficiency of mMTC service types is also calculated based on three different network slicing methods, and the calculation formula is as follows:

[0166]

[0167] in, The energy efficiency of the slice for mMTC services, i.e., the fourth energy efficiency; As the fourth business output, As the first effective energy consumption, For the effective energy consumption of the equipment, The percentage of time spent using renewable energy; The energy consumption of a slice based on 5QI slices. For RB-based slice energy consumption, The slice energy consumption is based on carrier isolation slicing, where slice energy consumption is the second effective energy consumption.

[0168] S208. Based on the preset planning data, determine the planning business output, planning quality factor and planning effective energy consumption, and calculate the predicted energy efficiency corresponding to the planning business output, planning quality factor and planning effective energy consumption based on the preset energy efficiency formula; perform fitting calculation based on the preset planning data and predicted energy efficiency, and determine the fitting curve; predict and determine the predicted business output based on the fitting curve, and calculate and determine the predicted energy consumption based on the predicted business output.

[0169] In this embodiment, preset planning data is determined by collecting network environment, device deployment type and configuration, service distribution and type, energy consumption indicators, and planned usage data. Based on the preset planning data, planned service output, planning quality factor, and planned effective energy consumption are determined. Predicted energy efficiency is calculated and determined using a preset energy efficiency formula. The preset energy efficiency formula is: Planned Service Output... Planning quality factor / Planning effective energy consumption.

[0170] like Figure 4 As shown in the eighth example, the preset planning data includes network environment factors such as population density, planning scenario, climate, and terrain; equipment deployment types and configurations such as macro / micro, indoor distributed systems, channels, frequency bands, manufacturers, and energy saving; and service distribution and types such as service characteristics, service assurance, load, distribution, and user distribution. Based on the preset planning data, the energy efficiency of a single site is determined. Three-dimensional clustering is used to form a fitted curve relating energy efficiency to specific network environments, specific equipment types and configurations, and specific service distributions and types. New site plans are made, and the service output of the planned sites is predicted, where service output can be either service volume or coverage area. The corresponding fitted energy efficiency curve function is found by combining the planned site attributes, where planned site attributes can be site environment, configuration, and service type. Based on the fitted energy efficiency curve function, the predicted service output is determined, allowing for accurate calculation of the predicted energy consumption after the base station is built. Simultaneously, an overall prediction of future wireless network energy efficiency and energy consumption trends can be made, including 5G energy consumption costs and energy usage.

[0171] Population density can be determined based on region type, including: urban areas, county towns, suburbs, and rural areas; planning scenarios include, but are not limited to: campuses, hospitals, and transportation hubs; climate is defined by season, including spring, summer, autumn, and winter, or can be determined based on the climate range of the geographical location; terrain includes, but is not limited to, mountains, plains, or plateaus. In terms of equipment deployment types and configurations, macro and micro include: macro base stations, micro base stations, and indoor distributed antenna systems (DAS); indoor DAS can be: In-Building Distributed Antenna System (DAS) or Distributed Indoor System (DIS); channels, i.e., wireless communication channels, can be divided into: 4TR, 32TR, and 64TR channels; frequency bands can be: 900M, 2.1G, or 3.5G; manufacturers are determined based on actual planning data; energy-saving types include: carrier shutdown type and symbol shutdown type. In terms of service distribution and types, services can be categorized into two main types: primarily uplink services and primarily downlink services. Service guarantees include: Quality of Service (QoS) in 5G networks, i.e., 5QI, which includes two different QoS categories: Guaranteed Bit Rate (GBR) and Non-Guaranteed Bit Rate (non-GBR). Load can be measured by the utilization efficiency of Physical Resource Blocks (PRBs). Distribution type can be 24-hour distribution. User distribution is determined based on the distance of users.

[0172] This application provides a method for evaluating the energy efficiency of a wireless network. The method includes: acquiring network performance data of a base station, energy consumption data of a device, and the service type corresponding to the wireless network service; determining the corresponding quality factor based on the service type, determining the service output based on the performance data, and determining the effective energy consumption of the device based on the energy consumption data; and determining the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption. Energy efficiency calculations are performed for three different service types. For eMBB services, quality factors and service outputs are determined for each application's primary scenario, and energy efficiency is calculated for both capacity and coverage types. Average energy efficiency values ​​are then calculated, and energy efficiency comparisons are achieved by comparing these average values ​​across different wireless networks. Simultaneously, when calculating effective energy consumption, the proportion of renewable energy input time in device energy consumption is obtained, and renewable energy is excluded from the calculation, resulting in more accurate device effective energy consumption and eliminating the impact of renewable energy on energy efficiency assessment, thus increasing the accuracy of energy efficiency evaluation. For uRLLC and mMTC services, slice energy consumption is calculated based on different network slicing methods to ensure the accuracy of effective energy consumption. By considering quality factors for various service types applied to wireless network construction, and performing energy efficiency assessments based on the quality factors, corresponding service outputs, and effective energy consumption of different service types, the technical effect of improving the accuracy of energy efficiency assessment is achieved, thereby solving the technical problem of low accuracy in existing energy efficiency assessment technologies.

[0173] Figure 5 This is a schematic diagram of the structure of a wireless network energy efficiency evaluation device provided in an embodiment of this application. The device in this embodiment can be in the form of software and / or hardware. For example... Figure 5 As shown in the figure, a wireless network energy efficiency assessment device 500 provided in this application embodiment includes: an acquisition module 501, a processing module 502, and a determination module 503.

[0174] The acquisition module 501 is used to acquire network performance data of the base station, energy consumption data of the device, and service type corresponding to the wireless network service;

[0175] Processing module 502 is used to determine the corresponding quality factor based on the service type, determine the service output based on performance data, and determine the effective energy consumption of the equipment based on energy consumption data.

[0176] The determination module 503 is used to determine the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption.

[0177] In one possible implementation, the device is also used for:

[0178] Determine whether the service type is eMBB; if so, determine the corresponding main scenario type and quality factor based on the wireless network service.

[0179] Determine business outputs based on main scenario type, quality factors, and performance data;

[0180] The preset renewable energy usage time ratio of the equipment is determined based on energy consumption data, and the first effective energy consumption of the equipment is determined based on the preset renewable energy usage time ratio.

[0181] In one possible implementation, the device is also used for:

[0182] When the main scenario type is determined to be capacity-type based on wireless network services, the corresponding first quality factor is determined based on the uplink retransmission rate and downlink retransmission rate of the RLC layer corresponding to the eMBB service type.

[0183] When the main scenario type is determined to be coverage type based on wireless network services, the method for obtaining the coverage quality factor of the eMBB service type is obtained, and the second quality factor corresponding to the coverage type is determined based on the method of obtaining the coverage quality factor.

[0184] In one possible implementation, the device is also used for:

[0185] Based on performance data, the eMBB service type is determined. When the main scenario is capacity-based, the service volume of the RLC layer is determined, and the first service output is determined based on the service volume.

[0186] Based on performance data, determine the eMBB service type when the main scenario is coverage type, the corresponding base station signal coverage area, and determine the second service output based on the base station signal coverage area.

[0187] In one possible implementation, the device is also used for:

[0188] When the service type is uRLLC, determine the third quality factor corresponding to the uRLLC service type;

[0189] Based on performance data, determine the network reliability and end-to-end latency corresponding to the uRLLC service type, and determine the third service output corresponding to the uRLLC service type;

[0190] The network slicing method based on uRLLC service type determines the slice energy consumption corresponding to uRLLC service type, and the corresponding second effective energy consumption is determined based on the slice energy consumption.

[0191] In one possible implementation, the device is also used for:

[0192] When the service type is mMTC, determine the fourth quality factor corresponding to the mMTC service type;

[0193] The number of supported connections corresponding to each mMTC service type is determined based on performance data, and the fourth service output corresponding to each mMTC service type is also determined.

[0194] The network slicing method based on mMTC service type determines the slice energy consumption corresponding to the mMTC service type, and the corresponding third effective energy consumption is determined based on the slice energy consumption.

[0195] In one possible implementation, the device is also used for:

[0196] When the service type is eMBB, the first energy efficiency is determined based on the ratio of the product of the first quality factor and the first service output to the first effective energy consumption.

[0197] The second energy efficiency is determined based on the ratio of the product of the second quality factor and the second service output to the first effective energy consumption.

[0198] The target energy efficiency corresponding to the eMBB service type is determined based on the first energy efficiency and the second energy efficiency.

[0199] When the service type is uRLLC, the ratio of the product of the third quality factor and the third service output to the second effective energy consumption is determined as the third energy efficiency, and the third energy efficiency is determined as the target energy efficiency corresponding to the uRLLC service type.

[0200] When the service type is mMTC, the ratio of the product of the fourth quality factor and the fourth service output to the third effective energy consumption is determined as the fourth energy efficiency, and the fourth energy efficiency is determined as the target energy efficiency corresponding to the mMTC service type.

[0201] In one possible implementation, the device is also used for:

[0202] Compare the average first energy efficiency and the average second energy efficiency of eMBB service types in different wireless networks, and obtain the comparison results;

[0203] The average value of two first energy efficiency values ​​corresponding to the eMBB service type is determined based on any two wireless networks. The comparison coefficient is calculated based on the distribution of uplink and downlink traffic of the base stations corresponding to the two wireless networks. The average value of the two first energy efficiency values ​​is compared and calculated based on the comparison coefficient, and the comparison result is determined.

[0204] The unit energy efficiency of the first energy efficiency and the unit energy efficiency of the second energy efficiency are compared based on the preset weights of the eMBB service types in the same wireless network, and the comparison results are obtained.

[0205] In one possible implementation, the device is also used for:

[0206] Based on the preset planning data, the planning business output, planning quality factor and planning effective energy consumption are determined, and the predicted energy efficiency corresponding to the planning business output, planning quality factor and planning effective energy consumption is calculated based on the preset energy efficiency formula.

[0207] The fitting calculation is performed based on the preset planning data and predicted energy efficiency, and the fitting curve is determined.

[0208] Based on the fitted curve, the predicted service output is predicted and determined. Based on the predicted service output, the predicted energy consumption is calculated and determined.

[0209] This application provides a wireless network energy efficiency assessment device. The device includes: an acquisition module for acquiring network performance data of a base station, energy consumption data of the device, and service types corresponding to wireless network services; a processing module for determining the corresponding quality factor based on the service type, determining the service output based on the performance data, and determining the effective energy consumption of the device based on the energy consumption data; and a determination module for determining the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption. Energy efficiency calculations are performed for three different service types. For eMBB services, quality factors and service outputs are determined for each application's primary scenario, and energy efficiency is calculated for both capacity and coverage types. Average energy efficiency values ​​are then calculated, and energy efficiency comparisons are achieved by comparing these average values ​​across different wireless networks. Simultaneously, when calculating effective energy consumption, the proportion of renewable energy input time in device energy consumption is obtained, and renewable energy is excluded from the calculation, resulting in more accurate device effective energy consumption and eliminating the impact of renewable energy on energy efficiency assessment, thus increasing the accuracy of energy efficiency evaluation. For uRLLC and mMTC services, slice energy consumption is calculated based on different network slicing methods to ensure the accuracy of effective energy consumption. By considering quality factors for various service types applied to wireless network construction, and performing energy efficiency assessments based on the quality factors, corresponding service outputs, and effective energy consumption of different service types, the technical effect of improving the accuracy of energy efficiency assessment is achieved, thereby solving the technical problem of low accuracy in existing energy efficiency assessment technologies.

[0210] Figure 6 This is a hardware structure diagram of the wireless network energy efficiency evaluation device provided in an embodiment of this application. Figure 6 As shown, the wireless network energy efficiency assessment device 600 includes:

[0211] Processor 601 and memory 602;

[0212] The memory stores the instructions that the computer executes;

[0213] The processor executes the computer execution instructions stored in memory 602, causing the wireless network energy efficiency assessment device to perform the wireless network energy efficiency assessment method described above.

[0214] It should be understood that the processor 601 described above can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The memory 602 may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0215] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement a wireless network energy efficiency evaluation method.

[0216] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0217] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0218] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0219] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0220] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0221] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0222] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0223] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0224] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for evaluating the energy efficiency of a wireless network, characterized in that, include: The network performance data of the base station, the energy consumption data of the device, and the service type corresponding to the wireless network service are obtained. The service type includes eMBB service type, uRLLC service type, or mMTC service type. The corresponding quality factor is determined based on the service type, the service output is determined based on the performance data, and the effective energy consumption of the device is determined based on the energy consumption data. The target energy efficiency corresponding to the service type is determined based on the ratio of the product of the quality factor and the service output to the effective energy consumption. The steps of determining the corresponding quality factor based on the service type, determining the service output based on the performance data, and determining the effective energy consumption of the device based on the energy consumption data include: When the service type is eMBB service type, the corresponding main scenario type is determined based on the wireless network service, and the main scenario type is either capacity type or coverage type. When the main scenario type is capacity-type, a first quality factor is determined based on the uplink retransmission rate and downlink retransmission rate of the RLC layer corresponding to the eMBB service type, and the service volume of the RLC layer is determined as the first service output based on the performance data; when the main scenario type is coverage-type, a second quality factor is determined based on the coverage quality factor acquisition method, and the base station signal coverage area is determined as the second service output based on the performance data. Based on the energy consumption data, the preset renewable energy usage time ratio of the device is determined, and based on the preset renewable energy usage time ratio, the first effective energy consumption of the device is determined; When the service type is uRLLC service type, determine the third quality factor corresponding to the uRLLC service type, determine the network reliability and end-to-end latency corresponding to the uRLLC service type based on the performance data to determine the third service output, and determine the slice energy consumption based on the network slicing method of the uRLLC service type to determine the second effective energy consumption; When the service type is mMTC service type, determine the fourth quality factor corresponding to the mMTC service type, determine the number of supported connections corresponding to the mMTC service type based on the performance data to determine the fourth service output, and determine the slice energy consumption based on the network slicing method of the mMTC service type to determine the third effective energy consumption.

2. The method according to claim 1, characterized in that, Determining the energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption includes: When the service type is the eMBB service type, the first energy efficiency is determined based on the ratio of the product of the first quality factor and the first service output to the first effective energy consumption; The second energy efficiency is determined based on the ratio of the product of the second quality factor and the second service output to the first effective energy consumption. The target energy efficiency corresponding to the eMBB service type is determined based on the first energy efficiency and the second energy efficiency. When the service type is the uRLLC service type, the ratio of the product of the third quality factor and the third service output to the second effective energy consumption is determined as the third energy efficiency, and the third energy efficiency is determined as the target energy efficiency corresponding to the uRLLC service type; When the service type is the mMTC service type, the ratio of the product of the fourth quality factor and the fourth service output to the third effective energy consumption is determined as the fourth energy efficiency, and the fourth energy efficiency is determined as the target energy efficiency corresponding to the mMTC service type.

3. The method according to claim 2, characterized in that, After determining the target energy efficiency corresponding to the eMBB service type based on the first energy efficiency and the second energy efficiency, the method further includes any one of the following: Compare the average value of the first energy efficiency and the average value of the second energy efficiency of the eMBB service type in different wireless networks, and obtain the comparison results; The average value of the two first energy efficiency values ​​corresponding to the eMBB service type is determined based on any two wireless network networks. A comparison coefficient is calculated based on the distribution of uplink and downlink traffic of the base stations corresponding to the two wireless network networks. The average value of the two first energy efficiency values ​​is compared based on the comparison coefficient, and the comparison result is determined. The unit energy efficiency of the first energy efficiency average value and the unit energy efficiency of the second energy efficiency average value of the eMBB service type in the same wireless network are compared based on preset weights, and the comparison result is obtained.

4. The method according to claim 1, characterized in that, After determining the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption, the method further includes: Based on preset planning data, the planning business output, planning quality factor and planning effective energy consumption are determined, and the predicted energy efficiency corresponding to the planning business output, the planning quality factor and the planning effective energy consumption is calculated based on preset energy efficiency formula; Based on the preset planning data and the predicted energy efficiency, a fitting calculation is performed, and the fitting curve is determined; Based on the fitted curve, a prediction is made and the predicted service output is determined. Based on the predicted service output, the predicted energy consumption is calculated and determined.

5. A wireless network energy efficiency assessment device, characterized in that, include: The acquisition module is used to acquire network performance data of the base station, energy consumption data of the device, and service types corresponding to wireless network services, wherein the service types include eMBB service type, uRLLC service type, or mMTC service type. The processing module is used to determine the corresponding quality factor based on the service type, determine the service output based on the performance data, and determine the effective energy consumption of the device based on the energy consumption data. The determination module is used to determine the target energy efficiency corresponding to the service type based on the ratio of the product of the quality factor and the service output to the effective energy consumption; The processing module is specifically used to determine the corresponding main scenario type based on the wireless network service when the service type is eMBB service type, wherein the main scenario type is capacity type or coverage type. When the main scenario type is capacity-type, a first quality factor is determined based on the uplink retransmission rate and downlink retransmission rate of the RLC layer corresponding to the eMBB service type, and the service volume of the RLC layer is determined as the first service output based on the performance data; when the main scenario type is coverage-type, a second quality factor is determined based on the coverage quality factor acquisition method, and the base station signal coverage area is determined as the second service output based on the performance data. Based on the energy consumption data, the preset renewable energy usage time ratio of the device is determined, and based on the preset renewable energy usage time ratio, the first effective energy consumption of the device is determined; When the service type is uRLLC service type, determine the third quality factor corresponding to the uRLLC service type, determine the network reliability and end-to-end latency corresponding to the uRLLC service type based on the performance data to determine the third service output, and determine the slice energy consumption based on the network slicing method of the uRLLC service type to determine the second effective energy consumption; When the service type is mMTC service type, determine the fourth quality factor corresponding to the mMTC service type, determine the number of supported connections corresponding to the mMTC service type based on the performance data to determine the fourth service output, and determine the slice energy consumption based on the network slicing method of the mMTC service type to determine the third effective energy consumption.

6. A wireless network energy efficiency assessment device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the wireless network energy efficiency assessment method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the wireless network energy efficiency assessment method as described in any one of claims 1 to 4.

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