Base station power optimization method and system, computer storage medium and electronic equipment

Abstract

The invention provides a base station power optimization method and system, a computer storage medium and electronic equipment, and relates to the technical field of wireless communication optimizing.Signal intensity and receiving power of a receiving end in a service area of a target base station are measured, a signal coverage index is generated, and signal intensity and channel quality indication of an interference area are combined, so that the power of the target base station is optimized. And calculating a signal quality index. Meanwhile, the base station resource utilization rate, the number of connected user equipment and the equipment operation power are counted, and an equipment load index is generated; constructing a prediction model, and predicting reference signal received power, received signal strength, inter-region interference ratio, channel quality indication, resource utilization rate, user equipment number and base station equipment operation power based on the base station power; and calculating a target function value by using the signal comprehensive evaluation index and the equipment load index, and optimizing the power of the base station by using a simulated annealing algorithm by using the maximum target function value as a target, thereby balancing the equipment load while improving the signal coverage and quality.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication optimization, and in particular to a base station power optimization method, system, computer storage medium and electronic equipment. Background Art

[0002] In mobile communication networks, base station power control is one of the important factors affecting network performance. The transmission power of base stations directly affects signal coverage, signal quality, and interference between users. Traditional power control methods usually rely on fixed parameter settings or simple power gain adjustments, and lack the ability to adapt to real-time dynamic changes in the network, resulting in problems such as insufficient signal coverage, significant signal interference, and low resource utilization, which in turn affects user experience and overall network performance. In addition, with the rapid growth in the number of mobile devices, base station load and energy consumption are gradually increasing. How to reduce energy consumption while ensuring network performance has become an important research direction. Therefore, it is urgent to propose a dynamic base station power optimization method based on real-time network status to balance base station load and energy consumption while improving signal range and quality.

[0003] In the prior art, publication number CN117349993A discloses a base station power optimization method, device, computer storage medium and electronic device, which inputs the base station transmit power into a wireless signal distribution model to generate corresponding regional signal strength data, and inputs the base station transmit power, regional signal strength data and other communication scenario parameters into a multi-objective optimization algorithm, and obtains multiple Pareto solutions based on the optimization goals of maximizing the regional signal strength data and minimizing the comprehensive base station transmit power, selects the most suitable Pareto solution as a result and sends it to the base station, and the base station adjusts the current power according to the result.

[0004] The main problem with the above method is that it only focuses on the strength of regional signals, while ignoring other parameters such as signal interference and equipment load. The optimization goal is relatively single, resulting in insufficient consideration of the final base station power optimization.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a base station power optimization method, system, computer storage medium and electronic equipment to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A base station power optimization method, the specific steps comprising:

[0009] Step 1: Select multiple measurement time periods, select a target receiving area served by a target base station for each measurement time period, obtain a reference signal received power and a received signal strength according to the reporting information of a receiving end in the target receiving area, and generate a signal coverage index based on the reference signal received power and the received signal strength, wherein the receiving end is a user equipment connected to the target base station in the target receiving area;

[0010] Step 2: Select other areas that reuse the same frequency as the target receiving area within the service range of the target base station and set them as interference areas. Obtain the received signal strengths of all interference areas to generate interference strengths. Generate an inter-area interference ratio based on the interference strength and the received signal strength of the target area. Obtain a channel quality indicator based on the receiving end reporting information of the target area. Generate a signal quality index based on the inter-area interference ratio and the channel quality indicator.

[0011] Step 3: Count the total number of physical resource blocks of the target base station, and collect the number of physical resource blocks allocated in the base station during the measurement period to generate resource utilization. At the same time, obtain the number of user equipment using base station resources for data transmission during the measurement period and the base station equipment operating power, and generate the equipment load index based on the resource utilization, the number of user equipment and the base station equipment operating power;

[0012] Step 4: Build a model based on the deep learning network, take base station power as input, reference signal received power and received signal strength as labels, build a signal coverage prediction model, take base station power as input, inter-area interference ratio and channel quality indication as labels, build a signal quality prediction model, take base station power as input, resource utilization, number of user devices and base station equipment operating power as labels, build a load prediction model;

[0013] Step 5: Based on the signal coverage index, signal quality index and equipment load index, the objective function value of the base station power is calculated. Taking the maximization of the objective function value as the optimization goal, the simulated annealing algorithm and the prediction model are combined to optimize the current power of the target base station.

[0014] Furthermore, the formula for generating the signal coverage index is:

[0015]

[0016] in, represents the signal coverage index, represents the weight coefficient of the reference signal received power, represents the scale factor, represents the reference signal power, Represents the hyperbolic tangent function Normalize it, Represents the weight coefficient of the received signal strength, represents the scaling factor, Indicates the received signal strength. Indicates logarithmic smoothing of the received signal strength. ,and .

[0017] Furthermore, the principle for generating the signal quality index is:

[0018] The formula used to generate the inter-area interference ratio is:

[0019]

[0020]

[0021] in, represents the inter-area interference ratio, represents the interference intensity, Indicates Interference ratio between interference areas, Indicates The received signal strength of each interference area, represents the index of the interference region, and , Indicates the number of interference areas;

[0022] The formula used to generate the signal quality index is:

[0023]

[0024] in, represents the signal quality index, represents the weight coefficient of the inter-region interference ratio, Indicates the maximum inter-area interference ratio among all interference areas within the target base station service range, represents the weight coefficient of the channel quality indicator, Indicates the channel quality indicator. ,and .

[0025] Furthermore, the principle for generating the equipment load index is as follows:

[0026]

[0027] in, Indicates the equipment load index, Indicates the number of allocated physical resource blocks, Indicates the total number of physical resource blocks, Indicates the number of user equipment using base station resources for data transmission during the measurement period. Indicates the maximum number of user equipment that the base station can support. Indicates the operating power of the base station equipment during the measurement period. Indicates the maximum operating power of the base station equipment. They represent the weight coefficients of the number of physical resource blocks, the number of user equipment and the operating power of the base station equipment respectively. and .

[0028] Furthermore, the principle for generating the objective function value is:

[0029] The formula for generating the comprehensive evaluation index of the signal is:

[0030]

[0031] in, represents the comprehensive evaluation index of the signal, represents the signal coverage index, represents the signal quality index, They represent the weight coefficients of signal coverage index, signal quality index and the interaction term between signal coverage index and signal quality index respectively. ,and ;

[0032] The formula used to generate the objective function value is:

[0033]

[0034] in, represents the objective function value, Indicates the equipment load index.

[0035] Furthermore, the principle for optimizing base station power is as follows:

[0036] Base station power range As a constraint, the base station power is optimized, where Indicates the minimum power of the base station. Represents the maximum power of the base station. When the base station power is optimized based on the simulated annealing algorithm, the base station power before optimization is calibrated as The corresponding annealing temperature is , the objective function value is , the formula for optimizing base station power is:

[0037]

[0038] in, represents the optimized base station power, represents the adjustment factor, and , Indicates the adjustment step size;

[0039] calculate Corresponding ,when When directly accepting ;

[0040] when When the probability Accept new interpretations;

[0041] After each new solution is calculated, the annealing temperature is reduced. The formula for reducing the annealing temperature is:

[0042]

[0043] in, represents the reduced annealing temperature after optimization, represents the attenuation coefficient, and ;

[0044] When the annealing temperature drops to When the value is below , the calculation is stopped and the current solution is output. The corresponding base station power is the optimized base station power.

[0045] The present invention also provides a base station power optimization system, which is used to execute the above base station power optimization method, specifically comprising:

[0046] A signal measurement module is used to select multiple measurement time periods, select a target receiving area served by a target base station for each measurement time period, obtain a reference signal received power and a received signal strength according to information reported by a receiving end in the target receiving area, and generate a signal coverage index based on the reference signal received power and the received signal strength, wherein the receiving end is a user equipment connected to the target base station in the target receiving area;

[0047] An interference calculation module is used to select other areas with the same frequency reuse distance as the target receiving area within the service range of the target base station, set them as interference areas, obtain the received signal strengths of all interference areas to generate interference strengths, generate inter-area interference ratios based on the interference strengths and the received signal strengths of the target area, obtain channel quality indicators based on the reporting information of the receiving end of the target area, and generate signal quality indexes based on the inter-area interference ratios and the channel quality indicators;

[0048] A load measurement module is used to count the total number of physical resource blocks of the target base station, collect the number of physical resource blocks allocated in the base station during the measurement period, generate resource utilization, and obtain the number of user devices that use base station resources for data transmission during the measurement period and the base station equipment operating power, and generate a device load index based on the resource utilization, the number of user devices and the base station equipment operating power;

[0049] A model building module is used to build a model based on a deep learning network, using base station power as input, reference signal received power and received signal strength as labels to build a signal coverage prediction model, using base station power as input, inter-area interference ratio and channel quality indication as labels to build a signal quality prediction model, and using base station power as input, resource utilization, number of user devices and base station equipment operating power as labels to build a load prediction model;

[0050] The comprehensive optimization module is used to calculate the objective function value of the base station power based on the signal coverage index, signal quality index and equipment load index, and optimize the current power of the target base station by maximizing the objective function value, combining the simulated annealing algorithm and the prediction model.

[0051] The present invention also provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is running, the computer storage medium is controlled to execute the above-mentioned base station power optimization method.

[0052] The present invention also provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein the memory is used to store computer programs or instructions, and the processor is used to execute the computer program or instructions so that the electronic device implements the above-mentioned base station power optimization method.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention uses the reference signal received power and received signal strength reported by the user equipment in the target receiving area to accurately evaluate the signal coverage of the target base station, and evaluates the current signal effect from the signal coverage; selects the interference area, and based on the signal strength of the target area combined with the inter-area interference ratio, accurately quantifies the influence of interference on the signal quality of the target area, generates a signal quality index from the two dimensions of interference strength and channel quality, and evaluates the current signal effect from the signal strength, comprehensively considering the physical layer status of the link and the actual perception of the user, which is more comprehensive than a single indicator, and generates a signal comprehensive evaluation index based on the signal coverage index and the signal quality index, which reflects the signal coverage breadth on the one hand and the signal quality and strength on the other hand, avoids the one-sidedness of a single indicator, achieves a balance between coverage and signal strength, more accurately reflects the global performance of the signal, and makes the optimization result closer to the actual situation.

[0055] The present invention also generates an objective function value by combining the signal comprehensive evaluation index and the equipment load, thereby achieving a global balance between signal performance and network load, avoiding resource waste or performance degradation caused by optimizing only one aspect, and using a simulated annealing algorithm to quickly optimize the base station power configuration and balance multi-dimensional performance indicators. The optimization effect is more significant and comprehensive, and is more suitable for complex actual communication environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of a method flow of an embodiment of the present invention;

[0057] Figure 2 Schematic diagram of system modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0059] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0060] Example:

[0061] See also Figure 1 , the present invention provides a technical solution:

[0062] A base station power optimization method, the specific steps comprising:

[0063] Step 1: Select multiple measurement time periods, select a target receiving area served by a target base station for each measurement time period, obtain a reference signal received power and a received signal strength according to the reporting information of a receiving end in the target receiving area, and generate a signal coverage index based on the reference signal received power and the received signal strength, wherein the receiving end is a user equipment connected to the target base station in the target receiving area;

[0064] In this embodiment, the formula for generating the signal coverage index is:

[0065]

[0066] in, represents the signal coverage index, represents the weight coefficient of the reference signal received power, represents the scale factor, represents the reference signal power, Represents the hyperbolic tangent function Normalize it, Represents the weight coefficient of the received signal strength, represents the scaling factor, Indicates the received signal strength. Indicates logarithmic smoothing of the received signal strength. ,and .

[0067] The signal coverage index reflects the performance of the signal sent by the base station within the coverage range. The higher the signal coverage index, the wider the signal coverage range and the higher the signal quality. The reference signal received power is the main indicator of the signal coverage index. It is the power of the signal sent by the base station received by the receiving user equipment. Different receiving user equipments receive different reference signal powers. The average reference signal power received by all user equipments in the target receiving area is taken as , obtained by extracting the reported information from the receiving end, reflecting the coverage range of the base station signal strength; performing nonlinear transformation on the reference signal receiving power, and normalizing the range to The formula for normalization by the hyperbolic tangent function is:

[0068]

[0069] in, Represents the scale factor, ranging from interval, The larger the value of right The more sensitive the change, The smaller the value of right The smoother the change of ;

[0070] The received signal strength is a secondary indicator that reflects the strength of all signals received by the receiving end user equipment, including the signal sent by the base station and the noise. Different receiving end user equipment receives different signal strengths. The average received signal strength of all user equipment in the target receiving area is taken as On the one hand, it reflects the coverage of the signal, and indirectly reflects environmental factors such as noise interference. It is a supplement to the received power of the reference signal. The data range of the received signal strength is relatively wide, so it is compressed through logarithmic transformation to avoid high-value areas covering low-value areas. The scaling factor Used to adjust the influence range of the received signal strength. , to avoid the data gradient being too gentle. The weight coefficients of the reference signal received power and the received signal strength are: , .

[0071] Step 2: Select other areas that reuse the same frequency as the target receiving area within the service range of the target base station and set them as interference areas. Obtain the received signal strengths of all interference areas to generate interference strengths. Generate an inter-area interference ratio based on the interference strength and the received signal strength of the target area. Obtain a channel quality indicator based on the receiving end reporting information of the target area. Generate a signal quality index based on the inter-area interference ratio and the channel quality indicator.

[0072] In this embodiment, the principle for generating the signal quality index is:

[0073] The formula used to generate the inter-area interference ratio is:

[0074]

[0075]

[0076] in, represents the inter-area interference ratio, represents the interference intensity, Indicates Interference ratio between interference areas, Indicates The received signal strength of each interference area, represents the index of the interference region, and , Indicates the number of interference areas;

[0077] When two areas reuse the same frequency, their signals will be superimposed at the receiving end, causing interference. Specifically, the receiving end cannot distinguish the signals of the target receiving area and the interference area and cannot correctly decode the signals. When the areas use different frequencies, their signals are isolated from each other in frequency and will not directly cause interference. The interference at this time is manifested as noise. The sum of the received signal strengths of the interference areas is the total interference strength caused to the target area. The inter-area interference ratio is proportional to the received signal strength of the interference area and inversely proportional to the received signal strength of the target area.

[0078] The formula used to generate the signal quality index is:

[0079]

[0080] in, represents the signal quality index, represents the weight coefficient of the inter-region interference ratio, Indicates the maximum inter-area interference ratio among all interference areas within the target base station service range, that is, the maximum , represents the weight coefficient of the channel quality indicator, Indicates the channel quality indicator. ,and .

[0081] The signal quality index reflects the transmission quality of the signal under the influence of noise and channel quality. The higher the signal quality index, the better the signal transmission effect. The inter-regional interference ratio reflects the impact of interference on the signal quality. It is used to measure the relative strength between the signal in the target receiving area and the signal in the surrounding interference area. The smaller the inter-regional interference ratio, the smaller the negative impact on the signal. When the inter-regional interference ratio approaches the maximum inter-regional interference ratio, the negative impact on the signal approaches the maximum value. The channel quality indication is used to reflect the communication quality of the channel. It is reported by the user equipment to the base station according to the current channel conditions. The higher the channel quality indication, the stronger the communication capability of the current channel and the better the signal transmission quality. When the channel quality is poor, a small improvement in the channel quality will significantly improve the signal quality. When the channel quality is good, the impact of improving the channel quality on the signal quality gradually decreases. Therefore, a logarithmic function is used to represent the relationship between channel quality and signal quality. In the process of signal transmission, the channel quality plays a direct role in the signal quality. Therefore, the weight coefficient of the channel quality indication is relatively high. , .

[0082] Step 3: Count the total number of physical resource blocks of the target base station, and collect the number of physical resource blocks allocated in the base station during the measurement period to generate resource utilization. At the same time, obtain the number of user equipment using base station resources for data transmission during the measurement period and the base station equipment operating power, and generate the equipment load index based on the resource utilization, the number of user equipment and the base station equipment operating power;

[0083] In this embodiment, the principle for generating the device load index is:

[0084]

[0085] in, Indicates the equipment load index, Indicates the number of allocated physical resource blocks, Indicates the total number of physical resource blocks, Indicates the number of user equipment using base station resources for data transmission during the measurement period. Indicates the maximum number of user equipment that the base station can support. Indicates the operating power of the base station equipment during the measurement period. Indicates the maximum operating power of the base station equipment. They represent the weight coefficients of the number of physical resource blocks, the number of user equipment and the operating power of the base station equipment respectively. and .

[0086] The equipment load index reflects the base station load within a period of time. The higher the equipment load index, the higher the current base station load and the worse the overall performance. The resource utilization rate reflects whether the spectrum resources of the base station are close to saturation. If the resource utilization rate is high, it means that the scheduling resources of the base station are close to the limit and there is load pressure. The resource utilization rate is proportional to the load pressure. The actual number of user devices and the maximum number of users that the base station can support reflect whether the base station is close to the user access upper limit. Approaching the user access upper limit leads to a decrease in the scheduling efficiency of the base station and an increase in the equipment load. The operating power of the base station equipment indicates the total power of all equipment in the base station during normal operation, which is related to its used resources. When the operating power is close to the upper limit, it reflects that the base station is under a large operating load. The physical resource block is the core capability of the base station service. The base station completes the transmission scheduling of user data by allocating physical resource blocks. If the resource blocks are exhausted, the base station cannot provide services. Therefore, the weight coefficient of resource utilization is the highest. The base station needs to dynamically schedule resource blocks according to the number of user devices. The more users there are, the higher the complexity of scheduling and the higher the load of the base station. The increase in the number of user devices will not directly lead to the exhaustion of base station resources. The base station can alleviate the pressure caused by the increase in the number of users by taking appropriate scheduling measurements. Therefore, the weight coefficient is second only to resource utilization. The increase in the operating power of base station equipment is usually indirectly caused by resource occupation or an increase in the number of users. The level of operating power of base station equipment reflects the energy consumption pressure of base station operation. Although it will increase the operating cost, it does not directly affect the service quality. Therefore, the weight coefficient of the operating power of base station equipment is the lowest. In summary, , , .

[0087] Step 4: Build a model based on the deep learning network, take base station power as input, reference signal received power and received signal strength as labels, generate a signal coverage prediction model, take base station power as input, inter-area interference ratio and channel quality indication as labels, generate a signal quality prediction model, take base station power as input, resource utilization, number of user devices and base station equipment operating power as labels, generate a load prediction model;

[0088] In this embodiment, the deep learning network structure for constructing the signal coverage prediction model is:

[0089] Input layer: contains 1 neuron, used to input base station power;

[0090] The first hidden layer: contains 64 neurons, activated by the ReLU activation function;

[0091] The second hidden layer: contains 32 neurons, activated using the ReLU activation function;

[0092] Output layer: contains 2 neurons, which are used to output the reference signal received power and received signal strength;

[0093] The deep learning network structure for building the signal quality prediction model is:

[0094] Input layer: contains 1 neuron, used to input base station power;

[0095] The first hidden layer: contains 64 neurons, activated by the ReLU activation function;

[0096] The second hidden layer: contains 32 neurons, activated using the ReLU activation function;

[0097] Output layer: contains 2 neurons, which are used to output the inter-region interference ratio and channel quality indication;

[0098] The deep learning network structure for building the load prediction model is:

[0099] Input layer: contains 1 neuron, used to input base station power;

[0100] The first hidden layer: contains 64 neurons, activated by the ReLU activation function;

[0101] The second hidden layer: contains 32 neurons, activated using the ReLU activation function;

[0102] Output layer: contains 3 neurons, which are used to output resource utilization, number of user devices and operating power of base station equipment.

[0103] Step 5: Based on the signal coverage index, signal quality index and equipment load index, the objective function value of the base station power is calculated. Taking the maximization of the objective function value as the optimization goal, the simulated annealing algorithm and the prediction model are combined to optimize the current power of the target base station.

[0104] In this embodiment, the principle for generating the objective function value is:

[0105] The formula for generating the comprehensive evaluation index of the signal is:

[0106]

[0107] in, represents the comprehensive evaluation index of the signal, represents the signal coverage index, represents the signal quality index, They represent the weight coefficients of signal coverage index, signal quality index and the interaction term between signal coverage index and signal quality index respectively. ,and ;

[0108] The signal comprehensive evaluation index reflects the comprehensive performance of the base station in terms of signal coverage and signal quality. The higher it is, the wider the base station coverage is. The higher the value, the better the signal transmission quality of the base station. It reflects the synergistic effect of signal coverage and signal quality. The purpose is to capture the situation where both signal coverage and signal quality are good at the same time. In the actual optimization process, both signal coverage and quality should be taken into account. , interaction term It is a supplement to some of the previous two situations, so the weight coefficient is relatively low. , .

[0109] The formula used to generate the objective function value is:

[0110]

[0111] in, represents the objective function value, Indicates the equipment load index.

[0112] The principle for optimizing base station power is:

[0113] Base station power range As a constraint, the base station power is optimized, where Indicates the minimum power of the base station. Represents the maximum power of the base station. When the base station power is optimized based on the simulated annealing algorithm, the base station power before optimization is calibrated as The corresponding annealing temperature is , the objective function value is , the formula for optimizing base station power is:

[0114]

[0115] in, represents the optimized base station power, represents the adjustment factor, and , Indicates the adjustment step size;

[0116] calculate Corresponding ,when When directly accepting ;

[0117] when When the probability Accept new interpretations;

[0118] After each new solution is calculated, the annealing temperature is reduced. The formula for reducing the annealing temperature is:

[0119]

[0120] in, represents the reduced annealing temperature after optimization, represents the attenuation coefficient, and ;

[0121] When the annealing temperature drops to When the value is below , the calculation is stopped and the current solution is output. The corresponding base station power is the optimized base station power.

[0122] See also Figure 2 The present invention also provides a base station power optimization system, which is used to implement the above-mentioned base station power optimization method, and specifically includes:

[0123] A signal measurement module is used to select multiple measurement time periods, select a target receiving area served by a target base station for each measurement time period, obtain a reference signal received power and a received signal strength according to information reported by a receiving end in the target receiving area, and generate a signal coverage index based on the reference signal received power and the received signal strength, wherein the receiving end is a user equipment connected to the target base station in the target receiving area;

[0124] An interference calculation module is used to select other areas with the same frequency reuse distance as the target receiving area within the service range of the target base station, set them as interference areas, obtain the received signal strengths of all interference areas to generate interference strengths, generate inter-area interference ratios based on the interference strengths and the received signal strengths of the target area, obtain channel quality indicators based on the reporting information of the receiving end of the target area, and generate signal quality indexes based on the inter-area interference ratios and the channel quality indicators;

[0125] A load measurement module is used to count the total number of physical resource blocks of the target base station, collect the number of physical resource blocks allocated in the base station during the measurement period, generate resource utilization, and obtain the number of user devices that use base station resources for data transmission during the measurement period and the base station equipment operating power, and generate a device load index based on the resource utilization, the number of user devices and the base station equipment operating power;

[0126] A model building module is used to build a model based on a deep learning network, using base station power as input, reference signal received power and received signal strength as labels to build a signal coverage prediction model, using base station power as input, inter-area interference ratio and channel quality indication as labels to build a signal quality prediction model, and using base station power as input, resource utilization, number of user devices and base station equipment operating power as labels to build a load prediction model;

[0127] The comprehensive optimization module is used to calculate the objective function value of the base station power based on the signal coverage index, signal quality index and equipment load index, and optimize the current power of the target base station by maximizing the objective function value, combining the simulated annealing algorithm and the prediction model.

[0128] The present invention also provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is running, the computer storage medium is controlled to execute the above-mentioned base station power optimization method.

[0129] The present invention also provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein the memory is used to store computer programs or instructions, and the processor is used to execute the computer program or instructions so that the electronic device implements the above-mentioned base station power optimization method.

[0130] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0131] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A base station power optimization method, characterized in that: The specific steps include: Step 1: Select multiple measurement time periods, select a target receiving area served by a target base station for each measurement time period, obtain a reference signal received power and a received signal strength according to the reporting information of a receiving end in the target receiving area, and generate a signal coverage index based on the reference signal received power and the received signal strength, wherein the receiving end is a user equipment connected to the target base station in the target receiving area; Step 2: Select other areas that reuse the same frequency as the target receiving area within the service range of the target base station and set them as interference areas. Obtain the received signal strengths of all interference areas to generate interference strengths. Generate an inter-area interference ratio based on the interference strength and the received signal strength of the target area. Obtain a channel quality indicator based on the receiving end reporting information of the target area. Generate a signal quality index based on the inter-area interference ratio and the channel quality indicator. Step 3: Count the total number of physical resource blocks of the target base station, and collect the number of physical resource blocks allocated in the base station during the measurement period to generate resource utilization. At the same time, obtain the number of user equipment using base station resources for data transmission during the measurement period and the base station equipment operating power, and generate the equipment load index based on the resource utilization, the number of user equipment and the base station equipment operating power; Step 4: Build a model based on the deep learning network, take base station power as input, reference signal received power and received signal strength as labels, build a signal coverage prediction model, take base station power as input, inter-area interference ratio and channel quality indication as labels, build a signal quality prediction model, take base station power as input, resource utilization, number of user devices and base station equipment operating power as labels, build a load prediction model; Step 5: Based on the signal coverage index, signal quality index and equipment load index, the objective function value of the base station power is calculated. Taking the maximization of the objective function value as the optimization goal, the simulated annealing algorithm and the prediction model are combined to optimize the current power of the target base station.

2. A base station power optimization method according to claim 1, characterized in that: The formula for generating the signal coverage index in step 1 is: in, represents the signal coverage index, represents the weight coefficient of the reference signal received power, represents the scale factor, represents the reference signal power, Represents the hyperbolic tangent function Normalize it, Represents the weight coefficient of the received signal strength, represents the scaling factor, Indicates the received signal strength. Indicates logarithmic smoothing of the received signal strength. ,and .

3. A base station power optimization method according to claim 2, characterized in that: The principle for generating the signal quality index in step 2 is: The formula used to generate the inter-area interference ratio is: in, represents the inter-area interference ratio, represents the interference intensity, Indicates Interference ratio between interference areas, Indicates The received signal strength of each interference area, represents the index of the interference region, and , Indicates the number of interference areas; The formula used to generate the signal quality index is: in, represents the signal quality index, represents the weight coefficient of the inter-region interference ratio, Indicates the maximum inter-area interference ratio among all interference areas within the target base station service range, represents the weight coefficient of the channel quality indicator, Indicates the channel quality indicator. ,and .

4. A base station power optimization method according to claim 1, characterized in that: The principle for generating the equipment load index in step 3 is: in, Indicates the equipment load index, Indicates the number of allocated physical resource blocks, Indicates the total number of physical resource blocks, Indicates the number of user equipment using base station resources for data transmission during the measurement period. Indicates the maximum number of user equipment that the base station can support. Indicates the operating power of the base station equipment during the measurement period. Indicates the maximum operating power of the base station equipment. They represent the weight coefficients of the number of physical resource blocks, the number of user equipment and the operating power of the base station equipment respectively. and .

5. A base station power optimization method according to claim 1, characterized in that: The principle for generating the objective function value in step 5 is: The formula for generating the comprehensive evaluation index of the signal is: in, represents the comprehensive evaluation index of the signal, represents the signal coverage index, represents the signal quality index, They represent the weight coefficients of signal coverage index, signal quality index and the interaction term between signal coverage index and signal quality index respectively. ,and ; The formula used to generate the objective function value is: in, represents the objective function value, Indicates the equipment load index.

6. A base station power optimization method according to claim 5, characterized in that: The principle for optimizing the base station power in step 5 is: Base station power range As a constraint, the base station power is optimized, where Indicates the minimum power of the base station. Represents the maximum power of the base station. When the base station power is optimized based on the simulated annealing algorithm, the base station power before optimization is calibrated as The corresponding annealing temperature is , the objective function value is , the formula for optimizing base station power is: in, represents the optimized base station power, represents the adjustment factor, and , Indicates the adjustment step size; calculate Corresponding ,when When directly accepting ; when When, with probability Accept new interpretations; After each new solution is calculated, the annealing temperature is reduced. The formula for reducing the annealing temperature is: in, represents the reduced annealing temperature after optimization, represents the attenuation coefficient, and ; When the annealing temperature drops to When the value is below , the calculation is stopped and the current solution is output. The corresponding base station power is the optimized base station power.

7. A base station power optimization system, characterized in that: The system is used to execute the base station power optimization method according to any one of claims 1 to 6, specifically comprising: A signal measurement module is used to select multiple measurement time periods, select a target receiving area served by a target base station for each measurement time period, obtain a reference signal received power and a received signal strength according to information reported by a receiving end in the target receiving area, and generate a signal coverage index based on the reference signal received power and the received signal strength, wherein the receiving end is a user equipment connected to the target base station in the target receiving area; An interference calculation module is used to select other areas with the same frequency reuse distance as the target receiving area within the service range of the target base station, set them as interference areas, obtain the received signal strengths of all interference areas to generate interference strengths, generate inter-area interference ratios based on the interference strengths and the received signal strengths of the target area, obtain channel quality indicators based on the reporting information of the receiving end of the target area, and generate signal quality indexes based on the inter-area interference ratios and the channel quality indicators; A load measurement module is used to count the total number of physical resource blocks of the target base station, collect the number of physical resource blocks allocated in the base station during the measurement period, generate resource utilization, and obtain the number of user devices that use base station resources for data transmission during the measurement period and the base station equipment operating power, and generate a device load index based on the resource utilization, the number of user devices and the base station equipment operating power; A model building module is used to build a model based on a deep learning network, using base station power as input, reference signal received power and received signal strength as labels to build a signal coverage prediction model, using base station power as input, inter-area interference ratio and channel quality indication as labels to build a signal quality prediction model, and using base station power as input, resource utilization, number of user devices and base station equipment operating power as labels to build a load prediction model; The comprehensive optimization module is used to calculate the objective function value of the base station power based on the signal coverage index, signal quality index and equipment load index, and optimize the current power of the target base station by maximizing the objective function value, combining the simulated annealing algorithm and the prediction model.

8. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is running, the computer storage medium is controlled to execute the base station power optimization method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: It includes at least one processor and a memory connected to the processor, wherein: the memory is used to store computer programs or instructions, and the processor is used to execute the computer program or instructions so that the electronic device implements the base station power optimization method described in any one of claims 1-6.

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