Energy-saving control method and device for 5G base station, electronic equipment and storage medium

Abstract

The invention discloses an energy-saving control method and device for a 5G base station, electronic equipment and a storage medium. The method comprises the following steps: constructing a base station energy consumption model of a target network; selecting a base station with the strongest received signal strength for each user equipment to perform pairing connection, and calculating the total energy efficiency of the target network after the pairing connection is completed; according to the total energy efficiency, by taking the maximum number of effective base stations of the target network as a target, constructing a target function of base station dormancy and a corresponding constraint condition; solving the target function under the constraint of the constraint condition to obtain a plurality of base station dormancy combinations of the target network when the number of effective base stations of the target network is maximum; and clustering the base station dormancy combinations in each dormancy time period to obtain the base station dormancy combinations capable of entering the dormancy state in each dormancy time period, and controlling the corresponding base station dormancy combinations to enter the dormancy state. According to the invention, unnecessary energy loss in a network system can be reduced, and energy-saving control of the 5G base station is realized.

Description

Technical Field

[0001] The present invention relates to the field of 5G base station energy-saving control technology, and in particular to a 5G base station energy-saving control method, device, electronic equipment and storage medium. Background Art

[0002] Compared with 4G networks, 5G networks have higher requirements for the number and density of base stations. Therefore, the resource consumption caused by the capacity of the communication system should be fully considered during the layout of mobile base stations. Mobile cellular networks are important data transmission carriers in mobile communications. There is an obvious tidal effect in the data transmission process, which directly leads to low energy utilization. When the business load of the base station is low, if the maximum power is maintained, it will lead to serious waste of resources. Therefore, necessary energy-saving control measures need to be taken to reduce unnecessary energy loss in the network system.

[0003] At present, traditional energy-saving control measures mainly focus on improving the allocation of mobile communication network resources. Among them, the allocation of mobile communication network resources mainly focuses on the effective allocation of network resources. The research parameters include spectrum efficiency, output signal strength, interference terms, etc. Reasonable configuration of these research parameters can effectively improve network operation efficiency and thus achieve energy-saving control of base stations. However, this research method is only applicable to 4G and previous network structures, not to the existing 5G network structure, and lacks the necessary 5G base station energy-saving control methods. Summary of the invention

[0004] The present invention provides a 5G base station energy-saving control method, device, electronic device and storage medium to solve the technical problem of the lack of necessary 5G base station energy-saving control method.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a 5G base station energy saving control method, including:

[0006] Obtain the number of base stations in the target network, the transmit power of each base station, and the distance between each base station and different user equipments;

[0007] Constructing a base station energy consumption model of the target network according to the number of base stations and the transmission power of each base station;

[0008] According to the transmission power of each base station and the distance between each base station and different user equipments, a base station with the strongest received signal strength is selected for each user equipment for pairing and connection, and after the pairing and connection is completed, the propagation loss between each base station and the connected user equipment is calculated, and the total energy efficiency of the target network is calculated according to the propagation loss and the base station energy consumption model;

[0009] According to the total energy efficiency, taking the maximum number of effective base stations in the target network as the goal, constructing an objective function for base station dormancy and corresponding constraints;

[0010] Solving the objective function under the constraint of the constraint condition to obtain a plurality of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest;

[0011] A preset sleep time period is obtained, and the base station sleep combination is clustered in each sleep time period to obtain a base station sleep combination that can enter a sleep state in each sleep time period, and when the corresponding sleep time period is reached, the corresponding base station sleep combination is controlled to enter a sleep state.

[0012] As a preferred solution, the calculating the total energy efficiency of the target network according to the propagation loss and the base station energy consumption model includes:

[0013] Calculating a signal-to-noise ratio between each base station and a connected user equipment according to the propagation loss and the transmit power of each base station;

[0014] Calculate the propagation rate of each base station to the connected user equipment according to the signal-to-noise ratio, and calculate the total capacity of the target network according to the propagation rate;

[0015] The total energy consumption of the target network is calculated according to the base station energy consumption model, and the total energy efficiency of the target network is calculated according to the total capacity and the total energy consumption.

[0016] As a preferred solution, the objective function is solved under the constraint of the constraint condition to obtain several base station sleep combinations of the target network when the number of effective base stations of the target network is the largest, including:

[0017] Initialize the parameters of the preset genetic algorithm;

[0018] The position of each base station and the corresponding base station state are taken as a gene chromosome, and the obtained gene chromosome is encoded;

[0019] Obtaining a preset fitness function, and calculating the fitness corresponding to the objective function according to the fitness function and the objective function;

[0020] According to the fitness, the encoded gene chromosome, the preset termination iteration function and the genetic algorithm, the objective function is solved under the constraint of the constraint condition to obtain several base station sleep combinations of the target network when the number of effective base stations of the target network is the largest.

[0021] As a preferred solution, clustering the base station sleep combinations in each sleep time period to obtain base station sleep combinations that can enter a sleep state in each sleep time period includes:

[0022] Taking the energy consumption and complexity balance of base stations as the goal, a corresponding energy consumption and complexity balance model is constructed;

[0023] Calculate the energy consumption and complexity corresponding to the base station, and calculate the load threshold corresponding to the energy consumption and complexity balance of the base station according to the energy consumption, complexity, a preset energy consumption weight coefficient, a preset complexity weight coefficient and the energy consumption and complexity balance model;

[0024] The base station sleep combinations are clustered in each sleep time period according to the load threshold to obtain base station sleep combinations that can enter a sleep state in each sleep time period.

[0025] As a preferred solution, the base station energy consumption model is:

[0026]

[0027] Among them, EE net is the total energy consumption of all base stations in the target network, N s is the number of base stations in the target network; is the throughput of the i-th base station; P i S is the power consumption of the i-th base station; is the energy consumption of a single base station in the target network; P0 is the basic power consumption of the circuit in the active state of the base station; Δm is the load-related proportional coefficient; is the transmission power of the base station; P sleep The power consumption of the base station when it is in sleep state.

[0028] As a preferred solution, the total energy efficiency of the target network is calculated by the following formula:

[0029]

[0030] Among them, η EE is the total energy efficiency of the target network; is the total capacity of the target network; is the total energy consumption of the target network; M is the number of base stations in the target network; N is the number of user devices in the target network; is the node degree of the base station; R m，n is the transmission rate from the mth base station to the nth user equipment; s m is the working status of the mth base station.

[0031] As a preferred solution, the objective function is:

[0032] MAX[f1];

[0033] The constraints are:

[0034]

[0035] Among them, f1 is the number of valid base stations in the target network; and are the node degrees of the base station and the user equipment respectively; pl m，n p m，n is the power received by the user equipment from the base station; 2 is the noise power; is the maximum number of users that can be connected to the mth base station; τ is the probability that the user equipment is not served; C1 represents the switching state of the base station and the connection state between the base station and the user equipment; C2 indicates that a user equipment can be served by at most one base station; C3 represents the maximum number of user equipment that a restricted base station can connect to; C4 indicates that only when the base station is in an active state can it connect to any user equipment and be served by the base station; C5γ m，n ≥^th is a variable form; C6 means that the interruption probability of the user equipment is less than τ.

[0036] On the basis of the above embodiment, another embodiment of the present invention provides an energy-saving control device for a 5G base station, including: a data acquisition module, a base station energy consumption model construction module, a total energy efficiency calculation module, an objective function construction module, an objective function solution module and a base station sleep control module;

[0037] The data acquisition module is used to acquire the number of base stations in the target network, the transmission power of each base station, and the distance between each base station and different user equipments;

[0038] The base station energy consumption model building module is used to build a base station energy consumption model of the target network according to the number of base stations and the transmission power of each base station;

[0039] The total energy efficiency calculation module is used to select the base station with the strongest received signal strength for each user equipment for pairing and connection according to the transmission power of each base station and the distance between each base station and different user equipment, and calculate the propagation loss between each base station and the connected user equipment after the pairing and connection is completed, and calculate the total energy efficiency of the target network according to the propagation loss and the base station energy consumption model;

[0040] The objective function construction module is used to construct an objective function of base station dormancy and corresponding constraint conditions based on the total energy efficiency and taking the maximum number of effective base stations in the target network as the goal;

[0041] The objective function solving module is used to solve the objective function under the constraint of the constraint condition to obtain a plurality of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest;

[0042] The base station sleep control module is used to obtain a preset sleep time period, and cluster the base station sleep combinations in each sleep time period to obtain base station sleep combinations that can enter a sleep state in each sleep time period, and when the corresponding sleep time period is reached, control the corresponding base station sleep combination to enter a sleep state.

[0043] Based on the above embodiments, another embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the energy-saving control method of the 5G base station described in the above embodiments of the invention is implemented.

[0044] Based on the above embodiments, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the energy-saving control method of the 5G base station described in the above invention embodiment.

[0045] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0046] The present invention provides an energy-saving control method for a 5G base station, which obtains the number of base stations of a target network, the transmission power of each base station, and the distance between each base station and different user equipment; constructs a base station energy consumption model of the target network according to the number of base stations and the transmission power of each base station; selects a base station with the strongest received signal strength for each user equipment for pairing connection according to the transmission power of each base station and the distance between each base station and different user equipment, and calculates the propagation loss between each base station and the connected user equipment after the pairing connection is completed, and calculates the total energy efficiency of the target network according to the propagation loss and the base station energy consumption model; according to the total energy efficiency, with the maximum number of effective base stations of the target network as the goal, constructs an objective function of base station sleep and corresponding constraints; solves the objective function under the constraints of the constraints to obtain a plurality of base station sleep combinations of the target network when the number of effective base stations of the target network is the maximum; obtains a preset sleep time period, and clusters the base station sleep combinations in each sleep time period to obtain a base station sleep combination that can enter a sleep state in each sleep time period, and when the corresponding sleep time period is reached, controls the corresponding base station sleep combination to enter a sleep state.

[0047] The present invention aims to maximize the number of effective base stations in the target network, solves and obtains several base station sleep combinations of the target network, and clusters the base station sleep combinations in each preset sleep time period to obtain base station sleep combinations that can enter the sleep state in each sleep time period, and then controls the corresponding base station sleep combination to enter the sleep state when the corresponding sleep time period is reached. Through the present invention, some base stations can be selected to enter the sleep state when the base station load is low, so as to reduce unnecessary energy loss in the network system and realize energy-saving control of 5G base stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flowchart of a 5G base station energy saving control method provided by one embodiment of the present invention;

[0049] Figure 2 It is the overall flow chart of energy-saving control of 5G base stations;

[0050] Figure 3 It is an optimization flow chart of the base station sleep strategy;

[0051] Figure 4 It is a structural schematic diagram of an energy-saving control device for a 5G base station provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0054] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0055] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0056] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0057] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0058] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0059] Embodiment 1

[0060] Please refer to Figure 1 , is a flow chart of a 5G base station energy saving control method provided by an embodiment of the present invention, comprising the following specific steps:

[0061] S1. Obtain the number of base stations in the target network, the transmission power of each base station, and the distance between each base station and different user equipments;

[0062] S2. Constructing a base station energy consumption model of the target network according to the number of base stations and the transmission power of each base station;

[0063] Preferably, the base station energy consumption model is:

[0064]

[0065] Among them, EE net is the total energy consumption of all base stations in the target network, N sis the number of base stations in the target network; is the throughput of the i-th base station; P i S is the power consumption of the i-th base station; is the energy consumption of a single base station in the target network; P0 is the basic power consumption of the circuit in the active state of the base station; Δm is the load-related proportional coefficient; is the transmission power of the base station; P sleep The power consumption of the base station when it is in sleep state.

[0066] For details, please refer to Figure 2 , which is the overall flow chart of energy-saving control of 5G base stations. Compared with 4G networks, 5G networks have higher requirements on the number and density of base stations. Therefore, the resource consumption caused by the capacity of the communication system should be fully considered in the layout of mobile base stations. Mobile cellular networks are important data transmission carriers in mobile communications. There is an obvious tidal effect in the data transmission process, which directly leads to low energy utilization. When the business load of the base station is low, if it is at maximum power, it will cause serious waste of resources. Therefore, low-load base stations can be directly selected to enter a dormant state to reduce unnecessary energy loss in the network system. In the process of analyzing 5G base station energy-saving technology, the present invention directly starts from the perspective of user access, combines user communication quality and network energy efficiency analysis, and combines genetic algorithms to construct a 5G base station energy-saving technology based on collaborative control of the Internet of Things to reasonably sleep base stations.

[0067] The present invention selects a heterogeneous network architecture to study the sleep system, designs a base station group corresponding to a threshold, conducts a system study with a service load as the sleep combination, studies heterogeneous network macro base stations and small base stations, and analyzes the best sleep combination scheme from the orthogonal spectrum of the macro base station and the small base station. Based on the structure of the heterogeneous cellular network, the present invention proposes a base station sleep method based on a heterogeneous cellular network based on the above problems, which specifically includes the following steps:

[0068] Step 1: Establish system network model:

[0069] Compared with 4G networks, 5G networks have higher requirements for the number and density of base stations. Therefore, the resource consumption caused by the capacity of the communication system should be fully considered during the layout of mobile base stations. The traditional macrocellular network architecture can no longer meet the energy consumption requirements of dense 5G base stations. It is necessary to improve the traditional macrocellular network structure and propose a new network structure solution. UDN technology is a heterogeneous network structure different from the traditional macrocellular network. This network structure has the characteristics of dense layout and high frequency reuse rate, which can effectively improve the network capacity. It is the basic network structure commonly used in 5G networks.

[0070] Specifically, UDN (Ultra-Dense Network) is a wireless communication network architecture that aims to improve network capacity and coverage through extremely dense base station deployment. The core idea of ​​UDN is to deploy small base stations (such as micro base stations, macro base stations, Femtocells, etc.) in the network to form an extremely high base station density and provide stronger signal coverage and data throughput. UDN improves capacity in the following ways:

[0071] 1. Increased cell density:

[0072] UND can achieve higher spatial reuse by increasing the number of cells (by deploying a large number of small base stations such as micro base stations, small base stations, etc.). Compared with macro cellular networks, macro base stations have a wider coverage area and may produce larger interference areas, resulting in insufficient utilization of spectrum resources. In ultra-dense networks, due to the significant increase in the density of base stations, interference between adjacent cells is effectively reduced, thereby improving the overall network capacity.

[0073] 2. Improved spectrum reuse rate:

[0074] UND networks can reuse spectrum more efficiently in space. The deployment of small base stations allows the same spectrum to be used in multiple cells without causing too much interference, thereby improving spectrum utilization. This is much more efficient than the waste of spectrum resources caused by overlap between cells in macro cellular networks.

[0075] 3. Reduce signal interference:

[0076] In macro cellular networks, interference between base stations is more significant due to the large size of base stations and wide coverage, especially when the number of users increases, which will affect the overall capacity. In UND networks, since the distance between small base stations is shorter and the coverage area is smaller, signal interference is reduced, so it can provide higher signal quality and greater throughput.

[0077] 4. More refined allocation of frequency resources:

[0078] UND network can achieve more flexible spectrum allocation through refined resource scheduling and management. In the small base station environment, frequency resources can be dynamically adjusted according to network needs, avoiding the rigidity of resource allocation in macro cellular networks, and can better cope with traffic demands in different areas, thereby effectively improving network capacity.

[0079] 5. High user density and balanced load sharing:

[0080] The UND network can better share the network load through high-density small base stations. In high-traffic areas, the traffic load can be more evenly distributed to each cell, thereby avoiding the overload of some macro base stations. This load balancing mechanism further improves the capacity and performance of the network.

[0081] 6. Advanced technical support:

[0082] UND usually combines advanced wireless technologies such as beamforming, millimeter wave communication, carrier aggregation, etc., which can effectively increase data transmission rate and capacity. Compared with traditional macro cellular networks, UND can provide higher data rates and lower latency, further improving network capacity.

[0083] The layout of this small cellular network structure will lead to a higher density of communication base stations. In order to improve the quality of network communication services, it is necessary to further reduce network operation energy consumption and achieve green communication. Affected by wavelength, the density of 5G base stations is much higher than that of 4G networks. Suppose there are M service base stations in the base station, represented by M = {1, 2, ..., M}, where BS m It is a base station. Assume that there are N users in the network, represented by N = {1, 2, ..., N}, where UN n For user n, the real-time switching state of the matrix base station is expressed by the matrix S = [S m ] l×M Indicates that a value of 1 indicates active state and a value of 0 indicates dormant state.

[0084] 1.1 Network load:

[0085] The network complexity in the system reflects the number of users of the network load at a certain moment, and there is a positive correlation between the number of users and the network load.

[0086] 1.2 Flow model:

[0087] In order to improve the effective deployment of subsequent skills, it is necessary to analyze the model, establish attribute parameters corresponding to the model, and the traffic model mainly corresponds to the spatial customer distribution relationship. The user distribution of the present invention conforms to the characteristics of random distribution.

[0088] In the construction of the base station system network, the flow model is mainly used to describe and analyze the transmission and distribution of data flows in the network. These models help optimize network design, capacity planning, traffic scheduling, resource allocation, and interference management. The flow model is usually part of the system network model, focusing on describing the data flow, traffic allocation, and resource requirements in the network. The flow model focuses on how data flows from one node (base station) to another in the network, especially the forwarding and switching of data between different base stations.

[0089] Step 2: System energy consumption model:

[0090] There are two main forms of base station energy consumption: dynamic energy consumption and static energy consumption. Static energy consumption is mainly the basic power consumption when there is no load. Dynamic energy consumption is a function of the base station load and can be expressed as:

[0091]

[0092] P0 is the basic power consumption of the circuit in the active state; Δm represents the load-related proportionality coefficient; is the transmitting power; P sleep Indicates the power consumption when in sleep state. There are two main methods for calculating the energy efficiency of cellular networks. One is to calculate the energy consumption per unit area, with the unit of W / m2. This method focuses on calculating the total energy consumption when calculating the energy consumption. The other is to calculate the energy consumption per unit bit, with the unit of bit / sW, simplified to bit / J. This calculation method mainly calculates the comparison value of data transmission rate and energy consumption, so it is more comprehensive, including both total energy consumption and data transmission rate. The present invention uses the energy consumption per unit bit as the calculation of the cellular network energy efficiency, and the calculation formula is as follows:

[0093]

[0094] In the above formula, C t represents the total capacity of the target network; P t Represents the total energy consumption of the target network system.

[0095] Combined with the network architecture model of the present invention, the base station energy consumption EE net The statistical formula is shown in (3):

[0096]

[0097] In the above formula, N s Indicates the number of base stations in the target network; represents the throughput of the th small base station; P i S represents the power consumption of the i-th small base station.

[0098] The above formula (1) calculates the energy consumption of a single base station, taking into account whether the base station is active and its corresponding load; formula (3) is an overall evaluation of the energy efficiency of all base stations in the network. It combines the power consumption and throughput of each base station to evaluate the energy efficiency of the network.

[0099] S3. According to the transmission power of each base station and the distance between each base station and different user equipments, select the base station with the strongest received signal strength for each user equipment to pair and connect, and calculate the propagation loss between each base station and the connected user equipment after the pairing and connection is completed, and calculate the total energy efficiency of the target network according to the propagation loss and the base station energy consumption model;

[0100] Preferably, the calculating the total energy efficiency of the target network according to the propagation loss and the base station energy consumption model includes: calculating the signal-to-noise ratio between each base station and the connected user equipment according to the propagation loss and the transmission power of each base station; calculating the propagation rate of each base station to the connected user equipment according to the signal-to-noise ratio, and calculating the total capacity of the target network according to the propagation rate; calculating the total energy consumption of the target network according to the base station energy consumption model, and calculating the total energy efficiency of the target network according to the total capacity and the total energy consumption.

[0101] Preferably, the total energy efficiency of the target network is calculated by the following formula:

[0102]

[0103] Among them, η EE is the total energy efficiency of the target network; is the total capacity of the target network; is the total energy consumption of the target network; M is the number of base stations in the target network; N is the number of user devices in the target network; is the node degree of the base station; R m，n S is the transmission rate from the mth base station to the nth user equipment; m is the working status of the mth base station.

[0104] Step 3: Establish system access model:

[0105] 3.1 Establish a pairing model to achieve pairing connection between users and base stations:

[0106] (1) When a user device accesses a network, it first needs to send an access request to the base station. This request usually contains the user's identity information and the signal strength of the requested connection. The user device sends the access request through the wireless channel. After receiving the request, the base station evaluates whether to accept the connection based on the signal strength. Assume that the received signal strength of the user device is P r , the signal quality evaluation formula is as follows:

[0107]

[0108] Among them: SNR is the quality standard of the signal, P r is the received signal power and N0 is the noise power.

[0109] (2) After receiving the request from the user equipment, the base station will evaluate the signal quality between different base stations. Assume that the signal strength received by base station k is P r，k , the link gain from base station k to user is G k , then the access quality of the base station can be expressed as:

[0110]

[0111] Where: P tx is the transmission power of the base station, d k is the distance between the user and base station k, G k is the link gain and σ is the path loss exponent.

[0112] The criterion for selecting the best base station is usually to select the base station with the strongest signal quality (such as received signal strength). Therefore, the condition for selecting base station k is:

[0113] P r,k =max(P r,1 ,P r,2 ,…,P r,K );

[0114] (3) Once the best base station is selected, the system will allocate the necessary wireless resources to the user. Assume that the user equipment requirement is R u (e.g. data rate), the resource available to base station k is R b，k , if the user's demand is less than or equal to the available resources of the base station, the pairing is successful. The resource allocation formula is:

[0115]

[0116] Among them: B is the bandwidth, block size is the size of each time slot.

[0117] The base station will use a load balancing algorithm to distribute traffic to other base stations. The 5G network uses a more flexible load balancing mechanism, which dynamically adjusts the base station to which the user is connected to share the traffic of the overloaded base station. This can avoid excessive load on a certain base station and improve the overall efficiency of the network. If the current base station cannot meet the user's needs, the system can connect the user to a neighboring base station or a more suitable base station. This switching is usually achieved through a switching strategy. Taking into account factors such as the user's location, signal quality, and network load, the switching between base stations will be automatically completed.

[0118] For dense 5G base stations, the distance between base stations is small, and there is a serious overlap between the coverage areas of each base station. Therefore, users face multiple choices in the overlapped coverage area. In order to improve the user experience, it is necessary to establish a reasonable pairing between users and base stations. The relevant variables of the algorithm are as follows:

[0119] USR(u,s ki ) Whether user u sends a request to the base station ski% within the coverage area, 1 means the request has been sent, 0 means no request has been sent, UF ree (u) User access status, 1 means the request has been connected or the user has not sent a request to the base station, 0 means the user request has not been passed, PT (u) SINR connection reference table; N max Indicates the maximum number of users that can access the base station.

[0120] 3.2 Establish base station sleep algorithm model:

[0121] In order to establish the base station sleep algorithm model, the propagation loss between the base station and the user is set to pl, which can be expressed as:

[0122] pl m,n =εlog10(d m,n )+c d (4)

[0123] Where d m，n Base Station BS m With user UN n The distance between d represents the distance-independent coefficient factor; ε represents the attenuation coefficient of signal propagation.

[0124] In order to establish the connection model between UE and BS, the connection matrix between UE and BS can be expressed as X=(x n，m ) N×M ;(x n , m)={0,1}, when UN n With BS m When connecting, x n.m =1, otherwise 0.

[0125] BS m and UN n The node degrees can be expressed as and Assume that p = (p m，n ) M×N M. IV represents the transmission power matrix between BS and UE, P m，n Indicates BS m The transmission power sent to the UE. Assume that the noise power is σ 2 , then BS m With UN n The signal-to-noise ratio between them can be expressed as formula (5):

[0126]

[0127] In the formula, pl m，n pm，n is the power received by the user equipment from the base station; represents the interference caused by other base stations to the user. Assume that when γ m，n Greater than the square of the threshold; UN n Connect BJ BS m The total power consumption can be expressed as formula (6):

[0128]

[0129] Among them, S m Represents the working status of the base station. Specifically, S m is a binary state variable; when s m =1, it indicates that the base station is in an active state, providing services and consuming resources; when s m =0, it means that the base station is in sleep mode and stops providing services, thus reducing energy consumption. This state management mechanism is the core part of the base station energy-saving strategy, which achieves effective energy management and conservation by dynamically adjusting the working state of the base station (active or dormant).

[0130] Therefore, BS m To the UN n The propagation rate is:

[0131] R m,n =B m,n ·log2(I+γ m,n ) (7)

[0132] As shown in formula (8), the ratio of total capacity to total energy consumption is:

[0133]

[0134] S4. According to the total energy efficiency, with the maximum number of effective base stations in the target network as the goal, construct an objective function for base station dormancy and corresponding constraints;

[0135] Preferably, the objective function is:

[0136] MAX[f1];

[0137] The constraints are:

[0138]

[0139] Among them, f1 is the number of valid base stations in the target network; and are the node degrees of the base station and the user equipment respectively; pl m，n p m，n is the power received by the user equipment from the base station; 2 is the noise power; is the maximum number of users that can be connected to the mth base station; τ is the probability that the user equipment is not served; C1 represents the switching state of the base station and the connection state between the base station and the user equipment; C2 indicates that a user equipment can be served by at most one base station; C3 represents the maximum number of user equipment that a restricted base station can connect to; C4 indicates that only when the base station is in an active state can it connect to any user equipment and be served by the base station; C5γ m，n ≥^th is a variable form; C6 means that the interruption probability of the user equipment is less than τ.

[0140] The final effective number of base stations (energy efficiency) of the system is f2 = S·E. In addition, E[1; 1; ...; 1] N×1 Based on the above analysis, the following model can be established according to the actual situation:

[0141] MAX[f1] (9)

[0142]

[0143] Among them, f1 is the number of valid base stations in the target network; and are the node degrees of the base station and the user equipment respectively; pl m，n p m，n is the power received by the user equipment from the base station; 2 is the noise power; is the maximum number of users that can be connected to the mth base station; τ is the probability that the user equipment is not served; C1 represents the switching state of the base station and the connection state between the base station and the user equipment; C2 indicates that a user equipment can be served by at most one base station; C3 represents the maximum number of user equipment that a restricted base station can connect to; C4 indicates that only when the base station is in an active state can it connect to any user equipment and be served by the base station; C5γ m，n ≥^th is a variable form; C6 means the interruption probability of the user equipment is less than τ

[0144] Through the above, the base station energy saving problem can be transformed into a multi-objective optimization problem with multiple parameters [C1-C6]. The minimum effective state is the best energy-saving state. Therefore, it is necessary to optimize the activity state of the base station under multiple states and find the minimum activity state from multiple states.

[0145] S5. Solving the objective function under the constraint of the constraint condition to obtain a plurality of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest;

[0146] Preferably, the objective function is solved under the constraints of the constraints to obtain a number of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest, including: initializing parameters of a preset genetic algorithm; taking the position of each base station and the corresponding base station state as a gene chromosome, and encoding the obtained gene chromosome; obtaining a preset fitness function, and calculating the fitness corresponding to the objective function based on the fitness function and the objective function; solving the objective function under the constraints of the constraints based on the fitness, the encoded gene chromosome, the preset termination iteration function and the genetic algorithm to obtain a number of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest.

[0147] Step 4: Centralized sleep strategy based on genetic algorithm:

[0148] 4.1 Sleep algorithm model:

[0149] In order to achieve energy saving, it is necessary to find the best combination of base stations in normal operation from multiple base station sleep. Based on the present invention, from the perspective of nonlinear constraints, a multi-objective solution model is constructed in combination with a genetic algorithm to explore the optimal sleep combination of 5G base station groups.

[0150] Chromosome design of genetic algorithm: Assume that there are N base stations in total, and the state corresponding to each base station is GK i , then there are N corresponding states. If the positions of these base stations and the corresponding state sequence functional genes are taken as a chromosome and real-coded, and the chromosome length is set to N, then the information structure they contain can be expressed as.

[0151] Gene=[(s1,G1),(s2,G2),…,(s i ,G i ),…,(s N ,G N )] (11)

[0152] In the formula, s i represents the location of the i-th base station; G i represents the corresponding state of the i-th base station; gene represents chromosome; N represents the length of chromosome.

[0153] 4.2 Fitness Function:

[0154] Different chromosomes in the population contain a base station sleep strategy, and the pros and cons of the strategy are mainly reflected in its function. Therefore, it is necessary to analyze the system access model to obtain different base station sleep combinations. In this process, the role of the two genetic algorithms is to find the maximum sleep combination sequence of the base station energy efficiency f1, which can be expressed as:

[0155] max(f l ) 12)

[0156] In the process of solving the sequence combination optimization, the sleep analysis is mainly carried out from the perspective of the user and base station connection. Before the analysis, it is necessary to analyze the fitness function and the over-limit fitness function from the perspective of threshold adaptation.

[0157] Threshold fitness function: This function mainly determines the sleep status of the base station by setting a threshold. In order to ensure that signal interference between users does not affect the user experience, it is necessary to avoid the occurrence of threshold sequences during the population evolution process. Therefore, the threshold fitness function can be expressed as:

[0158]

[0159] In the above formula, K i Indicates whether the signal-to-noise ratio between each user and the base station is greater than the threshold; ∧ th represents the connection threshold between the user and the base station; γ m，n Indicates the signal-to-noise ratio of the connection between the user and the base station; N is the number of users; K v is the threshold fitness value.

[0160] Limit fitness function: Overlimit means that the number of users of the base station cannot exceed the load limit of the base station to ensure the stable operation of the base station. The calculation formula is as follows

[0161]

[0162] In the above formula, K j Indicates whether the number of users connected to the base station exceeds the upper limit; B num Indicates the number of users connected to the base station; The upper limit of the number of users connected to the base station; K t Indicates an over-limit fitness value.

[0163] According to the above analysis, the constraints can be used as constraints for building the model, so that the sleep strategy model can be expressed as:

[0164] max obj=f1-(α·K ν +β·K j )

[0165]

[0166] In the above formula, the penalty coefficient of the objective function α is the threshold constraint, and the penalty coefficient of the objective function β is the overflow constraint. Through the above analysis, the algorithm model of the present invention is constructed, and the algorithm flow is analyzed.

[0167] Combined with the above algorithm analysis, the genetic algorithm is used to optimize the base station sleep strategy. Please refer to Figure 3, is the optimization flow chart of the base station sleep strategy, the specific process is described as follows:

[0168] Step 1: Initialize the genetic algorithm. The initialization parameters include population size m, maximum genetic generation, generation gap gg4p, individual precision value, individual crossover probability, mutation probability, etc.

[0169] Step 2: Establish constraints based on UE and BS, and build corresponding access models based on them;

[0170] Step 3: The chromosome information in the algorithm is encoded using real number coding, where the gene chromosome is the location and status of the base station;

[0171] Step 4: Calculate the fitness of the function, obtain the fitness function through the objective function, substitute the value for iterative calculation, and finally obtain the fitness corresponding to the threshold;

[0172] Step 5: Determine the iteration process by terminating the iteration function. If the condition is met, output the best result and then proceed to step 7. If not, proceed to step 6.

[0173] Step 6: Perform operator operations on the genetic algorithm. The number of iterations is accumulated by 1. The operator types are gene selection, crossover recombination and gene mutation. Obtain a new population through operator operations and then proceed to step 4.

[0174] Step 7: Calculate the best sleeping combination of base stations and output the results.

[0175] S6. Obtain a preset sleep time period, and cluster the base station sleep combinations in each sleep time period to obtain base station sleep combinations that can enter a sleep state in each sleep time period, and control the corresponding base station sleep combination to enter a sleep state when the corresponding sleep time period is reached.

[0176] Preferably, the base station sleep combinations are clustered in each sleep time period to obtain base station sleep combinations that can enter a sleep state in each sleep time period, including: taking the energy consumption and complexity balance of the base station as the target, constructing a corresponding energy consumption and complexity balance model; calculating the energy consumption and complexity corresponding to the base station, and calculating the load threshold corresponding to the energy consumption and complexity balance of the base station according to the energy consumption, complexity, a preset energy consumption weight coefficient, a preset complexity weight coefficient and the energy consumption and complexity balance model; clustering the base station sleep combinations in each sleep time period according to the load threshold to obtain base station sleep combinations that can enter a sleep state in each sleep time period.

[0177] Step 5: Clustering time period division:

[0178] Sharing the time of a day, 5 switching trigger nodes are set in a day, and the base station loads at different SP times are different, so the number of clusters they correspond to is different. If the corresponding time nodes will re-cluster the network structure, frequent clustering will affect the stability of the system. Therefore, according to the US SP load and its load development trend, the present invention designs a threshold search scheme for target optimization and divides the whole day under the threshold condition. The time period is represented by TD i , use the SP network to cluster the clustering algorithm at the beginning of the time period. When clustering, it is mainly based on the following conditions:

[0179] (1) For TD i Each SP in the i The load difference between other SPs in the Load max is the load value when the network is fully loaded, It is a value between [0 and 1], indicating the proportion of G to full load;

[0180] For TD i SP, all SP must be guaranteed i are continuous. For example, SP1 and SP3 cannot be divided into the same TD i , while SP1, SP2 and SP3 can be divided into the same TDi;

[0181] In actual decision-making, choosing different G will lead to different results. The more clustering results there are, the further energy consumption will increase, resulting in a reduction in the supply of adjacent base stations and the inability of base stations to sleep. Therefore, it is necessary to choose a suitable threshold G to achieve the effect of energy balance.

[0182] If a base station in the same cluster is defined as a neighboring base station, an energy consumption and complexity balance model can be established:

[0183] min C=α·A+β·B (16)

[0184] The constraint condition is formula (8) [C1-C6]. The optimization target is the energy consumption and complexity balance of the base station. In the above formula, α and β are values ​​in the range of [0, 1], which are the energy consumption weight and complexity weight, respectively. α+β=1 is 0.5 in the present invention. If A is the normalized energy consumption of the whole network, then the following formula is obtained.

[0185]

[0186] In the above formula, P cost represents the energy consumption of the current threshold G; max P cost represents the maximum energy consumption of all thresholds. Let B be the complexity, then we have the following formula.

[0187]

[0188] a is the number of small base station clusters, N i is the number of populations selected in the genetic algorithm, M i is the number of base stations in the ith cluster, k i is the number of iterations completed by the i-th cluster, Complexity is the complexity generated by the current threshold G; maxComplexity is the maximum complexity.

[0189] The minimum C obtained by formula (16) is the optimal threshold. In the subsequent calculations, there are certain differences in the optimal threshold, which can be calculated by the above method.

[0190] In the present invention, the clustering object is the base station. Specifically, through the load information of the base station, a dynamic clustering sleep method based on a genetic algorithm is proposed, the purpose is to group the base stations according to their load conditions to optimize energy efficiency and determine which base stations can enter the sleep state.

[0191] Clustering content: Clustering object: Base stations are clustered according to their load conditions. Base stations with low loads will be clustered together and can be considered to enter a dormant state, thereby reducing unnecessary energy consumption.

[0192] Results from clustering: Through clustering, we can group base stations and decide which base stations can enter a dormant state during a specific time period. This is done to optimize the system's energy consumption and improve network efficiency.

[0193] Clustering time period: The present invention mentions that clustering time periods refer to different time periods within a day, during which the load conditions of the base stations will be different. According to the load changes, the base stations will be re-clustered and decide which base stations should enter the dormant state. In order to avoid frequent clustering affecting the stability of the system, the article designs a threshold-based optimization scheme to segment different time periods within a day.

[0194] The role of clustering: Energy saving: The purpose of clustering is to reduce the energy consumption of low-load base stations and reduce overall energy consumption by dynamically adjusting the working state of base stations (sleeping or active). Improve system stability: Through reasonable clustering and base station sleep management, system instability caused by excessive load adjustment can be avoided.

[0195] Connection with the best sleep combination: The relationship between clustering and the best sleep combination is that clustering can determine which base stations can enter the sleep state, which provides a basis for the final selection of the best sleep combination. The best sleep combination refers to selecting the optimal base station sleep configuration to maximize energy saving while meeting user needs and network stability.

[0196] Clustering time period usually refers to the process of clustering base stations and user devices within a specific time period. The selection of this time period usually depends on the following factors: 1) Changes in traffic patterns: The traffic demand of 5G networks and the behavior of user devices will change throughout the day, so clustering may need to be performed during certain time periods. For example, during the morning and evening peak hours, the user's traffic demand and the load of the base station may increase significantly. 2) Load fluctuations of base stations: The load conditions and energy consumption of base stations may be different in different time periods. Clustering in different time periods can help the network optimize resource scheduling and adjust the working status of base stations according to load changes. 3) Adjustment of sleep strategy: The clustering time period may also be related to the sleep strategy of the base station. The base station can enter a sleep state when the load is low, thereby saving energy. After clustering, the sleep decision of the base station can be adjusted according to the load condition of the cluster.

[0197] The main functions of clustering in 5G base stations are as follows: 1) By clustering base stations or user devices, resources can be allocated more accurately. For base stations with heavier loads, more bandwidth and computing resources are allocated, while for base stations with lighter loads, resource usage can be appropriately reduced or even enter a sleep state. 2) Through clustering, load balancing can be achieved to avoid overload or resource waste in certain base stations or areas, thereby improving the overall efficiency of the network. 3) Clustering can help base stations identify which base stations are in a low-load state, and make reasonable sleep strategies based on the clustering results, thereby reducing energy consumption.

[0198] In the centralized sleep strategy, the sleep decision of the base station is made based on the load, traffic demand and clustering results. The best sleep combination refers to the combination of base stations that enter the sleep state through reasonable clustering selection within a specific time period to minimize energy consumption while ensuring network performance.

[0199] Step 6: Base station clustering algorithm:

[0200] The present invention designs a dynamic clustering model of interest tree controlled by cooperation factor parameters, which directly transforms the clustering problem into the interest tree generation problem from the perspective of network structure. The cooperation coefficient can be defined as the degree of willingness of two base stations to cooperate with each other, which can be described as follows: if there are two base stations a and B, and their cooperation coefficient is expressed as SF(a, B), then the interference benefit brought to a by the cooperation between the two base stations a and B can be expressed as:

[0201]

[0202] In the above formula, represents the drying ratio caused by the cooperation between base station A, base station B and base station A; When base station A and base station B do not cooperate, the interference caused by cooperation between base stations is greater than a.

[0203] From formula (19), we can see that for base station A, The larger the value, the larger the SF(A,B) value, the greater the SINR gain brought to A by the cooperation between A and B, and the greater the probability of cooperation between A and B. Similarly, formula (20) represents the SINR gain received when two base stations cooperate.

[0204]

[0205] In the formula The signal-to-interference ratio received by base station B when base station A cooperates with base station B; The signal-to-interference ratio received when base station B does not cooperate.

[0206] From the above analysis, it can be seen that the cooperation between different base stations has a certain directionality, SF(A,B)≠SF(B,A), which transforms the base station cooperation problem into a two-way selection problem.

[0207] There are N 5G base stations in the system, and the clustering result is expressed as Clu = {Clu1, Clu2, ...}, where |Clu| represents the number of clusters. For example, b i K represents the kth base station in cluster i. i -user set is represented by u i = {u i 1 ,u i 2 ,…,} Use base station cooperation to eliminate possible interference.

[0208] Assuming the rate of the system is Rt, it can be expressed as formula (21):

[0209]

[0210] If Clu i Without using intra-cluster anti-interference technology, the receiving interference ratio of base station i can be expressed as:

[0211]

[0212] In the above formula, l ki is the channel parameter vector between user K and base station i; |l ki | 2 =pl k，j is the propagation loss; is the noise variance; P u is the user's transmission power;

[0213] If user i and user j promote their work through collaborative technology and eliminate the mutual interference between them, the interference ratio can be expressed as:

[0214]

[0215] Through the above comparative analysis, it is not difficult to find that the base station cooperative control technology can effectively improve the interference ratio received between base stations. Therefore, after the cluster is established, the benefits can be distributed through the synergy effect to improve the stability of the system. It is not difficult to see from formula (24) that through the cooperation between base stations, the gain can be divided, which can effectively improve the system operation efficiency and data transmission efficiency. Therefore, the clustering algorithm proposed in the present invention has a certain effect.

[0216]

[0217] It can be seen that the present invention provides a 5G base station energy-saving control method, and the present invention can achieve the following beneficial effects:

[0218] (1) There are many base stations in the 5G communication network, the base station density is high, and the users fluctuate greatly in the time and space domains, which will lead to obvious resource waste during low-load periods. It is necessary to adopt base station sleep technology to put low-load base stations into sleep state, thereby reducing system energy consumption and improving system energy consumption. This paper analyzes the principles and performance of distributed algorithms and centralized algorithms in base station sleep schemes, and proposes a centralized dynamic cluster sleep strategy based on genetic algorithm.

[0219] (2) This paper studies the sleep strategy of 5G ultra-dense network base stations and constructs a 5G base station energy-saving technology based on IoT collaborative control. The results show that the proposed 5G base station sleep scheme has a certain energy-saving effect and can effectively improve the system energy efficiency.

[0220] (3) The performance of the clustering algorithm and genetic algorithm proposed in the present invention is analyzed. Both can effectively reduce the energy consumption of 5G base stations. The clustering algorithm has the advantages of being complex and more practical.

[0221] (4) The present invention abandons the traditional base station distributed network sleep mode and adopts a centralized sleep algorithm to consider the combination mode of all base stations, so as to find the best sleep solution and improve the energy saving effect and operation stability of the system;

[0222] (5) Base station collaborative control technology can effectively improve the interference ratio received between base stations. Therefore, after the cluster is established, the benefits can be distributed through the collaborative effect to improve the stability of the system.

[0223] (6) The genetic algorithm is applied to the selection and calculation of the sleep scheme, which effectively improves the computing efficiency of the system. A cluster mode of collaborative control is proposed, and the genetic algorithm is combined to calculate the energy-saving mode of each cluster. In base station clustering, the dynamic clustering algorithm based on coordination degree avoids the problem that the traditional algorithm cannot adapt to the time-varying channels of the actual system, further improving the stability of the system.

[0224] Embodiment 2

[0225] Please refer to Figure 4 , is a structural schematic diagram of an energy-saving control device for a 5G base station provided by an embodiment of the present invention, the device comprising: a data acquisition module, a base station energy consumption model construction module, a total energy efficiency calculation module, an objective function construction module, an objective function solution module and a base station sleep control module;

[0226] The data acquisition module is used to acquire the number of base stations in the target network, the transmission power of each base station, and the distance between each base station and different user equipments;

[0227] The base station energy consumption model building module is used to build a base station energy consumption model of the target network according to the number of base stations and the transmission power of each base station;

[0228] The total energy efficiency calculation module is used to select the base station with the strongest received signal strength for each user equipment for pairing and connection according to the transmission power of each base station and the distance between each base station and different user equipment, and calculate the propagation loss between each base station and the connected user equipment after the pairing and connection is completed, and calculate the total energy efficiency of the target network according to the propagation loss and the base station energy consumption model;

[0229] The objective function construction module is used to construct an objective function of base station dormancy and corresponding constraint conditions based on the total energy efficiency and taking the maximum number of effective base stations in the target network as the goal;

[0230] The objective function solving module is used to solve the objective function under the constraint of the constraint condition to obtain a plurality of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest;

[0231] The base station sleep control module is used to obtain a preset sleep time period, and cluster the base station sleep combinations in each sleep time period to obtain base station sleep combinations that can enter a sleep state in each sleep time period, and when the corresponding sleep time period is reached, control the corresponding base station sleep combination to enter a sleep state.

[0232] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0233] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0234] Embodiment 3

[0235] Accordingly, an embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the energy-saving control method of the 5G base station described in the above-mentioned embodiment of the invention when executing the computer program.

[0236] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0237] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and various interfaces and lines are used to connect various parts of the entire device.

[0238] Embodiment 4

[0239] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the energy-saving control method of the 5G base station described in the above-mentioned embodiment of the invention.

[0240] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0241] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0242] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A 5G base station energy saving control method, characterized in that: include: Obtain the number of base stations in the target network, the transmit power of each base station, and the distance between each base station and different user equipments; Constructing a base station energy consumption model of the target network according to the number of base stations and the transmission power of each base station; According to the transmission power of each base station and the distance between each base station and different user equipments, a base station with the strongest received signal strength is selected for each user equipment for pairing and connection, and after the pairing and connection is completed, the propagation loss between each base station and the connected user equipment is calculated, and the total energy efficiency of the target network is calculated according to the propagation loss and the base station energy consumption model; According to the total energy efficiency, taking the maximum number of effective base stations in the target network as the goal, constructing an objective function for base station dormancy and corresponding constraints; Solving the objective function under the constraint of the constraint condition to obtain a plurality of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest; A preset sleep time period is obtained, and the base station sleep combination is clustered in each sleep time period to obtain a base station sleep combination that can enter a sleep state in each sleep time period, and when the corresponding sleep time period is reached, the corresponding base station sleep combination is controlled to enter a sleep state.

2. The energy-saving control method of a 5G base station according to claim 1, characterized in that: The calculating the total energy efficiency of the target network according to the propagation loss and the base station energy consumption model includes: Calculating a signal-to-noise ratio between each base station and a connected user equipment according to the propagation loss and the transmit power of each base station; Calculate the propagation rate of each base station to the connected user equipment according to the signal-to-noise ratio, and calculate the total capacity of the target network according to the propagation rate; The total energy consumption of the target network is calculated according to the base station energy consumption model, and the total energy efficiency of the target network is calculated according to the total capacity and the total energy consumption.

3. The energy-saving control method of a 5G base station according to claim 1, characterized in that: The objective function is solved under the constraint of the constraint condition to obtain a plurality of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest, including: Initialize the parameters of the preset genetic algorithm; The position of each base station and the corresponding base station state are taken as a gene chromosome, and the obtained gene chromosome is encoded; Obtaining a preset fitness function, and calculating the fitness corresponding to the objective function according to the fitness function and the objective function; According to the fitness, the encoded gene chromosome, the preset termination iteration function and the genetic algorithm, the objective function is solved under the constraint of the constraint condition to obtain several base station sleep combinations of the target network when the number of effective base stations of the target network is the largest.

4. The energy-saving control method of a 5G base station according to claim 1, characterized in that: The clustering of the base station sleep combinations in each sleep time period to obtain base station sleep combinations that can enter a sleep state in each sleep time period includes: Taking the energy consumption and complexity balance of base stations as the goal, a corresponding energy consumption and complexity balance model is constructed; Calculate the energy consumption and complexity corresponding to the base station, and calculate the load threshold corresponding to the energy consumption and complexity balance of the base station according to the energy consumption, complexity, a preset energy consumption weight coefficient, a preset complexity weight coefficient and the energy consumption and complexity balance model; The base station sleep combinations are clustered in each sleep time period according to the load threshold to obtain base station sleep combinations that can enter a sleep state in each sleep time period.

5. The energy-saving control method of a 5G base station according to claim 1, characterized in that: The base station energy consumption model is: Among them, EE net is the total energy consumption of all base stations in the target network, N s is the number of base stations in the target network; is the throughput of the i-th base station; P i S is the power consumption of the i-th base station; is the energy consumption of a single base station in the target network; P0 is the basic power consumption of the circuit in the active state of the base station; Δm is the load-related proportional coefficient; is the transmission power of the base station; P sleep The power consumption of the base station when it is in sleep state.

6. The energy-saving control method of a 5G base station according to claim 5, characterized in that: The total energy efficiency of the target network is calculated by the following formula: Among them, η EE is the total energy efficiency of the target network; is the total capacity of the target network; is the total energy consumption of the target network; M is the number of base stations in the target network; N is the number of user devices in the target network; is the node degree of the base station; R m，n is the transmission rate from the mth base station to the nth user equipment; s m is the working status of the mth base station.

7. The energy-saving control method of a 5G base station according to claim 6, characterized in that: The objective function is: MAX[f1]; The constraints are: Among them, f1 is the number of valid base stations in the target network; and are the node degrees of the base station and the user equipment respectively; pl m，n p m，n is the power received by the user equipment from the base station; 2 is the noise power; is the maximum number of users that can be connected to the mth base station; τ is the probability that the user equipment is not served; C1 represents the switching state of the base station and the connection state between the base station and the user equipment; C2 indicates that a user equipment can be served by at most one base station; C3 represents the maximum number of user equipment that a restricted base station can connect to; C4 indicates that only when the base station is in an active state can it connect to any user equipment and be served by the base station; C5γ m，n ≥^th is a variable form; C6 means that the interruption probability of the user equipment is less than τ.

8. A 5G base station energy-saving control device, characterized in that: include: Data acquisition module, base station energy consumption model construction module, total energy efficiency calculation module, objective function construction module, objective function solution module and base station sleep control module; The data acquisition module is used to acquire the number of base stations in the target network, the transmission power of each base station, and the distance between each base station and different user equipments; The base station energy consumption model building module is used to build a base station energy consumption model of the target network according to the number of base stations and the transmission power of each base station; The total energy efficiency calculation module is used to select the base station with the strongest received signal strength for each user equipment for pairing and connection according to the transmission power of each base station and the distance between each base station and different user equipment, and calculate the propagation loss between each base station and the connected user equipment after the pairing and connection is completed, and calculate the total energy efficiency of the target network according to the propagation loss and the base station energy consumption model; The objective function construction module is used to construct an objective function of base station dormancy and corresponding constraints based on the total energy efficiency and taking the maximum number of effective base stations in the target network as the goal; The objective function solving module is used to solve the objective function under the constraint of the constraint condition to obtain a plurality of base station sleep combinations of the target network when the number of effective base stations of the target network is the largest; The base station sleep control module is used to obtain a preset sleep time period, and cluster the base station sleep combinations in each sleep time period to obtain base station sleep combinations that can enter a sleep state in each sleep time period, and when the corresponding sleep time period is reached, control the corresponding base station sleep combination to enter a sleep state.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the energy-saving control method of the 5G base station as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein, when the computer program is running, the device where the storage medium is located is controlled to execute the energy-saving control method of the 5G base station as described in any one of claims 1 to 7.

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