5g base station site location method and system centered on convergence device

Abstract

The application relates to the field of 5G communication technology, in particular to a 5G base station site selection method and system taking a convergence device as a center, which comprises setting candidate base stations, convergence device and link related parameters and decision variables; enabling the candidate base stations and connecting the nearest convergence device in a high demand area, calculating the total cost, the time delay of each service and the utilization rate at this time; using the evaluation function of the simulated annealing algorithm to calculate the total cost, the time delay and the utilization rate to obtain the current solution; randomly exchanging the convergence device allocation of the base station or adjusting the link path until the constraint condition is met, recalculating the total cost, the time delay and the utilization rate and substituting them into the evaluation function to obtain a new solution; judging whether to accept the new solution according to the simulated annealing algorithm criterion, constantly updating the temperature and iterating until the maximum running number is reached, and finally forming a convergence device deployment scheme and a link planning. The application is used for reasonably planning the base station site under the conditions of multi-site, multi-convergence device and multi-target optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of 5G communication technology, in particular to a 5G base station site selection method and system centered on a convergence device. BACKGROUND

[0002] With the rapid evolution of 5G networks, the dense deployment of base stations has become a key means to support high speed, low latency and large connectivity capabilities. In a typical networking architecture, a large number of access base stations need to be implemented through convergence devices for centralized transmission and resource scheduling. This convergence device-centered architecture not only improves the overall management efficiency of the network, but also lays the foundation for subsequent evolution in 6G and ultra-low latency scenarios. The current industry gradually shifts from single coverage optimization to multi-dimensional optimization considering network energy efficiency, service latency, link load balancing and transmission layer port capacity utilization, which puts higher technical requirements on the site selection of base stations.

[0003] Existing 5G base station site selection research mainly focuses on single-objective or double-objective models based on maximum coverage rate, minimum construction cost or energy optimization. Some methods use integer linear programming, genetic algorithms or particle swarm optimization to select sites, which can to some extent solve the contradiction between base station density and energy efficiency. Some research has also begun to introduce multi-objective heuristic methods, taking into account user distribution, spectrum resource allocation and energy consumption factors. However, most current technical solutions still focus on the geometric matching relationship between sites and users, and lack consideration of convergence device port capacity constraints, dynamic scheduling of link utilization and transmission performance of latency-sensitive services, and have not yet formed an end-to-end systematic optimization model. In actual deployment, this deficiency can easily lead to a number of problems: first, site planning ignores the limitations of convergence device port capacity, which can cause some ports to be overloaded for a long time while others are idle, leading to uneven allocation of link resources and potential congestion risks; second, link utilization is often treated as a static indicator, lacking modeling constraints for dynamic traffic fluctuations and high concurrency loads, which can easily lead to persistent congestion on local links while overall network resources are not effectively utilized; in addition, for low-latency applications emphasized by 5G, existing methods often still use hop count or bandwidth as the main optimization target, lacking end-to-end latency constraints, making it difficult to guarantee the quality of service for latency-sensitive services such as vehicle networking and industrial control in large-scale access scenarios; finally, although some heuristic or intelligent optimization algorithms can achieve a certain trade-off between multiple objectives, they fail to capture key details such as port-level capacity bottlenecks and queuing delays due to their coarse modeling granularity, resulting in a deviation between the site selection results and actual network operation, reducing the engineering feasibility and reliability. SUMMARY

[0004] This application provides a 5G base station site selection method and system centered on aggregation equipment, used to achieve rational planning of base station sites under conditions of multiple sites, multiple aggregation equipment, and multiple objectives. This application provides the following technical solutions:

[0005] In a first aspect, this application provides a 5G base station site selection method centered on a convergence device, the method comprising:

[0006] Set the relevant parameters and decision variables for candidate base stations, aggregation equipment, and links, and initialize service requirements;

[0007] Based on the set parameters and decision variables, candidate base stations are activated in high-demand areas and connected to the nearest aggregation device. The total cost, latency of each service, and utilization rate representing the port usage of all aggregation devices are calculated at this time.

[0008] The current solution is obtained by calculating the total cost, latency, and utilization using the evaluation function of the simulated annealing algorithm;

[0009] The aggregation equipment of the random switching base station allocates or adjusts the link path until the constraints are met, recalculates the total cost, latency, and utilization, and substitutes them into the evaluation function to obtain a new solution;

[0010] Based on the simulated annealing algorithm criteria, a new solution is accepted. The temperature is continuously updated and iterated until the maximum number of rounds is reached, forming the final aggregation device deployment plan and link planning.

[0011] In a specific feasible implementation, the setting of candidate base stations, aggregation equipment, and link-related parameters and decision variables includes:

[0012] Set the candidate base station set as The convergence equipment is a collection of The set of business requirements is ;

[0013] The fixed construction cost of each candidate base station is Operating energy consumption is and the maximum carrying capacity is ;

[0014] The total port capacity of each aggregation device is The transmission delay of the link between the candidate base station and the aggregation device is The bandwidth requirement of the demand point is ,parameter Indicates candidate base station Can it cover the demand points? ;

[0015] Define decision variables, including decision variables for candidate base station construction. Base stations choose As a decision variable for access aggregation devices and demand points By base station Covered decision variables .

[0016] In one specific implementation, the activation of candidate base stations and connection to the nearest aggregation device in high-demand areas based on set parameters and decision variables, and the calculation of the total cost, latency for each service, and utilization rate representing the port usage of all aggregation devices at this time, include:

[0017] When calculating total cost, total cost equal to base station cost and link cost The sum, where base station cost is divided into two parts: fixed construction cost and base station operating cost, is calculated using the following formula:

[0018] ;

[0019] The cost of each link consists of three parts: the cost of transmission distance, the cost related to bandwidth, and the fixed cost of link construction, which is calculated by the following formula:

[0020] ;

[0021] In the formula, This indicates the construction cost per kilometer of the link. Indicates from base station To convergence equipment physical distance, This represents the additional cost required per Gbps of bandwidth. Indicates base station Total bandwidth required Fixed construction cost for each link.

[0022] In one specific implementation, the activation of candidate base stations and connection to the nearest aggregation device in high-demand areas based on set parameters and decision variables, and the calculation of the total cost, latency for each service, and utilization rate representing the port usage of all aggregation devices at this time, include:

[0023] When calculating latency for each service, link latency is included. Divided into propagation delay Transmission delay and processing latency The link latency, consisting of three parts, can be calculated using the following formula:

[0024] ;

[0025] In the formula, , The speed at which a signal propagates in a medium; , The bandwidth of the link between the base station and the aggregation device. Indicates base station Total bandwidth required;

[0026] For each business requirement, the total link latency is .

[0027] In one specific implementation, the activation of candidate base stations and connection to the nearest aggregation device in high-demand areas based on set parameters and decision variables, and the calculation of the total cost, latency for each service, and utilization rate representing the port usage of all aggregation devices at this time, include:

[0028] When calculating utilization, the port utilization of each aggregation device is considered. It is determined by the total bandwidth of the services it carries and the total capacity of its ports, and can be calculated using the following formula:

[0029] ;

[0030] After calculating the port utilization rate of each aggregation device, the deviation of the port utilization rate for all aggregation devices is obtained by calculating their variance, as shown below:

[0031] ;

[0032] in, This represents the average port utilization of all aggregation devices.

[0033] In one specific implementation, the evaluation function using the simulated annealing algorithm to calculate the total cost, latency, and utilization to obtain the current solution includes:

[0034] Introducing the simulated annealing algorithm, through the evaluation function By unifying and quantifying the multi-objective indicators, we can obtain the current solution. The evaluation function is as follows:

[0035] ;

[0036] in, All are weighting coefficients. For total cost, For the total link latency, This refers to the utilization rate of the aggregation device ports.

[0037] In a specific feasible implementation, the process of determining whether to accept a new solution based on the simulated annealing algorithm criteria, continuously updating the temperature and iterating until the maximum number of running rounds is reached, to form the final aggregation device deployment scheme and link planning, includes:

[0038] To determine whether the new solution is better than the current solution, calculate the difference between the new solution and the current solution. If it is the difference If the value is greater than 0, it proves that the new solution is better than the current solution, and the new solution is accepted directly; otherwise, determine... Is it greater than ,in, Indicates the current temperature. Represents a random number between [0,1]. The algorithm iterates several times at each temperature. After completing all rounds at the current temperature, the temperature is reduced according to the preset cooling coefficient, and the algorithm enters the next temperature level to continue iterating. This process is repeated until the preset maximum number of rounds is reached, at which point the algorithm terminates and obtains the optimal solution.

[0039] The optimal solution determines the final deployment scheme for the aggregation equipment and the specific implementation plan for the link planning.

[0040] Secondly, this application provides a 5G base station site selection system centered on a convergence device, which adopts the following technical solution:

[0041] A 5G base station site selection system centered on a convergence device includes:

[0042] The parameter setting module is used to set the parameters and decision variables related to candidate base stations, aggregation equipment and links, and initialize service requirements;

[0043] The initial calculation module is used to activate candidate base stations and connect the nearest aggregation device in high-demand areas based on set parameters and decision variables, and calculate the total cost, latency of each service, and utilization rate representing the port usage of all aggregation devices at this time.

[0044] The current solution calculation module is used to calculate the total cost, latency, and utilization using the evaluation function of the simulated annealing algorithm to obtain the current solution;

[0045] The new solution calculation module is used to allocate or adjust the link path of the aggregation equipment of the random switching base station until the constraints are met, recalculate the total cost, latency and utilization and substitute them into the evaluation function to obtain a new solution;

[0046] The iterative output module is used to determine whether to accept a new solution based on the simulated annealing algorithm criteria, continuously update the temperature and iterate until the maximum number of running rounds is reached, forming the final aggregation device deployment scheme and link planning.

[0047] Thirdly, this application provides an electronic device, the device including a processor and a memory; the memory stores a program, the program being loaded and executed by the processor to implement a 5G base station site selection method centered on a convergence device as described in the first aspect.

[0048] Fourthly, this application provides a computer-readable storage medium storing a program that, when executed by a processor, is used to implement a 5G base station site selection method centered on a convergence device as described in the first aspect.

[0049] This method introduces key parameters and constraints for candidate base stations, aggregation devices, and links through mathematical modeling, including base station construction and operation costs, base station and aggregation device port capacity limitations, link utilization balance, and end-to-end transmission latency. Based on this, an improved simulated annealing heuristic algorithm is used for global and local optimization. First, the method determines the activation of candidate base stations and the connection scheme of aggregation devices in high-demand areas based on service demand density, calculates the corresponding total cost, link latency for each service, and aggregation device port utilization, and comprehensively quantifies multiple objective indicators using the evaluation function of the simulated annealing algorithm to obtain the current solution. Subsequently, by randomly adjusting the on / off state of base stations, switching aggregation device access, and reallocating service demands, a new solution is calculated under the premise of satisfying the constraints. The simulated annealing criterion is used to determine whether to accept the new solution, and optimization iterations are performed in conjunction with temperature parameters and iteration rounds, ultimately forming an aggregation device deployment scheme and link planning under various constraints. This method overcomes the limitations of existing technologies that mainly rely on geometric matching, achieves end-to-end systematic optimization, and fully considers aggregation device port capacity constraints, link utilization balance, and the transmission performance of latency-sensitive services. In terms of technical effectiveness, this method can effectively reduce the construction and operation costs of base stations, improve the utilization rate of aggregation equipment ports, optimize network transmission performance, and achieve a balance between coverage, cost, energy efficiency and latency performance while meeting user coverage needs. It provides a directly implementable optimization decision-making solution for large-scale base station deployment, and has significant innovation and application value.

[0050] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the 5G base station site selection method centered on the aggregation device in this application embodiment.

[0052] Figure 2 This is a schematic diagram of the overall process of the 5G base station site selection method centered on the aggregation device in the embodiments of this application.

[0053] Figure 3 This is a structural block diagram of a 5G base station site selection system centered on a convergence device, as described in this application embodiment.

[0054] Figure 4 This is a block diagram of an electronic device for 5G base station site selection centered on a convergence device, as described in this application embodiment. Detailed Implementation

[0055] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0056] Optionally, this application uses the 5G base station site selection method centered on the aggregation device provided in various embodiments as an example for application in electronic devices. The electronic device is a terminal or a server. The terminal can be a computer, tablet computer, etc. This embodiment does not limit the type of electronic device.

[0057] Reference Figure 1 This is a flowchart illustrating a 5G base station site selection method centered on a convergence device, provided in an embodiment of this application. The method includes at least the following steps:

[0058] Step S101: Set the candidate base station, aggregation equipment and link-related parameters and decision variables, and initialize the service requirements.

[0059] In step S101, the candidate base station set is first set as follows: The convergence equipment is a collection of The set of business requirements is Each candidate base station has a fixed construction cost. Operating energy consumption and maximum carrying capacity For each aggregation device, it has a total port capacity. The transmission delay of the link between the candidate base station and the aggregation device is... The bandwidth requirement of the demand point is ,parameter Indicates candidate base station Can it cover the demand points? Based on this, decision variables for subsequent candidate base station activation and demand allocation are defined, including candidate base station construction decision variables. Base stations choose As a decision variable for access aggregation devices and demand points By base station Covered decision variables All the above decision variables are binary variables, with values ​​of 0 or 1. Next, the service requirements are initialized, clarifying the bandwidth requirements of each requirement point and its location, so that high-demand areas can be identified when selecting candidate base stations and connection aggregation equipment later.

[0060] By setting the parameters and introducing the decision variables as described above, a mathematical modeling framework for the candidate base station, aggregation equipment, and link planning problem has been established, providing a clear mathematical expression and solution basis for the subsequent optimization process.

[0061] Step S102: Based on the set parameters and decision variables, activate candidate base stations in high-demand areas and connect to the nearest aggregation device, and calculate the total cost, latency of each service, and utilization rate representing the port usage of all aggregation devices.

[0062] Specifically, in network planning, the spatial distribution of service demands is often uneven. High-demand areas typically carry denser service traffic. If the access needs of these areas are not prioritized, local congestion can easily occur, leading to high latency or ineffective access for most services. Therefore, prioritizing the activation of candidate base stations in high-demand areas can ensure stable coverage of major services and improve the overall service quality and reliability of the network. Simultaneously, after a base station is activated, connecting it to the nearest aggregation device can minimize link length, thereby reducing propagation latency and transmission costs. This connection method not only reduces the total cost of link construction and maintenance but also effectively reduces latency overhead during data transmission. Furthermore, the proximity-based connection strategy avoids overly complex link layouts, keeping the network topology simple and facilitating subsequent optimization and expansion.

[0063] In implementation, firstly, when calculating the total cost, the total cost... Divided into base station costs ( ) and link cost ( Two parts, namely The cost of a base station can be divided into two parts: the fixed construction cost and the operating cost, which are calculated using the following formula:

[0064] ;

[0065] The cost of each link consists of three parts: the cost of transmission distance, the cost related to bandwidth, and the fixed cost of link construction, which is calculated by the following formula:

[0066] ;

[0067] In the formula, This indicates the construction cost per kilometer of the link. Indicates from base station To convergence equipment physical distance, This represents the additional cost required per Gbps of bandwidth. Indicates base station The total bandwidth required is determined by all selected base stations. The bandwidth requirements of the services are obtained by adding them together. Fixed construction cost for each link.

[0068] Secondly, when calculating the latency of each service, the link latency should be taken into account. Divided into propagation delay Transmission delay and processing latency The three parts are: propagation delay, which depends on the physical length of the link and the speed of signal propagation in the medium; transmission delay, which depends on the link bandwidth and the traffic load carried by the base station; and processing delay, which includes the processing overhead of the base station port and aggregation equipment. It is assumed that each link has a fixed processing delay. The link delay can be calculated using the following formula:

[0069] ;

[0070] In the formula, , The speed at which a signal propagates in a medium; , This represents the bandwidth (Gbps) of the link between the base station and the aggregation equipment; therefore, for each service requirement, the total link latency is... .

[0071] Finally, in calculating utilization, the port utilization of each aggregation device is considered. The total bandwidth of the services it carries and the total capacity of its ports are determined by the following formula:

[0072] ;

[0073] To assess the utilization distribution of ports across the entire network, after calculating the port utilization of each aggregation device, its variance is calculated to reflect whether the usage of all ports is uniform. A larger variance indicates more uneven port utilization. Therefore, for all aggregation devices, the deviation of port utilization is obtained by calculating their variance, as shown below:

[0074] ;

[0075] in, This represents the average port utilization of all aggregation devices.

[0076] Step S103: Use the evaluation function of the simulated annealing algorithm to calculate the total cost, latency, and utilization to obtain the current solution.

[0077] In step S103, after completing the activation of candidate base stations, connection of aggregation equipment, and calculation of related costs, service latency, and port utilization, a comprehensive evaluation of the current solution is required to determine the merits of the solution during subsequent optimization. To this end, a simulated annealing algorithm is introduced, using an evaluation function... By unifying and quantifying the multi-objective indicators, we can obtain the current solution. The evaluation function is as follows:

[0078] ;

[0079] in, These are all weighting coefficients, with different weight values ​​assigned based on the operator's needs for different factors.

[0080] Step S104: The aggregation equipment of the random switching base station allocates or adjusts the link path until the constraints are met, recalculates the total cost, latency, and utilization, and substitutes them into the evaluation function to obtain a new solution.

[0081] In step S104, the current network deployment scheme is partially and randomly adjusted to explore possible better solutions. Specifically, this includes the following operations: randomly changing the activation status of base stations, switching the access relationship between base stations and aggregation devices, and reallocating service requirements to adjust link paths as needed. After each random adjustment, it is necessary to determine whether the constraints of the base stations are met, namely, the uniqueness constraint of base station access, the port capacity constraint of base stations and aggregation devices, and the service latency constraint. Basic constraints must be met during the selection process of candidate base stations and aggregation devices. Specifically, firstly, the uniqueness of candidate base station access must be guaranteed; each activated candidate base station can only access one aggregation device, satisfying... Secondly, regarding coverage, each demand point must be covered by at least one candidate base station to meet the requirements. Furthermore, the coverage capability of the candidate base station must not exceed its actual coverage area, and must meet the following requirements. Secondly, regarding capacity factors, both the candidate base station capacity and the aggregation device port capacity need to be considered. The capacity of the candidate base station must meet the maximum capacity limit of the base station during use, i.e., satisfy... The port capacity of the aggregation device may meet the port capacity limit accordingly, i.e. Finally, in addition to meeting the above requirements, candidate base stations must also meet latency requirements when selecting them, i.e., they must meet the following conditions. .

[0082] If port capacity or latency exceeds limits, the solution needs to be corrected, for example, by migrating some service requirements to other base stations to ensure all constraints are met. Once the constraints are corrected and all constraints are satisfied, the total cost of the current solution, the link latency for each service, and the utilization rate of all aggregation device ports are recalculated and substituted into the evaluation function. The new solution after the current adjustment is obtained. .

[0083] Step S105: Determine whether to accept the new solution based on the simulated annealing algorithm criteria, continuously update the temperature and iterate until the maximum number of running rounds is reached, and form the final aggregation device deployment scheme and link planning.

[0084] In step S105, for the new solution generated in step S104, it is first determined whether the new solution is better than the current solution, that is, the difference between the new solution and the current solution is calculated. If it is the difference If the value is greater than 0, it proves that the new solution is better than the current solution, and the new solution is accepted directly. Otherwise, it is determined by probability. Deciding whether to accept a poor solution, i.e., judging Is it greater than ,in, The current temperature is a key parameter controlling the degrees of freedom in the solution space search of the simulated annealing algorithm. Higher temperatures increase the probability of accepting inferior solutions, helping the algorithm escape local optima and achieve global search. As iterations proceed, the temperature gradually decreases, reducing the probability of accepting inferior solutions and thus gradually converging the algorithm. The value represents a random number between [0,1] used to simulate random decision-making. The algorithm iterates several times at each temperature, i.e., the number of iterations at the current temperature. Each iteration includes random switching of base station aggregation equipment to allocate or adjust link paths, constraint judgment, repair operations, and new solution calculation and acceptance judgment operations. After completing all iterations at the current temperature, the temperature decreases according to a preset cooling coefficient, and the algorithm moves to the next temperature level to continue iterating. This process repeats until the preset maximum number of iterations is reached (including the sum of iterations at all temperatures), at which point the algorithm terminates. Through this mechanism, temperature and the number of iterations interact: temperature determines the probability of a poor solution being accepted, while each temperature corresponds to a fixed number of local iterations to ensure sufficient exploration of the solution space at high temperatures and gradual convergence to an approximate optimal solution at low temperatures. The obtained approximate optimal solution corresponds to the specific values ​​of the decision variables, thereby determining the specific implementation scheme for the aggregation equipment deployment scheme and link planning. The final deployment scheme and link planning for the aggregation equipment include: the deployment location of each aggregation equipment and the base station and service allocation it carries, as well as the specific connection and service carrying relationship between the base station and the aggregation equipment. All schemes meet the constraints of base station access uniqueness, capacity and latency, while taking into account the optimization of total cost, service latency and port utilization.

[0085] In summary, combining Figure 2 This method introduces key parameters and constraints for candidate base stations, aggregation devices, and links through mathematical modeling, including base station construction and operation costs, base station and aggregation device port capacity limitations, link utilization balance, and end-to-end transmission latency. Based on this, an improved simulated annealing heuristic algorithm is used for global and local optimization. First, the method determines the activation of candidate base stations and the connection scheme of aggregation devices in high-demand areas based on service demand density, calculates the corresponding total cost, latency of each service link, and aggregation device port utilization, and comprehensively quantifies multiple objective indicators using the evaluation function of the simulated annealing algorithm to obtain the current solution. Subsequently, by randomly adjusting the on / off state of base stations, switching aggregation device access, and reallocating service demands, a new solution is calculated under the premise of satisfying the constraints. The simulated annealing criterion is used to determine whether to accept the new solution, and optimization iterations are performed in conjunction with temperature parameters and iteration rounds, ultimately forming an aggregation device deployment scheme and link planning under various constraints.

[0086] This solution overcomes the limitations of existing technologies that primarily rely on geometric matching, achieving end-to-end systematic optimization. It fully considers factors such as aggregation device port capacity constraints, link utilization balance, and the transmission performance of latency-sensitive services. Technically, the goal is to achieve joint optimization of multiple objectives while meeting user coverage requirements, comprehensively considering factors such as base station construction and operation costs, aggregation device port capacity utilization, and end-to-end transmission latency. This method can effectively reduce base station construction and operation costs, improve aggregation device port utilization, and optimize network transmission performance while meeting user coverage needs, achieving a balance between coverage, cost, energy efficiency, and latency performance. It provides a directly implementable optimization decision-making solution for large-scale base station deployment, demonstrating significant innovation and application value.

[0087] Figure 3 This is a structural block diagram of a 5G base station site selection system centered on a convergence device, according to an embodiment of this application. The system includes at least the following modules:

[0088] The parameter setting module is used to set the parameters and decision variables related to candidate base stations, aggregation equipment and links, and initialize service requirements;

[0089] The initial calculation module is used to activate candidate base stations and connect the nearest aggregation device in high-demand areas based on set parameters and decision variables, and calculate the total cost, latency of each service, and utilization rate representing the port usage of all aggregation devices at this time.

[0090] The current solution calculation module is used to calculate the total cost, latency, and utilization using the evaluation function of the simulated annealing algorithm to obtain the current solution;

[0091] The new solution calculation module is used to allocate or adjust the link path of the aggregation equipment of the random switching base station until the constraints are met, recalculate the total cost, latency and utilization and substitute them into the evaluation function to obtain a new solution;

[0092] The iterative output module is used to determine whether to accept a new solution based on the simulated annealing algorithm criteria, continuously update the temperature and iterate until the maximum number of running rounds is reached, forming the final aggregation device deployment scheme and link planning.

[0093] For relevant details, please refer to the above method implementation examples.

[0094] Figure 4 This is a block diagram of an electronic device provided in one embodiment of this application. The device includes at least a processor 401 and a memory 402.

[0095] Processor 401 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 401 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 401 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 401 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 401 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0096] The memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 402 is used to store at least one instruction, which is executed by the processor 401 to implement the 5G base station site selection method centered on the aggregation device provided in the method embodiments of this application.

[0097] In some embodiments, the electronic device may also optionally include: a peripheral device interface and at least one peripheral device. The processor 401, memory 402, and peripheral device interface can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface via a bus, signal line, or circuit board. Indicatively, peripheral devices include, but are not limited to: radio frequency circuits, touch displays, audio circuits, and power supplies.

[0098] Of course, electronic devices may also include fewer or more components, and this embodiment does not limit this.

[0099] Optionally, this application also provides a computer-readable storage medium storing a program that is loaded and executed by a processor to implement the 5G base station site selection method centered on the aggregation device described in the above method embodiments.

[0100] Optionally, this application also provides a computer product including a computer-readable storage medium storing a program, which is loaded and executed by a processor to implement the 5G base station site selection method centered on the aggregation device described in the above method embodiments.

[0101] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A 5G base station site selection method centered on aggregation equipment, characterized in that, The method includes: Set candidate base stations, aggregation devices, and link-related parameters and decision variables, and initialize service requirements; setting candidate base stations, aggregation devices, and link-related parameters and decision variables includes: setting the candidate base station set as follows: The convergence equipment is a collection of The set of business requirements is The fixed construction cost for each candidate base station is Operating energy consumption is and the maximum carrying capacity is The total port capacity of each aggregation device is The transmission delay of the link between the candidate base station and the aggregation device is The bandwidth requirement of the demand point is ,parameter Indicates candidate base station Can it cover the demand points? Define decision variables, including decision variables for candidate base station construction. Base stations choose As a decision variable for access aggregation devices and demand points By base station Covered decision variables ; Based on the set parameters and decision variables, candidate base stations are activated in high-demand areas and connected to the nearest aggregation device. The total cost, latency for each service, and utilization rate representing the port usage of all aggregation devices are calculated, including: When calculating total cost, total cost equal to base station cost and link cost The sum, where base station cost is divided into two parts: fixed construction cost and base station operating cost, is calculated using the following formula: The cost of each link consists of three parts: the cost of transmission distance, the cost related to bandwidth, and the fixed cost of link construction, which is calculated by the following formula: In the formula, This indicates the construction cost per kilometer of the link. Indicates from base station To convergence equipment physical distance, This represents the additional cost required per Gbps of bandwidth. Indicates base station Total bandwidth required Fixed construction cost for each link; When calculating latency for each service, link latency is included. Divided into propagation delay Transmission delay and processing latency The link delay, consisting of three parts, can be calculated using the following formula: In the formula, , The speed at which a signal propagates in a medium; , The bandwidth of the link between the base station and the aggregation device. Indicates base station Total bandwidth required; For each business requirement, the total link latency is ; When calculating utilization, the port utilization of each aggregation device is considered. It is determined by the total bandwidth of the services it carries and the total capacity of its ports, and can be calculated using the following formula: After calculating the port utilization rate of each aggregation device, the deviation of the port utilization rate for all aggregation devices is obtained by calculating their variance, as shown below: in, This represents the average port utilization of all aggregation devices; The current solution is obtained by calculating the total cost, latency, and utilization using the evaluation function of the simulated annealing algorithm; The aggregation equipment of the random switching base station allocates or adjusts the link path until the constraints are met. The total cost, latency and utilization are recalculated and substituted into the evaluation function to obtain a new solution. The new solution is then judged according to the simulated annealing algorithm criteria. The temperature is continuously updated and iterated until the maximum number of running rounds is reached, forming the final aggregation equipment deployment scheme and link planning.

2. The 5G base station site selection method centered on the aggregation device according to claim 1, characterized in that, The evaluation function using the simulated annealing algorithm calculates the total cost, latency, and utilization to obtain the current solution, including: Introducing the simulated annealing algorithm, through the evaluation function By unifying and quantifying the multi-objective indicators, we can obtain the current solution. The evaluation function is as follows: in, All are weighting coefficients. For total cost, For the total link latency, This refers to the utilization rate of the aggregation device ports.

3. The 5G base station site selection method centered on the aggregation device according to claim 2, characterized in that, The process of determining whether to accept a new solution based on the simulated annealing algorithm criteria, continuously updating the temperature and iterating until the maximum number of rounds is reached, to form the final aggregation device deployment scheme and link planning includes: To determine whether the new solution is better than the current solution, calculate the difference between the new solution and the current solution. If it is the difference If the value is greater than 0, it proves that the new solution is better than the current solution, and the new solution is accepted directly; otherwise, determine... Is it greater than ,in, Indicates the current temperature. Represents a random number between [0,1]. The algorithm iterates several times at each temperature. After completing all rounds at the current temperature, the temperature is reduced according to the preset cooling coefficient, and the algorithm enters the next temperature level to continue iterating. This process is repeated until the preset maximum number of rounds is reached, at which point the algorithm terminates and obtains the optimal solution. The optimal solution determines the final deployment scheme for the aggregation equipment and the specific implementation plan for the link planning.

4. A 5G base station site selection system centered on a convergence device, characterized in that, include: The parameter setting module is used to set the parameters and decision variables related to candidate base stations, aggregation equipment and links, and initialize service requirements; The setting of candidate base stations, aggregation devices, and link-related parameters and decision variables includes: setting the candidate base station set as follows: The convergence equipment is a collection of The set of business requirements is The fixed construction cost for each candidate base station is Operating energy consumption is and the maximum carrying capacity is The total port capacity of each aggregation device is The transmission delay of the link between the candidate base station and the aggregation device is The bandwidth requirement of the demand point is ,parameter Indicates candidate base station Can it cover the demand points? Define decision variables, including decision variables for candidate base station construction. Base stations choose As a decision variable for access aggregation devices and demand points By base station Covered decision variables ; The initial calculation module is used to activate candidate base stations and connect to the nearest aggregation device in high-demand areas based on set parameters and decision variables. It calculates the total cost, latency for each service, and utilization rate representing the port usage of all aggregation devices, including: When calculating total cost, total cost equal to base station cost and link cost The sum, where base station cost is divided into two parts: fixed construction cost and base station operating cost, is calculated using the following formula: The cost of each link consists of three parts: the cost of transmission distance, the cost related to bandwidth, and the fixed cost of link construction, which is calculated by the following formula: In the formula, This indicates the construction cost per kilometer of the link. Indicates from base station To convergence equipment physical distance, This represents the additional cost required per Gbps of bandwidth. Indicates base station Total bandwidth required Fixed construction cost for each link; When calculating latency for each service, link latency is included. Divided into propagation delay Transmission delay and processing latency The link delay, consisting of three parts, can be calculated using the following formula: In the formula, , The speed at which a signal propagates in a medium; , The bandwidth of the link between the base station and the aggregation device. Indicates base station The total bandwidth required; for each service requirement, the total link latency is... ; When calculating utilization, the port utilization of each aggregation device is considered. It is determined by the total bandwidth of the services it carries and the total capacity of its ports, and can be calculated using the following formula: After calculating the port utilization rate of each aggregation device, the deviation of the port utilization rate for all aggregation devices is obtained by calculating their variance, as shown below: in, This represents the average port utilization of all aggregation devices; The current solution calculation module is used to calculate the total cost, latency, and utilization using the evaluation function of the simulated annealing algorithm to obtain the current solution; The new solution calculation module is used to randomly switch the aggregation equipment of the base station to allocate or adjust the link path until the constraints are met, recalculate the total cost, latency and utilization and substitute them into the evaluation function to obtain the new solution; the iterative output module is used to determine whether to accept the new solution according to the simulated annealing algorithm criteria, continuously update the temperature and iterate until the maximum number of running rounds is reached, forming the final aggregation equipment deployment scheme and link planning.

5. An electronic device, characterized in that, The device includes a processor and a memory; the memory stores a program that is loaded and executed by the processor to implement a 5G base station site selection method centered on a convergence device as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The storage medium stores a program that, when executed by a processor, is used to implement a 5G base station site selection method centered on a convergence device as described in any one of claims 1 to 3.

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