A heuristic edge base station deployment method with minimum user coverage priority

Abstract

The present invention relates to a heuristic edge base station deployment method with the least coverage and user priority. Step 1: Calculate the distance between each user location and each candidate site, and continue to execute Step 2; Step 2: Count the number of candidate sites covering each user, and select eligible users, and continue to execute Step 3; Step 3: For each candidate base station covering the users selected in Step 2, count the number of users covered by each candidate base station, and determine the new edge base station deployment location according to preset conditions, and continue to execute Step 4; Step 4: Remove the candidate base stations selected in Step 3 and the users they cover, and continue to execute Step 5; Step 5: When there are users not covered and there are still candidate sites, go to Step 2, otherwise end. The heuristic edge base station deployment method provided by the present invention can reduce the number of edge base station deployments on the premise of ensuring the maximum user coverage rate, thereby reducing the investment cost of building or upgrading the edge computing platform.

Description

Technical Field

[0001] The present invention relates to a heuristic edge base station deployment method with the least covered users prioritized. Background Art

[0002] In the past two decades, cloud computing has been widely used in various fields due to its many advantages such as on-demand, flexible, and reliable. However, in recent years, relying solely on cloud computing cannot meet the real-time requirements of many users, especially in mobile networks. This is mainly because cloud resources are shared by users all over the world, and the cloud computing platform provides services through a wide area network (WAN), which usually has a high latency. In addition, with the rapid development of communication and network technologies, more and more users now request services through mobile devices, which leads to a highly dynamic origin of requests. Therefore, the communication delay between users and the cloud computing platform is high and unstable.

[0003] Therefore, in recent years, edge computing has become increasingly popular in the industrial and academic fields, which can effectively make up for the deficiencies of cloud computing. In edge computing, some computing and storage resources are deployed close to user devices, so as to provide users with ultra-low latency services. However, due to the distributed and heterogeneous nature of limited edge resources, it is a very challenging task to provide high-quality services for all users. Many efforts have focused on solving various challenges such as edge server placement, edge caching, and task offloading. However, these efforts all assume that edge base stations have been deployed, and edge resources are placed on these sites and used to process requests. Since edge sites usually provide local area network (LAN) connections for users through wireless networks over short distances, for each request, it is expected that neighboring edge sites can accept and process its request. Therefore, the location of edge sites determines which edge resources can be used to process each request, thus affecting the quality of service.

[0004] For a user, if it is not covered by the network signals of any edge site, then it cannot communicate with any edge site. In this case, the user's requests cannot be received by edge sites and thus cannot be processed by edge resources, which may lead to a serious decline in the performance of these requests.

[0005] There are mainly three existing algorithms for solving edge base stations, namely heuristic-based, clustering-based, and metaheuristic-based algorithms. The basic idea of the heuristic algorithm is to iteratively select the candidate site that covers the most users until there are no uncovered users or no candidate sites. With this heuristic idea, in the later stage of the algorithm, there will be several users scattered in different geographical locations, which may lead to too many edge base stations required to cover these users. The clustering-based method first divides users into several categories according to their locations, and then selects the candidate sites closest to the centers of these categories as the deployment locations of edge base stations. By this method, the distance between some users and their respective category centers may exceed the signal range of the edge stations, thus reducing the user coverage rate. The metaheuristic-based algorithm utilizes some global search strategies inspired by social and natural laws, aiming to provide the globally optimal solution for optimization problems. The main problems with the metaheuristic-based algorithm are: due to the randomness of the global search strategy used, its performance is unstable and it is very easy to fall into a local optimal state, especially when solving large-scale edge deployment problems. Summary of the Invention

[0006] To solve the existing problems, the present invention provides a heuristic edge base station deployment method with the least covered users first.

[0007] The present invention adopts the following technical solutions:

[0008] A heuristic edge base station deployment method with the least covered users first, comprising:

[0009] Step 1: Calculate the distance between each user location and each candidate site. When the distance between a user and a candidate site is less than the maximum transmission distance of the edge base station network signal, the user is covered by the candidate site, otherwise not covered, and continue to execute Step 2;

[0010] Step 2: For each user, count the number of candidate sites covering each user, and select the users that meet the first preset condition, and continue to execute Step 3;

[0011] Step 3: For each candidate base station covering the users selected in Step 2, count the number of users covered by each candidate base station, and determine the new edge base station deployment location according to the second preset condition, and continue to execute Step 4;

[0012] Step 4: Remove the candidate base station selected in Step 3 and the users it covers, and continue to execute Step 5;

[0013] Step 5: When there are uncovered users and there are still candidate sites, go to Step 2, otherwise end.

[0014] Further, in step 2, the selection of users meeting the first preset condition includes: selecting the user with the smallest number of candidate sites.

[0015] Further, in step 3, the determination of the new edge base station deployment location according to the second preset condition includes: selecting the candidate site with the largest number of covered users as the new edge base station deployment location.

[0016] The beneficial effects of the present invention include: A heuristic edge base station deployment method with the least covered users first provided by the present invention focuses on the edge base station deployment problem, that is, deciding which edge base stations to deploy from multiple candidate sites. Taking maximizing the user coverage rate as the main goal, that is, maximizing the number of users covered by the network signals of at least one edge site. Since maximizing profit is the primary goal of service providers, the deployment cost is taken as the second optimization goal, and the number of deployed edge base stations is minimized. Therefore, the heuristic edge base station deployment method with the least covered users first provided by the present invention is a new heuristic algorithm. By giving priority to considering users with the least covered candidate sites, it avoids the problems existing in existing heuristic algorithms, thereby reducing the number of edge base stations required to cover users. It can ensure the maximum user coverage rate while reducing the number of edge base station deployments, thus reducing the investment cost for the construction or upgrade of the edge computing platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments:

[0018] Figure 1 is a flowchart of a heuristic edge base station deployment method with the least covered users first provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0020] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0021] It should also be understood that the term "and / or" as used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0022] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrases "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" depending on the context.

[0023] In addition, in the description of the specification and appended claims of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0024] Reference to "one embodiment" or "some embodiments" or the like described in the specification of this application means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0025] To illustrate the technical solutions described in this application, the following will be described through specific embodiments.

[0026] As Figure 1 shown, this embodiment provides a heuristic edge base station deployment method with the least coverage of users first, including the following steps:

[0027] Step 1: Calculate the distance between each user location and each candidate site. When the distance between a user and a candidate site is less than the maximum transmission distance of the edge base station network signal, the user is covered by the candidate site; otherwise, the user is not covered, and proceed to step 2.

[0028] Step 2: For each user, count the number of candidate sites covering each user, and select the users meeting the first preset condition, then continue to execute Step 3. Among them, selecting the users meeting the first preset condition includes: selecting the users with the smallest number of candidate sites. Then, Step 2 is specifically: for each user, count the number of candidate sites covering each user, select the users with the smallest number of candidate sites, and continue to execute Step 3.

[0029] Step 3: For each candidate base station covering the users selected in Step 2, count the number of users covered by each candidate base station, and determine the new edge base station deployment locations according to the second preset condition, then continue to execute Step 4. Among them, determining the new edge base station deployment locations according to the second preset condition includes: selecting the candidate site with the largest number of covered users as the new edge base station deployment location. Then, Step 3 is specifically: for each candidate base station covering the users selected in Step 2, count the number of users covered by each candidate base station, select the candidate site with the largest number of covered users as the new edge base station deployment location, and continue to execute Step 4.

[0030] Step 4: Remove the candidate base stations selected in Step 3 and the users they cover, then continue to execute Step 5.

[0031] Step 5: When there are users not covered and there are still candidate sites, go to Step 2; otherwise, end.

[0032] A heuristic edge base station deployment method with the least covered users first provided in this embodiment focuses on the edge base station deployment problem, that is, deciding which edge base stations to deploy from multiple candidate sites, taking maximizing the user coverage rate as the main goal, that is, maximizing the number of users covered by the network signals of at least one edge site. Since maximizing profit is the primary goal of service providers, the deployment cost is taken as the second optimization goal, and the number of deployed edge base stations is minimized. Therefore, a heuristic edge base station deployment method with the least covered users first provided by the present invention is a new heuristic algorithm. By giving priority to considering the users with the least covered candidate sites, it avoids the problems existing in the existing heuristic algorithms, thereby reducing the number of edge base stations required to cover users. It can ensure the maximum user coverage rate while reducing the number of edge base station deployments, thus reducing the investment cost for the construction or upgrade of the edge computing platform.

[0033] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A heuristic edge base station deployment method with minimum coverage user priority, characterized in that: include: Step 1: Calculate the distance between each user location and each candidate site. When the distance between a user and a candidate site is less than the maximum transmission distance of the edge base station network signal, the user is covered by the candidate site. Otherwise, the user is not covered and proceed to step 2. Step 2: For each user, count the number of candidate sites covering each user, select users that meet the first preset condition, and proceed to step 3; Step 3: For each candidate site covering the user selected in step 2, count the number of users covered by each candidate site, and determine a new edge base station deployment location according to the second preset condition, and proceed to step 4; Step 4: Remove the candidate sites selected in step 3 and the users they cover, and proceed to step 5; Step 5: If there are users who are not covered and there are candidate sites, go to step 2, otherwise end; In step 2, the selecting of the user meeting the first preset condition includes: selecting the user with the smallest number of candidate sites; In step 3, determining a new edge base station deployment location according to the second preset condition includes: selecting a candidate site with the largest number of covered users as a new edge base station deployment location.

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