A 6g outdoor hotspot scenario network planning method based on limited backtracking

Abstract

The application discloses a 6G outdoor hotspot scene network planning method based on limited backtracking, relates to the technical field of wireless communication, and comprises the following steps: 1) constructing a system model under an outdoor hotspot scene and analyzing system interference; 2) introducing deployment cost and base station downlink capacity limitation, and constructing a 6G outdoor hotspot scene base station deployment optimization problem into a multi-dimensional knapsack problem; 3) using a limited backtracking dynamic programming algorithm fusing a user association mechanism to solve an optimal base station deployment scheme; and 4) evaluating and analyzing the performance of the proposed scheme in view of coverage rate and user average data transmission rate. The application can effectively determine the optimal deployment quantity and position of the base station in the 6G outdoor hotspot scene, take into account deployment cost and load balancing, and significantly improve network coverage rate and data transmission rate.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a 6G outdoor hotspot scenario network planning method based on finite backtracking. Background Technology

[0002] With the rapid development of 6G mobile communication technology, communication networks are continuously moving towards the vision of "full coverage, full frequency band, full application, full sensory experience, full digitalization, and strong security." The rapid growth of smart terminals and high-data-density applications has led to an increasing demand for Quality of Service (QoS), especially in outdoor hotspot scenarios. Traditional network deployment methods are struggling to meet the comprehensive requirements for coverage, data transmission rates, and network stability in high-density user environments. Therefore, how to optimize base station deployment strategies to reduce costs and improve resource utilization while meeting QoS requirements has become a core issue in wireless network planning and research.

[0003] Currently, a large amount of research has been conducted on base station deployment, and the research methods can be mainly divided into three categories. The first is to find the optimal base station deployment configuration by adjusting base station parameters such as antennas, transmit power, and location, combined with channel measurement data under different scenarios and frequency bands. The second method is to build channel models for different scenario characteristics, use deterministic or stochastic channel models to evaluate the overall network quality, and finally obtain a feasible base station deployment scheme based on the analysis results. For example, accurately modeling obstacles in industrial IoT scenarios to calculate the optimal base station deployment density; using ray tracing methods to evaluate the quality of existing wireless networks in indoor office scenarios, and adding several base stations in the worst-performing locations to improve network communication quality, etc. Although the above two methods have low complexity and perform well in specific frequency bands and environments, they are usually limited to specific scenarios and problems, resulting in limited portability in large-scale, variable scenarios. The deployment schemes obtained are often only simple feasible solutions or local optima. Therefore, more and more research is shifting towards abstracting the network planning problem into a mathematical model and solving the optimal deployment strategy through algorithm design to balance accuracy and computational efficiency. These methods typically still rely on empirical channel models or ray tracing to obtain channel characteristics, but the research focus has shifted from channel modeling itself to the rationality of mathematical modeling and the effectiveness of algorithm solutions. Nonlinear pattern search methods, represented by the Hooke-Jeeves algorithm, and heuristic algorithms, represented by greedy algorithms and genetic algorithms, are commonly used solutions in this third category.

[0004] However, most existing studies assume uniform user distribution or only optimize a fixed number of base stations, failing to fully consider the downlink capacity overload problem of base stations under high user density and the challenges brought by non-uniform user distribution. This results in insufficient performance of deployment strategies in real-world application scenarios. Especially in outdoor hotspot areas, such as commercial districts, transportation hubs, and areas around sports stadiums, the non-uniformity of user distribution and the volatility of service demand make it difficult for traditional methods to achieve flexible and joint optimization of base station location and number. Therefore, it is necessary to design a 6G outdoor hotspot scenario network planning method based on finite backtracking to improve network service quality while alleviating the overload problem in hotspot scenarios, so as to achieve joint optimization of the number and location of base stations.

[0005] Current base station deployment methods assume uniform user distribution or only optimize a fixed number of base stations, failing to fully consider the downlink capacity overload problem of base stations under high user density and the challenges brought by non-uniform user distribution, making the deployment methods insufficiently adaptable to 6G outdoor hotspot scenarios. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a network planning method for 6G outdoor hotspot scenarios based on limited backtracking. The present invention can meet the service quality requirements such as coverage and data transmission rate, while taking into account the base station deployment cost and load balancing, realize the joint optimization of the number and location of base stations, and effectively alleviate the overload phenomenon of downlink in hotspot areas, thereby improving the overall network performance and deployment efficiency.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] A network planning method for 6G outdoor hotspot scenarios based on finite backtracking, proposed according to the present invention, includes:

[0009] Establish a system model for outdoor hotspot scenarios;

[0010] By introducing deployment costs and base station downlink capacity limitations into the system model, the optimization problem of 6G outdoor hotspot base station deployment is constructed as a multi-dimensional knapsack problem.

[0011] Based on a multidimensional knapsack problem, a finite backtracking dynamic programming algorithm with integrated user association mechanism is used to solve for the optimal base station deployment scheme.

[0012] As a further optimization of the 6G outdoor hotspot scenario network planning method based on finite backtracking described in this invention, a system model under the outdoor hotspot scenario is established, including:

[0013] S101. In an outdoor hotspot scenario, there are several buildings, and the base station is restricted to be deployed only on the top of the buildings. The top of the buildings is divided into several grids with a density δ, and the potential location of the base station is located at the center of the grid.

[0014] Candidate points (CP) are used to represent potential base station locations. Users are randomly distributed outdoors, and their distribution is represented by the Thomas cluster distribution TCPP; user index set. Represented as K represents the total number of users; CP index set L represents the total number of CPs; the set of indices for deployed base stations. l is from The selected CP location index is used for base station deployment; when the binary variable z l =1 indicates that a base station is deployed on the l-th CP, z l =0 indicates that no base station has been deployed at the l-th CP; the number of deployed base stations is determined by... cardinality To indicate;

[0015] S102, The path loss between user k and the l-th CP is calculated as follows:

[0016]

[0017] Among them, PL kl d represents the path loss between user k and the l-th CP. kl f is the distance between user k and the l-th CP. c It is the carrier frequency, p kl p represents the line-of-sight (LOS) probability. kl =1 indicates that there is a Loss of Path (LOS) between user k and the l-th CP, p kl =0 indicates that there is no Loss of Path (LOS) path between user k and the l-th CP; F L For shadow fading in the case of Loss, F N Shadow fading in the NLoS case;

[0018] S103, User k receives the received signal power S from the base station placed in the l-th CP. kl =P BS / PL kl , where P BS It is the base station's transmit power, PL kl The path loss between user k and the l-th CP; the signal-to-noise ratio (SNR) of the signal received by user k from the base station placed on the l-th CP. kl =S kl / P n P n This is the noise power received by the user;

[0019] S104. When the signal-to-noise ratio of a signal received by a user from a certain base station is greater than a preset threshold. SNR At that time, the user is considered reachable from the base station; the set of reachable users of the base station deployed on the l-th CP is represented as

[0020] S105. The base station selects the optimal user from its reachable user set to serve, which is the set of users covered and served by the base station deployed at the l-th CP. Among the binary variables This indicates that user k is being served by the base station deployed on the l-th CP. This indicates that user k is not served by the base station deployed on the l-th CP;

[0021] S106, Define coverage r c The percentage of users being served out of all users;

[0022] S107, The signal-to-interference-plus-noise ratio (SINR) of the signal received by user k from its serving base station. k for:

[0023]

[0024] in, This indicates that user k receives data from the location placed at position l. k The received signal power of a CP base station, which covers and serves user k; I k It is the power of interference signals from other base stations;

[0025] S108, Data transmission rate R of user k k =B·log2(1+SINR) k );

[0026] Where B is the bandwidth;

[0027] Average data transfer rate for all users

[0028] As a further optimization scheme for the 6G outdoor hotspot scenario network planning method based on finite backtracking described in this invention, deployment cost and base station downlink capacity constraints are introduced into the system model, constructing the 6G outdoor hotspot scenario base station deployment optimization problem as a multi-dimensional knapsack problem; specifically as follows:

[0029] The S201 6G outdoor hotspot scenario base station deployment optimization problem aims to improve coverage and average data transmission rate while limiting deployment costs and downlink capacity. It seeks to determine the optimal number and location of base stations. Assuming all deployed base stations use the same parameter configuration, the 6G outdoor hotspot scenario base station deployment optimization problem is structured as a multi-dimensional knapsack problem P1.

[0030]

[0031] Where λ is the weight for the trade-off between coverage and data transmission rate, r c For coverage, and R k It uses binary decision variables The calculated average data transfer rate and data transfer rate per user, ω l This is the cost of deploying a base station at the l-th CP, where Ω represents the total deployment budget, and C... max It is the maximum downlink capacity of the base station, max k Indicates taking The maximum value;

[0032] S202. Define a lower limit C for the downlink capacity of a base station. min This represents the minimum acceptable data transmission rate to satisfy basic network communication functions, and calculates the maximum number of users a base station can serve. Therefore, the base station downlink capacity limitation expression (4) simplifies to

[0033]

[0034] S203. Rewrite P1 as a simplified multidimensional knapsack problem P2.

[0035]

[0036] st (2), (3), (5), (6), (7).

[0037] As a further optimization scheme of the 6G outdoor hotspot scenario network planning method based on finite backtracking described in this invention, based on a constructed multi-dimensional knapsack problem, a finite backtracking dynamic programming algorithm incorporating user association mechanisms is used to solve the optimal base station deployment scheme; including:

[0038] S301 and P2 are divided into a series of nested subproblems.

[0039]

[0040]

[0041] Among them, Pmn This is a subproblem where, given a deployment budget of n, we are limited to selecting the optimal base station deployment scheme from the first m CPs, where m = 0, ..., L and n = 0, ..., Ω. F is the optimization objective function of the subproblem. mn The subproblem P is to select the optimal base station deployment scheme from the first m CPs, given a deployment budget of n. mn The objective function value;

[0042] S302, Define the cost of deploying a base station for each CP {ω l Let ω be a vector composed of}, define the backtracking depth as d, and set the state transition flag γ to indicate whether the current state has undergone a valid transition;

[0043] S303, Define the base station deployment optimization sub-problem P mn The objective function value is F mn Base station deployment optimization sub-problem P mn The set of deployed base stations is Initialize all state values, i.e., when m=0 and n=0, we have F mn ←0, Simultaneously initialize the service binary decision matrix between the base station and the user. It is an empty set;

[0044] S304. Use the finite backtracking dynamic programming algorithm of the converged user association mechanism to solve for the optimal base station deployment scheme, let m=1, n=1;

[0045] S305, Begin solving the base station deployment optimization subproblem P mn Initialize the state transition flag γ = 0;

[0046] S306, Update F mn ←F m′n , Where F m′n For subproblem P m'n The corresponding objective function value, For subproblem P m'n The corresponding set of deployed base stations, P m'n It is a subproblem of selecting the optimal base station deployment scheme from the first m′ = m-1 CPs, given a deployment budget of n.

[0047] S307, Determine Budget Constraints ω m Check if <n is true; if true, execute steps S308-S314 sequentially; if false, execute step S315.

[0048] S308. Define a temporary variable for the storage base station set. and assign values For subproblem P m'n' The corresponding set of deployed base stations, P m'n' The deployment budget is n′=n-ω m Given the condition that the optimal base station deployment scheme is selected from the first m′ = m-1 CPs, the subproblem is ω m It is the cost of deploying a base station at the m-th CP; then the reachable user set is obtained. The reachable user set of the base station deployed at the l-th CP;

[0049] S309. For each base station placed in the l-th CP that is not fully loaded, i.e. First, users who are not yet connected to any base station are selected from the set of reachable users placed at the l-th base station that is not fully loaded. Then, they are sorted in descending order of signal-to-noise ratio. Let k be the binary decision variable between user k and the j-th CP; then, from the sorted users, select the remaining number of connections that can be made by placing the l-th CP at an unloaded base station. These users constitute the candidate connection user set C of the base station. l , This represents the number of users currently served by the base station that is not at full capacity in the l-th CP, where Let user i be the binary decision variable between the l-th CP; finally, the one belonging to CP will be... l The user's associated binary decision variable is set to 1, i.e. k∈C l ;

[0050] S310, for users connected to multiple base stations, i.e. Among all the base stations connected to the user, select the base station that provides the highest signal-to-noise ratio. l k To provide the base station with the maximum signal-to-noise ratio for user k, SNR kl Let the signal-to-noise ratio (SNR) of the signal received by user k from the base station located at the l-th CP be denoted as ; then, the associated binary decision variables for the remaining base stations are set to 0, i.e.

[0051] S311. Repeat steps S309 and S310 until either of the following conditions is met: First, the downlink capacity of all deployed base stations is full. Alternatively, all users may already be connected, or the remaining users may be unable to connect to any base station.

[0052] S312, Store the temporary variable of the base station set. and its corresponding binary decision matrix Substituting into equation (1), we obtain the optimized objective function value F′;

[0053] S313. Determine whether F′ > F. m′n′ Whether it is true or not, F m′n′ For subproblem P m'n' The corresponding objective function value, P m'n' The deployment budget is n′=n-ω m Given the condition that the optimal base station deployment scheme is selected from the first m′ = m-1 CPs, the subproblem is ω m This is the cost of deploying a base station at the m-th CP.

[0054] S314. If the judgment in step S313 is true, set the state transition flag variable γ to 1; if the judgment in step S313 is false, repeat the finite depth backtracking until... Each element in the array is attempted to be removed once; where finite depth backtracking refers to: updating Try from one by one Remove one element from the set, construct a new set of base stations, and perform steps S308-S311 to update the binary decision matrix. Substitute it into formula (1) to calculate the optimization objective function value F′; if the result of a certain calculation satisfies that F′ is greater than F m′n′ If the finite depth backtracking stops, the state transition flag variable γ is set to 1.

[0055] S315. Determine whether γ = 1 is true. If it is true, update F. mn ←F′,B mn ←B′;

[0056] S316. Determine whether the current deployment budget has reached the maximum budget limit, i.e., whether n < Ω is true;

[0057] S317. If the judgment in step S316 is true, then let n = n + 1 and return to step S304, repeating step S304-S316; if the judgment in step S316 is false, it means that all budget states corresponding to the current m have been traversed. At this time, let n = 1 and determine whether m < L is true.

[0058] S318. If the judgment in step S317 is true, then let m = m + 1, and return to step S304, repeating step S304-S317.

[0059] S319. If the judgment in step S317 is not true, the finite backtracking dynamic programming algorithm with integrated user association mechanism has been executed, and the final base station deployment scheme is obtained. At this time, m = L and n = Ω.

[0060] As a further optimization scheme for the 6G outdoor hotspot scenario network planning method based on finite backtracking described in this invention, the performance of the finite backtracking dynamic programming algorithm that integrates user association mechanism is simulated and analyzed based on two indicators: coverage rate and average data transmission rate per user.

[0061] As a further optimization scheme for the 6G outdoor hotspot scenario network planning method based on finite backtracking described in this invention, the performance of the finite backtracking dynamic programming algorithm incorporating user association mechanisms is simulated and analyzed based on two indicators: coverage rate and average user data transmission rate; including:

[0062] S401. With a fixed user density and a changing deployment cost budget, the finite backtracking dynamic programming algorithm that integrates user association mechanisms is compared with the Hooke-Jeeves algorithm, the greedy algorithm, and the random deployment algorithm to evaluate its performance in terms of coverage and data transmission rate.

[0063] S402. Under the condition of fixed deployment cost budget, change the user density and compare and analyze the coverage and data transmission rate performance of the finite backtracking dynamic programming algorithm with user association mechanism, Hooke-Jeeves algorithm, greedy algorithm and random deployment algorithm under different densities.

[0064] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0065] This invention employs a finite backtracking dynamic programming algorithm that integrates user association mechanisms. Based on a knapsack-like optimization framework, it efficiently determines the optimal number and location of base stations, significantly improving network deployment efficiency and overall performance. While ensuring over 95% network coverage, the proposed algorithm not only achieves higher data transmission rates but also significantly reduces the economic cost of base station deployment. The algorithm exhibits good robustness to changes in user density, making it particularly suitable for deployment requirements in 6G outdoor hotspot scenarios. Even in high-density user environments, it effectively alleviates downlink overload in hotspot areas, increasing the average user data transmission rate by over 10%. Attached Figure Description

[0066] Figure 1 This is a flowchart of the method of the present invention;

[0067] Figure 2 This is a schematic diagram of a 6G outdoor hotspot scenario in Embodiment 1 of the present invention;

[0068] Figure 3 This is a graph showing the maximum allowed number of base stations to be deployed versus the actual number of base stations deployed in Embodiment 1 of the present invention.

[0069] Figure 4This is a graph showing the maximum allowed number of base stations to be deployed versus coverage / data transmission rate in Embodiment 1 of the present invention, wherein (a) is a schematic diagram of the change in coverage performance with the maximum allowed number of base stations to be deployed, and (b) is a schematic diagram of the change in data transmission rate performance with the maximum allowed number of base stations to be deployed.

[0070] Figure 5 This is a user density-coverage / data transmission rate curve in Embodiment 1 of the present invention, wherein (a) is a schematic diagram of the change in coverage performance with user density, and (b) is a schematic diagram of the change in data transmission rate performance with user density. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] Example 1:

[0073] See Figure 1 This embodiment provides a 6G outdoor hotspot scenario network planning method based on finite backtracking, specifically including the following steps:

[0074] Step S1: Establish a system model for outdoor hotspot scenarios.

[0075] Specifically, in this embodiment, step 1 includes:

[0076] S101, such as Figure 2 As shown, 312.18 × 330.51 m 2 The outdoor hotspot scenario has several buildings, represented by geometric cubes. Base stations are restricted to being deployed only on building rooftops. The building rooftops are divided into grids with a density δ of 10m. The candidate base station location (CP) is located at the center of each grid. Users are randomly distributed outdoors, following a TCPP distribution. (User index set is also provided.) Represented as K represents the total number of users; CP index set L represents the total number of CPs; the set of indices for deployed base stations. l is from The selected CP location index is used for base station deployment; when the binary variable z l =1 indicates that a base station is deployed on the l-th CP, z l =0 indicates that no base station has been deployed at the l-th CP; the number of deployed base stations is determined by... cardinality To express.

[0077] S102. The path loss between user k and the l-th CP is calculated as follows, where the unit of path loss is dB:

[0078]

[0079] Where, d kl It is the distance between them, f c The carrier frequency is set to 2.4GHz, p kl p represents the line-of-sight (LoS) probability. kl =1 indicates that a Loss-of-Sight (LoS) path exists between user k and the l-th CP, while 0 indicates that no such path exists. The shadow fading in the LoS and non-line-of-sight (NLoS) cases are represented by F... L ~N(0,4) 2 ) and F N ~N(0,8.1) 2 ).

[0080] S103, The received signal power received by user k from the base station placed in the l-th CP is expressed as S kl =P BS / PL kl , where P BS The base station's transmit power is set to 30 dBm. Therefore, the signal-to-noise ratio (SNR) is calculated using the following formula:

[0081] SNR kl =S kl / P n

[0082] Among them, P n This is the noise power received by the user.

[0083] S104, Preset Threshold SNR Set to 30dB. When the signal-to-noise ratio of a user's received signal from a base station exceeds a preset threshold. SNR At that time, the user is considered reachable from the base station. The set of reachable users for the base station deployed on the l-th CP is...

[0084] S105. Because the downlink capacity of a base station is limited, it may not be able to serve all reachable users simultaneously. Therefore, the base station must select the optimal users to serve from its reachable user set. The set of users served (covered) by the base station deployed on the l-th CP can be represented as: Among the binary variables A value of 0 indicates that user k is served by the base station deployed on the l-th CP; otherwise, a value of 0 indicates that the user is not served.

[0085] S106. Coverage is defined as the percentage of users served out of all users, calculated as follows:

[0086]

[0087] S107, The signal-to-interference-plus-noise ratio (SIR) of the signal received by user k from its serving base station is:

[0088]

[0089] in, It is the power of interference signals from other base stations.

[0090] S108, The calculation method for the data transmission rate of user k is as follows:

[0091] R k =B·log2(1+SINR) k )

[0092] Where B is the bandwidth, set to 20MHz. The average data transfer rate for all users can be calculated using the following formula:

[0093]

[0094] Step S2: Introducing deployment costs and base station downlink capacity limitations, the 6G outdoor hotspot scenario base station deployment optimization problem is constructed as a multi-dimensional knapsack problem.

[0095] Specifically, in this embodiment, step 2 includes:

[0096] The S201, 6G outdoor hotspot scenario base station deployment optimization problem aims to improve coverage and average data transmission rate while limiting deployment costs and base station downlink capacity. It seeks to determine the optimal number and location of base stations. Assuming all deployed base stations use the same parameter configuration, this multi-objective combined optimization can be constructed as a multi-dimensional knapsack problem P1:

[0097]

[0098] Here, λ represents the weight for the trade-off between coverage and data transmission rate. Pareto analysis shows that an optimal trade-off is achieved when λ = 0.5. ω l It is the cost of deploying a base station at the l-th CP, and the deployment cost of each CP is set to be equal, i.e., ω. l =1, At this point, the deployment cost budget Ω is equivalent to the maximum allowed number of base stations. C max This is the maximum downlink capacity of the base station, and its value is set to 2Gbps.

[0099] The base station downlink capacity constraint expression (4) in S202 and P1 is coupled with the dynamically changing data transmission rate during deployment, introducing unbearable computational complexity. Therefore, a lower limit C is defined. min Let C represent the minimum acceptable data transmission rate to meet basic network communication functions, and based on the typical rate requirements of high-definition video services, this lower limit C is... min The speed was set to 10Mbps. Then, the maximum number of users the base station could serve was calculated. That is, each base station can serve a maximum of 200 users simultaneously. Therefore, the base station downlink capacity limitation expression (4) can be simplified to:

[0100]

[0101] S203, P1 can be rewritten as

[0102]

[0103] st(2), (3), (5), (6), (7)

[0104] Step S3: Use the finite backtracking dynamic programming algorithm with integrated user association mechanism to solve for the optimal base station deployment scheme.

[0105] Specifically, in this embodiment, step 3 includes:

[0106] S301 and P2 can be divided into a series of nested subproblems.

[0107]

[0108] st(2), (3), (5), (6), (7)

[0109] Among them, P mn Given a deployment budget of n, this is a subproblem of selecting the optimal base station deployment scheme from the first m CPs, where m = 1, ..., L and n = 1, ..., Ω. It is the optimization objective function of the subproblem, where and R k It uses the binary decision variable {z} in the current iteration. l}、 The calculated average data transfer rate and data transfer rate per user.

[0110] S302, Define the cost of deploying a base station for each CP {ω l Let ω be the vector formed by}. Define the backtracking depth as d, and set the state transition flag γ to indicate whether a valid transition has occurred in the current state.

[0111] S303, Definition F mn and Store the base station deployment optimization sub-problem P separately mn The objective function value and the set of deployed base stations. Initialize all state values, i.e., when m=0 and n=0, F has... mn ←0, Simultaneously initialize the service binary decision matrix between the base station and the user.

[0112] S304. Start executing the finite backtracking dynamic programming algorithm of the integrated user association mechanism to solve the optimal base station deployment scheme, let m=1, n=1.

[0113] S305, Begin solving the base station deployment optimization subproblem P mn Initialize the state transition flag γ = 0.

[0114] S306, Update F mn ←F m′n , Where m′=m-1.

[0115] S307, Determine Budget Constraints ω m Is <n true? If true, then execute the following deployment and user association steps (S308-S315) sequentially; otherwise, skip this part and proceed to step S316.

[0116] S308. Define a temporary variable for the storage base station set. and assign values Where n′=n-ω m and obtain the reachable user set

[0117] S309. For each base station placed in the l-th CP that is not fully loaded, i.e. First, users who are not yet connected to any base station are selected from the reachable user set, and then sorted in descending order of signal-to-noise ratio (SNR). Then, the remaining number of users that can be connected to the base station is selected from the sorted users. These users constitute the candidate connection user set C of the base station. l Finally, it will belong to C. l The user's associated binary decision variable is set to 1, i.e. k∈C l .

[0118] S310, for users connected to multiple base stations, i.e. Among all the base stations connected to the user, select the base station that provides the highest signal-to-noise ratio. Then set the associated binary decision variables of the remaining base stations to 0, i.e.

[0119] S311. Repeat steps S309 and S310 until one of the following conditions is met: First, the downlink capacity of all deployed base stations is full. Second, all users have been connected, or the remaining users cannot connect to any base station.

[0120] S312, Set the current candidate base stations and its corresponding binary decision matrix Substituting into (1) for calculation, we obtain the optimized objective function value F′.

[0121] S313. Determine whether F′ > F. m′n′ Whether it is valid or not.

[0122] S314. If the judgment in step S313 is true, set the state transition flag variable γ to 1.

[0123] S315. If the judgment in step S313 is not true, perform a finite-depth backtracking: update. Then try from one by one. Remove one element from the set, construct a new set of base stations, and perform steps S308-S311 to update the binary decision matrix. Substitute it into (1) to calculate the optimization objective function value F′. This process (i.e., removing elements, calculating the binary decision matrix and its corresponding objective function value) is repeated until... Each element in the array is attempted to be removed once. If the result of a certain calculation satisfies F′ greater than F... m′n′ If the above operations are stopped, the state transition flag variable γ will be set to 1.

[0124] S316. Determine whether γ = 1 is true. If it is true, update F. mn ←F′,B mn ←B′.

[0125] S317. Determine whether the current deployment budget has reached the maximum budget limit, i.e., whether n < Ω is true.

[0126] S318. If the judgment in step S317 is true, then let n = n + 1, and return to step S304, repeating steps S304-S317.

[0127] S319. If the judgment in step S317 is not true, it means that all budget states corresponding to the current m have been traversed. At this time, let n = 1 and determine whether m < L is true.

[0128] S320. If the judgment in step S319 is true, then let m = m + 1, and return to step S304, repeating steps S304-S319.

[0129] S321. If the judgment in step S319 is not true, the finite backtracking dynamic programming algorithm with integrated user association mechanism has been executed, and the final base station deployment scheme is as follows: At this time, m = L and n = Ω.

[0130] Step S4: Based on two metrics, coverage and average user data transmission rate, the performance of the proposed algorithm is simulated and analyzed.

[0131] Specifically, in this embodiment, step 4 includes:

[0132] S401, fixed user density is 1.5×10 4 users / km 2 Under the condition that the deployment cost budget changes from 0 to 9, the proposed algorithm is compared with three commonly used base station deployment optimization algorithms, namely the Hooke-Jeeves algorithm, the greedy algorithm and the random deployment algorithm, and its performance in terms of coverage and data transmission rate is evaluated.

[0133] S402, the fixed deployment cost budget is 5, with a user density of 0.5×10 4 Change to 1.7 × 10 4 users / km 2 Under the given conditions, the proposed algorithm and three benchmark algorithms were compared and analyzed to assess their coverage and data transmission rate performance at different densities.

[0134] Figure 3 This shows the actual number of base stations deployed. As the maximum allowed deployment number Ω changes, the finite backtracking dynamic programming algorithm proposed in this invention converges to 4 base stations when Ω≥4. However, the actual deployment number of other algorithms increases linearly with the maximum allowed deployment number. This indicates that this invention can control the base station deployment cost to effectively solve for the optimal number of base stations to deploy.

[0135] Figure 4 This demonstrates a user density of 1.5 × 10⁻⁶. 4 users / km 2 At that time, the coverage and data transmission rate performance of the finite backtracking dynamic programming algorithm proposed in this invention are demonstrated. For example... Figure 4 As shown in (a), the Hooke-Jeeves algorithm and the greedy algorithm have similar covering performance, while the proposed finite backtracking dynamic programming algorithm converges when Ω=4, and its covering performance is improved by more than 12% compared with the former two. Figure 4As shown in (b), the finite backtracking dynamic programming algorithm exhibits the best data transmission rate performance, achieving an improvement of over 10% compared to the Hooke-Jeeves algorithm when Ω = 3. Furthermore, as Ω increases further, the performance gain reaches approximately 16%. These results demonstrate that the present invention can significantly improve the data transmission rate while maintaining coverage, verifying the effectiveness and superiority of the proposed algorithm.

[0136] Figure 5 This demonstrates the impact of varying user density on coverage data transmission rate performance when Ω=5. To verify the effectiveness of the user association mechanism, a finite backtracking dynamic programming algorithm without a user association mechanism is introduced for comparison. This algorithm employs a fixed user association strategy, where users connect to the nearest base station, without considering base station load balancing and interference suppression mechanisms. Figure 5 As shown in (a), the finite backtracking dynamic programming algorithm (with user association) maintains a high coverage rate of over 95% even with increasing user density, while the coverage rates of other comparative algorithms decrease significantly with increasing density. The coverage performance of the finite backtracking dynamic programming algorithm (without user association) is similar to that of the Hooke-Jeeves algorithm, but the former has a much lower time complexity. Figure 5 As shown in (b), the finite backtracking dynamic programming algorithm (with user association) improves data transmission rate performance by up to 70% in low-user-density scenarios, and maintains a gain of about 10% even in high-density scenarios; while the finite backtracking dynamic programming algorithm (without user association) is slightly inferior in terms of data transmission rate performance. These results verify the effectiveness of the user association mechanism proposed in this invention in improving system performance, and demonstrate that the finite backtracking dynamic programming algorithm incorporating the user association mechanism has good robustness and general applicability under different user densities, and is particularly suitable for the efficient base station deployment optimization problem in 6G outdoor hotspot scenarios.

[0137] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0138] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A network planning method for 6G outdoor hotspot scenarios based on finite backtracking, characterized in that, include: Establish a system model for outdoor hotspot scenarios; By introducing deployment costs and base station downlink capacity limitations into the system model, the optimization problem of 6G outdoor hotspot base station deployment is constructed as a multi-dimensional knapsack problem. Based on a constructed multidimensional knapsack problem, a finite backtracking dynamic programming algorithm with integrated user association mechanism is used to solve the optimal base station deployment scheme; By incorporating deployment costs and base station downlink capacity limitations into the system model, the optimization problem of 6G outdoor hotspot base station deployment is constructed as a multi-dimensional knapsack problem; specifically as follows: The S201, 6G outdoor hotspot scenario base station deployment optimization problem is to find the optimal number and location of base stations with the goal of improving coverage and average data transmission rate, under the constraints of deployment cost and base station downlink capacity. With all deployed base stations using the same parameter configuration, the base station deployment optimization problem in a 6G outdoor hotspot scenario is structured as a multidimensional knapsack-like problem. : ； s.t. ; ； ； ； ； in, It is a trade-off between coverage and data transfer rate. For coverage, and It uses binary decision variables The calculated average data transmission rate and the data transmission rate of user k, It is the cost of deploying a base station at the l-th CP. Indicates the total deployment budget, This is the maximum downlink capacity of the base station. Indicates taking The maximum value; , All are binary variables. For the total number of CP, Total number of users; S202. Define a lower limit for the downlink capacity of a base station. This represents the minimum acceptable data transmission rate to satisfy basic network communication functions, and calculates the maximum number of users a base station can serve. Therefore, the base station downlink capacity limitation expression is... Simplified to ； S203, will Rewritten as a simplified multidimensional knapsack problem ; ； ST type ,Mode ,Mode ,Mode ,Mode ; Based on two metrics, coverage and average data transmission rate per user, the performance of a finite backtracking dynamic programming algorithm that incorporates user association mechanisms is simulated and analyzed. Based on two metrics—coverage and average user data transmission rate—the performance of a finite backtracking dynamic programming algorithm incorporating user association mechanisms is simulated and analyzed; including: S401. With a fixed user density and a changing deployment cost budget, the finite backtracking dynamic programming algorithm that integrates user association mechanisms is compared with the Hooke-Jeeves algorithm, the greedy algorithm, and the random deployment algorithm to evaluate its performance in terms of coverage and data transmission rate. S402. Under the condition of fixed deployment cost budget, change the user density and compare and analyze the coverage and data transmission rate performance of the finite backtracking dynamic programming algorithm with user association mechanism, Hooke-Jeeves algorithm, greedy algorithm and random deployment algorithm under different densities.

2. The 6G outdoor hotspot scenario network planning method based on finite backtracking as described in claim 1, characterized in that, Establish a system model for outdoor hotspot scenarios, including: S101, In an outdoor hotspot scenario, there are several buildings, and the base station is limited to being deployed only on the rooftops of these buildings; the rooftops are arranged with density... The system is divided into several grids, with potential base station locations at the center of each grid. Candidate points (CP) are used to represent potential base station locations. Users are randomly distributed outdoors, and their distribution is represented by the Thomas cluster distribution TCPP; user index set. Represented as , Total number of users; CP index set , Total number of CPs; set of indices for deployed base stations. , From The selected CP location index is used for base station deployment; when the binary variable This indicates that a base station is deployed on the l-th CP. This indicates that no base station has been deployed at the l-th CP; the number of deployed base stations is determined by... cardinality To indicate; S102, The path loss between user k and the l-th CP is calculated as follows: ； in, This represents the path loss between user k and the l-th CP. It is the distance between user k and the l-th CP. It is the carrier frequency. For the line-of-sight (LoS) probability, This indicates that there is a Loss of Path (LoS) between user k and the l-th CP. This indicates that there is no Loss of Path (LoS) path between user k and the l-th CP. Shadow fading in the case of Loss of Speed ​​(LoS) Shadow fading in the NLoS case; S103, User k receives the received signal power from the base station placed in the l-th CP. ,in, It is the base station's transmission power. The path loss between user k and the l-th CP; the signal-to-noise ratio of the signal received by user k from the base station placed on the l-th CP. , This is the noise power received by the user; S104. When the signal-to-noise ratio of a signal received by a user from a certain base station is greater than a preset threshold. At that time, the user is considered reachable from the base station; the set of reachable users of the base station deployed on the l-th CP is represented as ; S105. The base station selects the optimal user from its reachable user set to serve, which is the set of users covered and served by the base station deployed at the l-th CP. Among them, binary variables This indicates that user k is being served by the base station deployed on the l-th CP. This indicates that user k is not served by the base station deployed on the l-th CP; S106, Define Coverage The percentage of users being served out of all users; S107, Signal-to-Interference-plus-Noise Ratio (SIR) of the Signal Received by User k from its Serving Base Station for: ； in, This indicates that user k receives data from the device placed at position 1. The received signal power of a CP base station, which covers and serves user k; It is the power of interference signals from other base stations; S108, Data transmission rate of user k ; in, It's bandwidth; Average data transfer rate for all users .

3. The 6G outdoor hotspot scenario network planning method based on finite backtracking according to claim 2, characterized in that, Based on a constructed multidimensional knapsack problem, a finite backtracking dynamic programming algorithm with integrated user association mechanism is used to solve the optimal base station deployment scheme; include: S301, Divided into a series of nested subproblems ； ST type ,Mode ,Mode ,Mode ,Mode ; in, This is a subproblem where, given a deployment budget of n, the optimal base station deployment scheme is selected from the first m CPs. , , It is the optimization objective function of the subproblem. This is a subproblem where, given a deployment budget of n, the optimal base station deployment scheme is selected from the first m CPs. The objective function value; S302, Define the cost of deploying each CP base station. The vector formed Define the backtracking depth as and set the state transition flag. This indicates whether a valid transition has occurred in the current state; S303, Define the base station deployment optimization sub-problem The objective function value is Base station deployment optimization sub-problem The set of deployed base stations is Initialize all state values, i.e. From time to time , Simultaneously initialize the service binary decision matrix between the base station and the user. , It is an empty set; S304. Execute the finite backtracking dynamic programming algorithm of the fused user association mechanism to solve for the optimal base station deployment scheme, and let... ; S305. Begin solving the base station deployment optimization subproblem. Initialize state transition flags ; S306, Update , ,in For subproblems The corresponding objective function value, For subproblems The corresponding set of base stations to be deployed, This is under the condition that the deployment budget is n, limited to the first... The subproblem of selecting the optimal base station deployment scheme from among the CPs; S307, Determining Budget Constraints Is it true? If true, then execute steps S308-S314 sequentially; if false, then execute step S315. S308. Define a temporary variable for the storage base station set. and assign values , For subproblems The corresponding set of base stations to be deployed, The deployment budget is Under certain circumstances, limited to the preceding The subproblem of selecting the optimal base station deployment scheme from among the CPs, where It is the cost of deploying a base station at the m-th CP; then the reachable user set is obtained. , The reachable user set of the base station deployed at the l-th CP; S309. For each base station placed in the l-th CP that is not fully loaded, i.e. First, select users who are not yet connected to any base station from the set of reachable users placed at the l-th base station that is not fully loaded. Then, sort them in descending order of signal-to-noise ratio. Let k be the binary decision variable between user k and the j-th CP; then, from the sorted users, select the remaining number of connections that can be made by placing the l-th CP at an unloaded base station. These users constitute the candidate connection user set of the base station. , This represents the number of users currently served by the base station that is not at full capacity in the l-th CP, where Let i be the binary decision variable between user i and the l-th CP; finally, the one belonging to The user's associated binary decision variable is set to 1, i.e. ; S310, for users connected to multiple base stations, i.e. Select the base station that provides the highest signal-to-noise ratio from all base stations already connected to the user. , To provide users The base station that provides the highest signal-to-noise ratio. Let the signal-to-noise ratio (SNR) of the signal received by user k from the base station located at the l-th CP be denoted as ; then, the associated binary decision variables for the remaining base stations are set to 0, i.e. ; S311. Repeat steps S309 and S310 until either of the following conditions is met: First, the downlink capacity of all deployed base stations is full. Alternatively, all users may already be connected, or the remaining users may be unable to connect to any base station. S312, Store the temporary variable of the base station set. and its corresponding binary decision matrix Substitution Calculate and obtain the value of the optimization objective function. ; S313. Judgment Whether it is valid, For subproblems The corresponding objective function value, The deployment budget is Under certain circumstances, limited to the preceding The subproblem of selecting the optimal base station deployment scheme from among the CPs, where This is the cost of deploying a base station at the m-th CP. S314. If the judgment in step S313 is true, change the state transition flag variable. Set to 1; if step S313 fails, repeat the finite depth backtracking until... Each element in the array is attempted to be removed once; where finite depth backtracking refers to: updating ; try in turn from Remove one element from the set, construct a new set of base stations, and perform steps S308-S311 to update the binary decision matrix. Substitute it into the formula Calculate the value of the objective function If a certain calculation result satisfies Greater than Then stop backtracking to a finite depth and change the state transition flag variable. Set to 1; S315, Judgment If true, update. , ; S316. Determine whether the current deployment budget has reached the maximum budget limit, i.e. Is it valid? S317. If the judgment in step S316 is true, then let Then return to step S304 and repeat steps S304-S316; if the judgment in step S316 is not true, it means that the current All corresponding budget states have been traversed, at this point let and judge Is it valid? S318. If the judgment in step S317 is true, then let Then return to step S304 and repeat steps S304-S317; S319. If the judgment in step S317 is not true, the finite backtracking dynamic programming algorithm with integrated user association mechanism has been executed, and the final base station deployment scheme is obtained. ,at this time , .

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