A Coverage-Based Location Approach and Performance
This paper presents an approach for the deployment of 5G base stations under the considerations of both the cost and the signal coverage. We formulate an optimization problem
This paper presents an approach for the deployment of 5G base stations under the considerations of both the cost and the signal coverage. We formulate an optimization problem
Based on the principle of priority business volume and the cost performance of base station, this paper establishes a set of models to solve the site selection planning problem of urban base
In this paper, we summarize the following conclusions obtained by different scholars in different application scenarios by querying the relevant literature on rational
We employ a simulated annealing algorithm to determine the number of new base stations needed. After rigorous analysis, our optimal solution suggests deploying 131 micro and 19
presents a following method: location selection and network optimization for the wireless communication network. First, it collects the experimental data set of base station locati.
Through the analysis of base station layout in cellular networks, using Geometric Dilution of Precision (GDOP) as the optimization objective, we propose a Dynamic Base
Based on the principle of priority business volume and the cost performance of base station, this paper establishes a set of models to
Firstly, this paper outlines the site selection issues for communication base stations, considering the varying communication needs of users and constructs a site selection
Based on the principle of priority business volume and the cost performance of base station, this paper establishes a set of models to solve the site selection planning
This method enables the system to dynamically select the positioning base station when positioning target in the detection area. DBSS mainly include three steps: nearest base station
We developed a mixed integer programming model to provide the optimal location of base stations at different time periods with the network''s minimum total cost (i.e., installation
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We employ a simulated annealing algorithm to determine the number of new base stations needed. After rigorous analysis, our optimal solution suggests deploying 131 micro and 19 macro base stations, with a total cost of 321. References is not available for this document.
Some studies optimize the location of BSs in wireless communication networks through exact solution approaches such as mixed integer linear programs (MILP) and algorithmic approaches , , .
Hence, additional base stations (BSs) may be needed to satisfy the new demand. This case addresses the application of dynamic permanent demand for service such as establishing a new residential area over several time periods where new demand clusters are created in each time period as the residential area expands.