Faced with the ever-increasing trend of the cellular network scale, how to quantitatively evaluate the effectiveness of the large-scale deployment of base stations (BSs) has become a challenging topic. To this end, a deeper understanding of the cellular network topology is of fundamental significance to be achieved. In this paper, @math -Shape, a powerful algebraic geometric tool, is integrated into the analysis of real BS location data for six Asian countries and six European countries, respectively. Firstly, the BS spatial deployments of both Asian and European countries express fractal features based on two different testifying metrics, namely the Betti numbers and the Hurst coefficients. Secondly, it is found out that the log-normal distribution presents the best match to the cellular network topology when the practical BS deployment is characterized by the Euler characteristics.
In order to meet various performance requirements, substantial efforts have been made on how to effectively address the cellular network deployment issues. @cite_42 investigated the influence of cell size on the network energy consumption for the purpose of maximizing energy efficiency. In @cite_39 , an optimal approach was presented to prolong the lifetime of wireless sensor networks (WSNs) by deploying BSs appropriately. Stochastic geometry models were built in @cite_41 for BS deployments in shared cellular networks, and the proposed models were observed to be suitable for different countries. @cite_28 proposed to jointly optimize the placement of BSs with other factors like power control. @cite_43 studied the BS deployment scheme based on the overall traffic variation process rather than the peak traffic load, which can successfully bring energy consumption reduction and performance improvement. @cite_12 developed a greedy framework to arrange the BSs in heterogeneous cellular networks, so as to improve the energy efficiency. In the strive for meeting coverage demands and traffic requirements, the BSs were automatically planned and distributed in @cite_26 in mobile networks. From an energy reduction perspective, @cite_15 proposed a heuristic method to lay out the BSs in the WSNs.
@cite_26 @cite_41 @cite_28 @cite_42 @cite_39 @cite_43 @cite_15 @cite_12
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The optimal base station placement is a serious task in cellular wireless networks. In this paper we propose a method that automatically distributes the base stations on a studied scenario, maintaining coverage requirement and enabling the transmission of traffic demands distributed over the area. A city scenario with normal demands is examined and the advantages disadvantages of this method are discussed. The planner and optimizing tasks are based on an iterative K-Means clustering method. Numerical results of coverage, as well as graphical results permit on radio coverage maps, are presented as base for main consequences. Furthermore, the final results are compared to another simple planning algorithm. Stochastic models of base station infrastructure deployment by multiple mobile operators can be an invaluable tool for deriving fundamental results about wireless network sharing. In this paper, we study stochastic geometry models for a shared cellular network consisting of base stations deployed by multiple mobile operators, based on real cellular network data coming from three European countries. Relying on a statistical approach as well as the evaluation of wireless network performance metrics, we show that the log-Gaussian Cox process provides the most compelling fitness results with real multi-operator base station deployment patterns and a model that offers some degree of analytical tractability. The model captures the fact that, in urban areas, there is strong correlation between the locations where the base stations of different operators are deployed. In contrast to that, in rural areas we observe some repulsion between antenna locations of different operators. Moreover, we observe that the behavior which can be modelled with the help of these processes occurs over and over again for similar areas in different countries, which suggests universality of the proposed models. While the deployment of femtocell in residential buildings has firmly positioned it as a major performance leap in wireless communications, its deployment in commercial buildings remains under-explored. In commercial building environments, the femtocell base station (FBS) placement planning is particularly challenging due to the impact of the building size, layout, structure, and wall floor separation. In this paper, we study the problem of jointly optimizing FBS placement and power control in commercial building environments to prolong the battery life for mobile handsets. We first propose a mathematical model that captures the unique building features. Based on this model, we employ a set of novel transformation strategies to formulate the FBS placement problem as a mixed-integer convex program (MICP). Accordingly, we propose an effective global optimization algorithm based on using the convex relaxation of the formulated MICP within a branch-and-bound framework. This approach guarantees finding a global optimal solution. To demonstrate the efficacy of our algorithm, we conduct extensive numerical studies. Our proposed model and optimization approach offer useful insights into femtocell deployments in commercial buildings. Energy efficient radio networks, i.e. radio base stations and transport network mainly, are something that becomes more and more important with the increase of installed base stations globally. This study evaluates the total base station energy consumption per area ( @math ) for different cell sizes (deployments). This is done by theoretical calculations assuming a certain minimum received power at the cell edge regardless of cell size and channel model. This study shows that with just a minimal fixed power consumption overhead factor for each cell (e.g. 0.1 W per cell), it is never optimal to build a cellular network as dense as possible from an energy consumption per area point of view. Only when there is no fixed overhead at all, the optimal cell size from an energy per area perspective, is as small as possible. With a rather low fixed overhead (1-5 W), the optimal inter-site-distance is around 300-700 m. With the current (high) fixed power consumption overhead, as large inter-site-distances as possible are the most energy efficient. Sensor network lifetime is largely affected by the energy consumption for data transmission from sensor nodes to a base station. We generalize and solve the problems of deploying multiple base stations in sensor networks using one-hop and multi-hop communication models to maximize network lifetime. Under the one-hop communication model, the sensors far away from base stations always deplete their energy much faster than others. We propose an optimal solution for small-scale networks and a heuristic approach for large-scale ones based on the smallest enclosing circle algorithm that deploys a base station at the geometric center of each cluster. Under the multi-hop communication model, both the base station locations and the data routing scheme need to be considered in maximizing network lifetime. We propose an iterative algorithm based on rigorous mathematical derivations and use linear programming to compute the optimal routing path for benchmark purposes. Extensive simulation results show superior network lifetime performance of the proposed deployment algorithms in comparison with existing ones. Energy reduction is one of the major problems in the design of a wireless sensor network (WSN). Multiple base stations can be used to dramatically reduce the energy consumption of sensor nodes. We consider the following problem of deploying k base stations in a wireless sensor network: Given a wireless sensor network where the location of each sensor node is known, partition the whole sensor network into k disjoint clusters and place one base station for each cluster such that the maximum total energy consumption of any cluster is minimised. We propose the first heuristic for this problem. The time complexity of our heuristic is O(kn3), where n is the number of sensor nodes of the sensor network. In the special case where k is equal to 1, we propose a quadratic-time algorithm for optimally deploying the base station. Our simulation results show that our heuristic is efficient. In this paper, we develop an energy-efficient base station deployment framework for heterogeneous networks. Micro base station deployment increases the total capacity of the network. However, increasing the number of micro base stations excessively may reduce the energy efficiency of the network. Therefore, in this paper, we examine the energy efficiency aspect of the micro base station deployment problem. This problem can be divided into two subproblems: choosing feasible candidate micro base station locations and selecting the optimum set of micro base stations among the candidate locations. The proposed algorithm first chooses the subset of the feasible locations as candidate locations, and then selects the micro base stations which maximize the energy efficiency of the network iteratively. It is shown that the proposed algorithm is a constant-factor approximation of the optimal solution. Our simulations demonstrate that proposed algorithm improves the energy efficiency of the network up to 12 for low-loaded scenarios and 98 for the high-loaded scenarios.