Base Station Location Optimization Research Articles

Optimal location of base stations for cellular mobile network considering changes in users locations

In the past few decades, the demand for cellular networks has grown tremendously due to the growing demand for data. However, the vast majority of the users are dynamic since they use their smart phones and tablets to connect to these networks when they move to one location to another to attend sport games or other events for example. The location of these events might not cover the large demand. In this paper, we address the classical problem of locating base stations for a mobile cellular network to serve mobile users in a given geographical area considering the users' movements within the network. 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 and operation of base stations). This paper considers the COST-231-Walfisch-Ikegami propagation model for the path loss calculation with a non-line-of-site situation. To demonstrate the capability of the proposed model, it is experimented with a real-world problem on the Red Sea coast of Saudi Arabia. The model was capable of finding the optimal base station locations with minimum installation and operational costs considering the capacity and quality of service constraints.

Optimal Location of Cellular Base Stations via Convex Optimization: An Analytical Framework and Numerical Algorithms

Optimal Location of Cellular Base Stations via Convex Optimization: An Analytical Framework and Numerical Algorithms

A framework for the optimal deployment of police drones based on street-level crime risk

Drones are increasingly adopted for policing in many countries, as they can aid police officers to detect hazards and respond to incidents with timely and low-cost services. However, the planning and deployment of police drones are subject to several challenges, including the proper distance metric for drone flying and the risk-based location optimisation of drone base stations. This study proposes a new framework that enables the optimal deployment of police drones to address crime risk issues on urban street networks. This risk-based decision framework takes into account three potential distance metrics that regulate and shape the flying routes of drones, which in turn affects the optimal location of drone base stations. In addition, this framework takes into account the major risk constraints of flying drones in urban areas, including domestic privacy and elevation. The proposed risk-based decision framework is validated using the real case study of Liverpool with historical crime data and street network layouts. The findings contribute to the operations and management of police drones in urban areas and shift the paradigm of policing drones towards a risk-based regime.

The optimal 5G base station location of the wireless sensor network considering timely reliability

The optimal 5G base station location of the wireless sensor network considering timely reliability

FORMALIZATION OF THE PROBLEM FOR OPTIMAL PLACEMENT OF BASE STATIONS OF CELLULAR COMMUNICATION

The article discusses the issue of the optimal location of base stations for mobile communications. A method for finding the optimal location of base stations, determining their throughput in accordance with the expected traffic volume and determining the size of the cells will be considered. The purpose of the work is to formalize the question of the optimal location of base stations according to the criteria for minimizing costs for all network elements, taking into account the expected number of subscribers and the volume of traffic. As a result, a generalized algorithm for solving problems for systems is described, which allows one to formalize the objective function and constraints.

Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

Optimal Base Station Location for Network Lifetime Maximization in Wireless Sensor Network

Wireless sensor networks have attracted worldwide attention in recent years. The failure of the nodes is caused by unequal energy dissipation. The reasons that cause unequal energy dissipation are, first and foremost, the distance between the nodes and the base station, and secondly, the distance between the nodes themselves. In wireless sensor networks, the location of the base station has a substantial impact on the network’s lifetime effectiveness. An improved genetic algorithm based on the crossover elitist conservation genetic algorithm (CECGA) is proposed to optimize the base station location, while for clustering, the K-medoids clustering (KMC) algorithm is used to determine optimal medoids among sensor nodes for choosing the appropriate cluster head. The idea is to decrease the communication distance between nodes and the cluster heads as well as the distance among nodes. For data routing, a multi-hop technique is used to transmit data from the nodes to the cluster head. Implementing an evolutionary algorithm for this optimization problem simplifies the problem with improved computational efficiency. The simulation results prove that the proposed algorithm performed better than compared algorithms by reducing the energy use of the network, which results in increasing the lifetime of the nodes, thereby improving the whole network.

Red Deer and Simulation Annealing Optimization Algorithm-Based Energy Efficient Clustering Protocol for Improved Lifetime Expectancy in Wireless Sensor Networks

Energy stability is considered as the core challenge of Wireless Sensor Networks (WSNs) as it is the main factor that contributes towards improved network lifetime expectancy. Clustering with maximized energy efficiency is determined to be the well documented NP hard optimization solution that has the possibility of resulting in prolonged network lifetime. Diversified number of computational approaches that includes nature inspired metaheuristic algorithms, reinforcement schemes and evolutionary algorithms were used for attaining efficient clustering process in WSNs. In this paper, Red Deer and Simulation Annealing Optimization Algorithm-based Energy Efficient Clustering Protocol (RDSAOA-EECP) is proposed for improving lifetime expectancy and energy stability in WSNs. This clustering protocol is proposed with the global optimization capability of the red deer optimization algorithm and local optimization potential of simulation annealing. In specific, RDA is used for diversification and Simulated Annealing (SA) for intensification in order to establish balance between them during the clustering process in order to sustain energy stability. This RDSAOA-EECP protocol is proposed with SA as the search tool that prevents the phases of fighting and roaring phases of RDA involved in the intensification process. Moreover, this clustering protocol inherits the potentialities of the proposed metaheuristic properties in order to achieve optimal cluster heads together with the optimal base station location for the objective of enhancing energy efficiency. The simulation results of the proposed RDSAOA-EECP protocol guaranteed its potentiality over the baseline clustering approaches in improving network lifetime, throughput, packet deliver rate with minimized network latency and energy consumptions independent to the homogeneous and heterogeneous sensor network setup considered for implementation.

Proposed Base-Station Location Optimization with Genetic Algorithm Scheme for Lte Network Radio Planning

Long-term evolution (LTE) is used widely in inefficient network technologies which serve billions of users. These technologies have the following features like higher bandwidth, high spectrum efficiency and less latency. This paper proposes minimizing the cost of a cellular network, which usually includes the optimum selection and base-station locations that meet certain capacity and coverage constraints. In the LTE wireless network framework, coverage and capacity planning are interrelated in terms of interference. Moreover, the ever-increasing capacity demand for non-uniformly distributed users makes base station location optimization an important action. This paper adopts one type of improvement, which is a genetic algorithm-based methodology that optimally performs the task of base-stations location by minimizing the cost of the network while fulfilling the coverage and capacity criteria planning by using ATDI ICS TELECOM. Reducing 19.95% of the overlap of the area will result in good coverage of the proposed GA thus, improving efficiency of the network.

Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds

3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.

Two-agent cooperative search model with Petri nets

PurposeThe purpose of this paper is to propose a model for a two-agent multi-target-searching scenario in a two-dimensional region, where some places of the region have limited resource capacity in terms of the number of agents that can simultaneously pass through those places and few places of the region are unreachable that expand with time. The proposed cooperative search model and Petri net model facilitate the search operation considering the constraints mentioned in the paper. The Petri net model graphically illustrates different scenarios and helps the agents to validate the strategies.Design/methodology/approachIn this paper, the authors have applied an optimization approach to determine the optimal locations of base stations, a cooperative search model, inclusion–exclusion principle, Cartesian product to optimize the search operation and a Petri net model to validate the search technique.FindingsThe proposed approach finds the optimal locations of the base stations in the region. The proposed cooperative search model allows various constraints such as resource capacity, time-dependent unreachable places/obstacles, fuel capacities of the agents, two types of targets assigned to two agents and limited sortie lengths. On the other hand, a Petri net model graphically represents whether collisions/deadlocks between the two agents are possible or not for a particular combination of paths as well as effect of time-dependent unreachable places for different combination of paths are also illustrated.Practical implicationsThe problem addressed in this paper is similar to various real-time problems such as rescue operations during/after flood, landslide, earthquake, accident, patrolling in urban areas, international borders, forests, etc. Thus, the proposed model can benefit various organizations and departments such as rescue operation authorities, defense organizations, police departments, etc.Originality/valueTo the best of the authors’ knowledge, the problem addressed in this paper has not been completely explored, and the proposed cooperative search model to conduct the search operation considering the above-mentioned constraints is new. To the best of the authors’ knowledge, no paper has modeled time-dependent unreachable places with the help of Petri net.

Joint Optimization Framework for Operational Cost Minimization in Green Coverage-Constrained Wireless Networks

In this paper, we investigate the joint optimization of base station (BS) location, its density, and transmit power allocation to minimize the overall network operational cost required to meet an underlying coverage constraint at each user equipment (UE), which is randomly deployed following the binomial point process (BPP). As this joint optimization problem is nonconvex and combinatorial in nature, we propose a non-trivial solution methodology that effectively decouples it into three individual optimization problems. Firstly, by using the distance distribution of the farthest UE from the BS, we present novel insights on optimal BS location in an optimal sectoring type for a given number of BSs. After that we provide a tight approximation for the optimal transmit power allocation to each BS. Lastly, using the latter two results, the optimal number of BSs that minimize the operational cost is obtained. Also, we have investigated both circular and square field deployments. Numerical results validate the analysis and provide practical insights on optimal BS deployment. We observe that the proposed joint optimization framework, that solves the coverage probability versus operational cost tradeoff, can yield a significant reduction of about 65% in the operational cost as compared to the benchmark fixed allocation scheme.

3D WLS hybrid and non hybrid localization using TOA, TDOA, azimuth and elevation

In this paper, we propose a Three Dimensional (3D) Weighted Least Square (WLS) estimation of mobile position using Time Of Arrival (TOA), Time Difference Of Arrival (TDOA), Direction of Arrival (DOA) given by azimuth and elevation measurement. We present both hybrid and non hybrid localization techniques. Hybrid localization techniques use a combination of TOA, TDOA and DOA whereas non hybrid localization use only TOA or TDOA or DOA measurement. This is the first paper to tackle the problem of 3D hybrid localization using TOA, TDOA, azimuth and elevation. We also present a theoretical performance analysis of the hybrid localization technique where the covariance matrix of the WLS estimator is derived in closed form expression. The theoretical derivation is valid for any distribution of the observation noise. We show that the WLS estimator is unbiased and equivalent to the Maximum Likelihood estimator when the observation noise is Gaussian. We also optimize the localization accuracy by optimizing the base station location in order to minimize the Mean Square Error. Base station location optimization to enhance localization accuracy has not been yet proposed.

Optimal base station location in LTE heterogeneous network using non-dominated sorting genetic algorithm II

The main objective of the radio network planning is to provide a cost-effective solution for the radio network in terms of coverage, capacity and quality of service. The network planning process and design criteria vary from region to region depending on the dominant factor, which could be capacity or coverage. However, base stations deployment optimisation is an important and crucial process in cellular network planning. It represents a major challenge for mobiles operators and considered as NP-hard problem. In this work, we study the placement of base station and configuration with an optimisation approach. In addition, a mathematical model based on set covering problem is suggested to solve the BS positioning. The main objective of the model is to maximise the coverage and minimising the financial cost. Non-Dominate Sorting Genetic Algorithm (NSGA II) is applied to find a suitable solution. Simulation results and discussions on the performance of suggested algorithm are provided.

Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks

Efficient clustering is a well-documented NP-hard optimization problem in wireless sensor networks (WSNs). Variety of computational intelligence techniques including evolutionary algorithms, reinforcement learning, artificial immune systems and recently, artificial bee colony (ABC) metaheuristic have been applied for efficient clustering in WSNs. Due to ease of use and adaptive nature, ABC arose much interest over other population-based metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to comparably poor exploitation cycle and requirement of certain control parameters. Thus, we propose an improved artificial bee colony (iABC) metaheuristic with an improved solution search equation to improve exploitation capabilities of existing metaheuristic. Further, to enhance the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student’s t-distribution, which require only one control parameter to compute and store and therefore increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements; moreover, the use of first-of-its-kind compact Student’s t-distribution makes it suitable for limited hardware requirements of WSNs. Additionally, an energy-efficient clustering protocol based on iABC metaheuristic is presented, which inherits the capabilities of the proposed metaheuristic to obtain optimal cluster heads along with an optimal base station location to improve energy efficiency in WSNs. Simulation results show that the proposed clustering protocol outperforms other well-known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.

Multi-agent target searching with time constraints using game-theoretic approaches

PurposeThe purpose of this paper is to propose a model for a target searching problem in a two-dimensional region with time constraints. The proposed model facilitates the search operation by minimizing the mission time and fuel usage, and the search operation is performed by a set of agents divided into a number of groups.Design/methodology/approachThe authors have applied optimization techniques, Cartesian product, inclusion–exclusion principle, cooperative strategy, Shapley value, fuzzy Shapley function and Choquet integral to model the problem.FindingsThe proposed technique optimizes the placement of base stations that minimizes the sortie length of the agents. The results show that the cooperative strategy outperforms the non-cooperative strategy. The Shapley values quantify the rewards of each group based on their contributions to the search operation, whereas the fuzzy Shapley values determine the rewards of each group based on their contributions and level of cooperation in the search operation.Practical implicationsThe proposed model can be applied to model many real-time problems such as patrolling in international borders, urban areas, forests and managing rescue operations after natural calamities, etc. Therefore, defence organizations, police departments and other operation management sectors will be benefitted by applying the proposed approach.Originality/valueTo the best of the authors’ knowledge, determining the optimal locations of base stations in a region is not explored in the existing works on target searching problems with fuel constraints. The proposed approach to cooperatively search the targets in a region is new. Introducing the Shapley function and fuzzy Shapley function is a novel idea to quantify the rewards of each group based on their contributions and level of cooperation in the search operation. This paper addresses these unexplored areas.

Increasing the Responsiveness of Firefighter Services by Relocating Base Stations in Amsterdam

In life-threatening situations where every second counts, the timely presence of firefighter services can make the difference between survival and death. Motivated by this, in collaboration with Fire Department Amsterdam-Amstelland in the Netherlands, we developed a mathematical programming model for determining the optimal locations of the vehicle base stations, and for optimally distributing firefighter vehicle types over the base stations. The model is driven by practical considerations. It (1) allows for fixing any subset of existing base locations that cannot be relocated (e.g., for historical reasons); (2) includes multiple vehicle types, each of which may have a type-dependent response-time target; and (3) includes crews that consist of arbitrary mixtures of professional (i.e., career) and volunteer firefighters. Extensive analysis of a large data set obtained from the Fire Department Amsterdam-Amstelland demonstrates: (1) that a reduction of over 50 percent in the fraction of firefighter late arrivals can be realized by relocating only three of the current 19 base locations; and (2) that adding new base locations to improve performance is unnecessary: optimization of the locations of the current base stations is as effective, and saves money. The results show an enormous potential for substantially reducing the fraction of late arrivals of firefighter services, with little investment in relocating a small number of stations.

Scalable Route Map for Advanced Metering Infrastructure Based on Optimal Routing of Wireless Heterogeneous Networks

This article presents a scalable route map for the least cost deployment of wireless heterogeneous networks that support traffic from the advance metering infrastructure (AMI). We first explore the performance of a common scenario in which a single technology is employed to connect smart meters sending traffic to the utility. Based on simulations with actual city maps, we study the coverage provided to smart meters by an LTE cellular network. In order to improve the coverage, an optimization model that considers network capacity and range is proposed to determine the optimal location of base stations to achieve a target coverage of smart meters. According to these preliminary results with a single access technology, we propose an evolved network architecture that considers several alternatives of wireless heterogeneous networks to guarantee the coverage to smart meters with the least use of resources. We introduce a heuristic model that involves elements from base stations, universal data aggregation points, number of smart meters, and an optimal routing to achieve the desired connectivity from the group of smart meters. We employ geo-referenced models to consider actual characteristics of cities as well as geographical conditions. Results from the evolved model demonstrate that by combining technologies and employing data aggregation points with optimized localizations, the network is able to achieve a target coverage of smart meters with a reduced cost in terms of technological resources.

Comparison of Radio Wave Propagation Models for Mobile Networks

Heterogeneous cellular networks are gaining momentum in industry and the research community, and are attracting the attention of standard bodies such as 3GPP LTE and IEEE 802.16j, whose objectives are to increase the capacity and coverage of cellular networks. In this article, we provide an overview of expansion strategies, optimal locations of base stations with different characteristics, and radio-planning models.

Base Station Location -Aware Optimization Model of the Lifetime of Wireless Sensor Networks

Recently, wireless sensor networks (WSNs) have been progressively applied in various fields and areas. However, its limited energy resources is indisputably one of the weakest point that strongly affects the network's lifetime. A WSN consists of a sensor node set and a base station. The initial energy of each sensor node will be depleted continuously during data transmission to the base station either directly or through intermediate nodes, depending on the distance between sending and receiving nodes. This paper consider determining an optimal base station location such that the energy consumption is kept lowest, maximizing the network's lifetime and propose a nonlinear programming model for this optimizing problem. Our proposed method for solving this problem is to combine methods mentioned in [1] respectively named the centroid, the smallest total distances, the smallest total squared distances and two greedy methods. Then an improved greedy method using a LP tool provided in Gusek library is presented. Finally, all of the above methods are compared with the optimized solution over 30 randomly created data sets. The experimental results show that a relevant location for the base station is essential.