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Optimal base station location in LTE heterogeneous network using non-dominated sorting genetic algorithm II
Abstract
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.
... In this section, we analyse the impact of its parameters on coverage and their needs to solve the overshooting problem. Firstly, a cellular network can be constituted by several antennas types, namely omnidirectional (OD), large directional (LD) and small directional (SD) Ouamri et al., 2018). In our case, we only select OD and LD antennas. ...
... This constraint should allow one TP i to attach itself with another BS according to S q value. Normally, a mobile device with reception signals above S q = 90 dBm should only select the strongest signal (Ouamri et al., 2018). ...
Overshooting is undoubtedly the most ambitious challenge in planning cellular networks. This problem means the scope of antenna coverage exceeds that one intended, causing interference in adjacent cells. However, solving this problem remains a complex task for mobile operators. In this work, we propose a mathematical model underlying the overshooting problem in LTE network as well as a self-optimisation process using genetic algorithm based on Laplace crossover. The proposed algorithm aims to determine the appropriate antenna parameters in order to reduce a long-distance signal, which forms a discontinuous coverage area in other network sites. Numerical results of the performances of the proposed algorithm indicate that the effects of overshooting problem are reduced. Reference to this paper should be made as follows: Ouamri, M.A. and Azni, M. (2020) 'New optimisation method to minimise overshooting problem in LTE using genetic algorithms based on Laplace crossover', Int. Since then, he was involved in many research projects. Also, since 2005, he is involved in research cooperation with the Signal and Communication Department at TELECOM Bretagne, Brest, France. His main research interests include signal processing and source and channel coding in wireless mobile networks and computer systems. His is currently with the LIMED laboratory where his research interests focus on interference management in LTE-advanced networks and on wireless sensor networks.
... To perform cell planning, authors in [9] employed third generation Genetic Algorithm. Optimal base stations planning in LTE networks using Genetic Algorithm was introduced in [11]. The authors formulated the approach as a multi-objective problem, where the main objective aims to increase coverage while using a minimum number of BS. ...
... Once the initial procedure is completed, the algorithm proceeds to optimize with a Multi-Objective genetic algorithm. Our choice is based on the use of Non-dominate Sorting Genetic Algorithm II (NSGAII) applied in [11]. Proposed by [13], this algorithm is characterized by a fast non-dominated sorting approach, fast crowded distance estimation procedure and simple crowded comparison operator. ...
In cellular networks design, cell planning is the primary and most
important phase before the deployment of the network’s infrastructure.
Cell planning aims to determine the best Base Stations (BS)
placement in a given area, in order to meet traffic and coverage
requirements, and to minimize overshooting that may affect user
equipments. Overshooting consists in a signal from a given cell
forming a discontinuous coverage area in another adjacent cell. In
this paper, we develop a novel optimization method for BS planning.
Our objective is to reduce the number of deployed BS while
minimizing signal overshooting, under capacity constraint. In the
proposed approach, cell planning is formulated as a multi-objective
problem. Then, a non-dominate sorting genetic algorithm based
on Laplace Crossover is applied in order to solve the problem. The
proposed algorithm is implemented and simulations under real conditions
are conducted. The performance of the proposed algorithm
is compared with that of a real deployed network infrastructure.
The results obtained show that the number of required BS can be
reduced with a lower rate overshooting.
... The quantity of BSs that must ultimately be installed is determined by the best position for every BS, given all relevant traffic patterns. To locate BSs within LTE diverse systems, a non-dominated sorted GA (Amine et al. 2018) as well as a stochastic multiple-objective optimization approach were studied. ...
Many vehicle applications requiring large capacity, minimal latency, and excellent dependability are anticipated to be made possible by 5G. To help with this, 5G suggests using highly focused mmWave networking implementation, denser, more minor cell innovation, and various new sophisticated communications techniques to increase networking bandwidth, lower latency, and offer high dependability. Robust mobility managing strategies are needed in these networks to enable automotive connection wherein the points are very dynamic to reduce transmission costs and disruptions during periodic transfers. Engineers working on technology for communication have to tackle this significant obstacle to fulfil the potential of 5G networks enabling V2X and comparable purposes. Therefore, a new implementation strategy is essential to support 5G functionalities. A possible approach for the 5G system installation optimization challenge is predicted to involve using meta-heuristics. To reduce the number of basis stations (BSs) as well as optimize the assignments in millimetre wave (mmWave) frequency ranges (such as 28 GHz as well as 38 GHz) within the framework of such 5G system while fulfilling customer data rates requirements, a deployment regarding a meta-heuristic method depending upon swarming intelligence is presented in this paper. After that, a sequential procedure is used to eliminate unnecessary BSs.
... However, the advancement of mobile networks also encounters unprecedented challenges. The scarcity of spectrum resources and the constraints of network deployment costs pose significant obstacles to achieving extensive network coverage and site deployment in the short term [7,8]. Additionally, issues such as network duplication and high energy consumption impose higher requirements on the evolution of mobile communication technology [9,10]. ...
Radio resource allocation schemes are critical to enhance user experience and spectrum efficiency. In the context of fifth-generation (5G) and future networks, co-construction and sharing among multiple telecom operators, which effectively mitigate challenges stemming from resource scarcity, energy consumption, and network construction costs, also attract wide attention. Therefore, optimal resource allocation techniques in sharing networks should be explored. Current resource allocation schemes primarily optimize for load balancing, single-user throughput, and fairness of multi-user whole network throughput, with minimal consideration for network-level user experience. Moreover, existing approaches predominantly concentrate on specific resource domains, seldom considering holistic collaboration across all domains, which limits the user experience of the whole network. This paper introduces an innovative resource allocation method grounded in the Shannon theorem, incorporating time-frequency-spatial domain multi-dimensional collaboration. More importantly, by constructing an optimization model, we strive to attain optimal network-level user experience. Furthermore, we provide a smart grid technology based on the Artificial Intelligence (AI) method to predict inter-frequency information, including Received Signal Reference Power (RSRP), beam ID, and spectral efficiency, which are modeled as air interface utilization, channel bandwidth, and signal-to-noise ratio, respectively, providing input for the optimization algorithm, which seeks to achieve the optimal time-frequency-space resource allocation scheme. Extensive experimentation validates the effectiveness and superiority of our proposed methodology.
... For this purpose, a fitness value, related to the objective of the optimization problem, is the key criterion for ranking the individuals. A chromosome with a larger fitness value has high probability of resulting in an optimal solution to the problem [37,38]. ...
The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively.
... Base station placement and configuration with an optimization approach has been studied by Amine et al. [29]. A mathematical model based on the set coverage problem was proposed to solve the base station positioning, with the main objectives of maximizing the coverage and minimizing the financial cost. ...
The problem of insufficient signal coverage of 5G base stations can be solved by building new base stations in areas with weak signal coverage. However, due to construction costs and other factors, it is not possible to cover all areas. In general, areas with high traffic and weak coverage should be given priority. Although many scientists have carried out research, it is not possible to make the large-scale calculation accurately due to the lack of data support. It is necessary to search for the central point through continuous hypothesis testing, so there is a large systematic error. In addition, it is difficult to give a unique solution. In this paper, the weak signal coverage points were divided into three categories according to the number of users and traffic demand. With the lowest cost as the target, and constraints such as the distance requirement of base station construction, the proportion of the total signal coverage business, and so on, a single objective nonlinear programming model was established to solve the base station layout problem. Through traversal search, the optimal threshold of the traffic and the number of base stations was obtained, and then, a kernel function was added to the mean shift clustering algorithm. The center point of the new macro station was determined in the dense area, the location of the micro base station was determined from the sca ered and abnormal areas, and finally the unique optimal planning scheme was obtained. Based on the assumptions made in this paper, the minimum total cost is 3752 when the number of macro and micro base stations were determined to be 31 and 3442 respectively, and the signal coverage rate can reach 91.43%. Compared with the existing methods, such as K-means clustering, K-medoids clustering, and simulated annealing algorithms, etc., the method proposed in this paper can achieve good economic benefits; when the traffic threshold and the number of base stations threshold are determined, the unique solution can be obtained.
... Recently, meta-heuristics prove their performance in many recent fields like optimization of neural networks [4], cellular network planning [2], and WSN deployment [16]. In this work, we use a recent meta-heuristic called Whale Optimization Algorithm (WOA) [11], which proved its performance in recent works on the deployment of WSN such as in [13] and [8]. ...
Smart Car Parks (SCPs) based on Wireless Sensor Networks (WSNs) are one of the most interesting Internet of Things applications. This paper addresses the deployment optimization problem of two-tiered WSNs dedicated to fire monitoring in SCPs. Networks deployed inside the SCP consist of three types of nodes: Sensor Nodes (SNs) which cover the spots within the parking area, Relay Nodes (RNs) which forward alert messages generated by SNs, and the Sink node which is connected to the outside world (e.g, firefighters), through a high bandwidth connection. We propose an algorithm based on chaos theory and Whale Optimization Algorithm (WOA), which minimizes simultaneously the deployed number of SNs, RNs, and network diameter while ensuring coverage and connectivity. To evaluate the effectiveness of our proposal, we have conducted extensive tests. The results show that the Chaos WOA (CWOA) outperforms the original WOA in terms of solution quality and computation time and by comparison with an exact method, CWOA provides results very close to the optimal in terms of fitness value and is efficient in terms of computational time when the problem becomes more complex.
... Reference [16] proposes a green cell planning scheme based on a stochastic approach to minimize the number of deployed BSs, where the final number of BSs to be deployed derives from the optimal location of each BS under all considered traffics pattern. A non-dominated sorting GA [17] and an evolutionary multi-objective optimization algorithm [18] were investigated to find the locations of BSs in LTE heterogeneous networks. The authors in [19] investigate a metaheuristic algorithm for the 5G hyper-dense deployment problem and the proposed search economics algorithm divides the search space into a set of subspaces to determine the location of BSs. ...
It can be predicted that the infrastructure of the existing wireless networks will not fill the requirement of the fifth generation (5G) wireless network due to the high data rates and a large number of expected traffic. Thus, a novel deployment method is crucial to satisfy 5G features. Meta-heuristic is expected to be a promising method for the complex deployment optimization problem of the 5G network. This work presents an implementation of a meta-heuristic algorithm based on swarm intelligence, to minimize the number of base stations (BSs) and optimize their placements in millimeter wave (mmWave) frequencies (e.g., 28 GHz and 38 GHz) in the context of the 5G network while satisfying user data rates requirement. Then, an iterative method is applied to remove redundant BSs. We formulate an optimization problem that takes into account multiple 5G network deployment scenarios. Further, a comparative study is conducted with the well-known simulated annealing (SA) using Monte Carlo simulations to assess the performance of the developed model. In our simulation results, we divide the region of interest into two subareas with different user distributions for different network scenarios while considering the intercell interference. The results demonstrate that the proposed approach has better network coverage with low percentage users in outage. In addition, the developed approach has less computational times to reach the desired target network quality of service (QoS).
The 5G mobile network is a kind of critical information infrastructure for future Internet of Things. Due to its rapid development, the planning and deployment of 5G network base stations is a more urgent and meaningful problem than ever before from the aspect of optimization. Because the purpose of establishing a 5G base station is to make its wireless signal cover as many areas as possible, a 0–1 programming model with two optimal objectives is proposed for 5G base stations based on the cover set. Then after analyzing the difficulties in solving the problem expressed by above model, employing the idea of divide and conquer and the strategies of parallelization, a scheme based on Binary Particle Swarm Optimization (BPSO) algorithm is proposed to solve this problem. In the first step of the scheme, the partition processing based on coordinate position is considered by the way of dividing the target area into many smaller sub-areas with the algorithm of Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH). The next step is to use BPSO algorithm improved by sparse particles initialization and a suppressor factor to solve each small sub-area with the parallel computing and the memory mapping technology. The proposed approach has been tested in several experiments and the experimental results demonstrate its effectiveness.Keywords5G base station deploymentCover setBi-objective optimal schemeBinary particle swarm optimization (BPSO)
The optimum location of base-stations that meet certain coverage and capacity constraints is a major step in dimensioning a cellular network. The increasing capacity demand and mixed cell scenarios (relay nodes, microcells) make base-station location in LTE systems non trivial and call for sophisticated optimization methods. This paper proposes the use of a multi-objective genetic algorithm (NSGA-II) that fulfills three criteria related to coverage, capacity and total network cost. We used various setups for capacity and the heterogeneity of the network to optimize, to study the algorithm's behavior and show its convergence. Using results obtained, we show a tendency for choosing microcells in scenarios of increased capacity, discuss the pareto-front solutions of the algorithm and present SINR performance for a mixed-cell scenario.
This paper presents the novel access network planning model for Wireless Interoperability for Microwave Access. The proposed model aims to assign optimize amount and location of Base Stations in study area. Integer Linear Programming is used for formulating optimization problem which objectives are minimize installation cost and maximize service coverage simultaneously. Numerical network planning result demonstrate that proposed model can achieve overall service area and can efficient serve almost service area in case of budget limitation.
In this paper, dynamic traffic load is considered to determine optimal location of base station (BS) using evolutionary optimization algorithms. The various parameters such as site coordinates (x, y), transmitting power, height and tilt are taken as design parameters for BS placement. Coverage maximization and cost minimization are considered as two conflicting objectives with inequality constraints such as handover, traffic demand and overlap. RGA and MNSGA-II algorithms are used to solve single objective and multiobjective BS placement problem respectively. A 2×2 km2 synthetic test system is discretized as hexagonal cell structure for simulation purposes. Receiving field strength for all service testing points is calculated using simulations and path loss is calculated using Hata model. In dynamic traffic model, both vehicle and pedestrian movements in up and side directions are considered. Dynamic movement is achieved by randomly moving vehicles and pedestrians for a fixed speed in each sample time. The results show that the RGA is able to determine the optimal BS location after considering the dynamic traffic load and satisfying inequality constraints for both coverage maximization and cost objectives. MNSGA-II algorithm gives well distributed pareto-front for the multiobjective BS placement in single simulation run. The simulation results reveal that the proposed dynamic traffic model is suitable for the real world BS placement problem.
The placement of antennas is an important step in the design of mobile radio networks. We introduce a model for the antenna placement problem (APP) that addresses cover, traffic demand, interference, different parameterized antenna types, and the geometrical structure of cells. The resulting optimization problem is constrained and multi-objective. We present an evolutionary algorithm, capable of dealing with more than 700 candidate sites in the working area. The results show that the APP is tractable. The automatically generated designs enable experts to focus their efforts on the difficult parts of a network design problem.
The antenna-positioning problem concerns finding a set of sites for antennas from a set of pre-defined candidate sites, and for each selected site, to determine the number and types of antennas, as well as the associated values for each of the antenna parameters. All these choices must satisfy a set of imperative constraints and optimize a set of objectives. This paper presents a heuristic approach for tackling this complex and highly combinatorial problem. The proposed approach is composed of three phases: a constraint-based pre-processing phase to filter out bad configurations, an optimization phase using tabu search, and a post-optimization phase to improve solutions given by tabu search. To validate the approach, computational results are presented using large and realistic data sets.
Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN³) computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN²) computational complexity is presented. Second, a selection operator is presented which creates a mating pool by combining the parent and child populations and selecting the best (with respect to fitness and spread) N solutions. Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA—two other elitist multi-objective EAs which pay special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, elitist approach, and parameter-less sharing approach, NSGA-II should find increasing applications in the years to come.
The aim of this paper is to evaluate the impact of a novel method for advanced antenna technologies for the downlink performance of the 3GPP long term evolution (LTE) networks. The network performance impact of 3 times 2 vertical sectorization in different macro-cellular network scenarios for various combinations of antenna parameter configurations have been studied in terms of signal to interference plus noise ratio (SINR) and throughput performance. Simulation studies are performed for uniform UE distribution and full-buffer traffic by using LTE snap-shot simulator and three dimensional propagation modeling.
Third generation (3G) cellular networks are being implemented in many countries at high rate. Due to the fact that manual cell planning is a time consuming process and prone to a degree of error and inefficiency, there is a need for automated approaches to optimise coverage, capacity and quality of cellular networks in a fraction of the time. This paper studies the application of genetic algorithms to solve the antenna placement problem (APP) in universal mobile telecommunication system (UMTS) networks. The parameters of the genetic algorithm are tuned so that the algorithm converges optimally. The main task of the algorithm is to find the best set of base station locations so as to maximise coverage and quality of service measured as the signal-to-interference and noise ratio (SINR), as well as minimise the network cost by using fewer base stations. Assuming that a flat area is considered, the performance of the proposed algorithm was evaluated with 98% of the users in the network being covered with a good quality signal.
The base station placement problem, with n potential candidate sites is NP-Hard with 2n
solutions (Mathar and Niessen, Wirel. Netw. 6, 421–428, 2000). When dimensioned on m unknown variable settings (e.g., number of power settings + number of tilt settings, etc.) the computational complexity becomes (m+1)n
(Raisanen, PhD. thesis, 2006). We introduce a novel approach to reduce the computational complexity by dimensioning sites only once to guarantee traffic hold requirements are satisfied. This approach works by determining the maximum set of service test points candidate sites can handle without exceeding a hard traffic constraint, TMAX
. Following this, the ability of two evolutionary strategies (binary and permutation-coded) to search for the minimum set cover are compared. This reverses the commonly followed approach of achieving service coverage first and then dimensioning to meet traffic hold. To test this approach, three realistic GSM network simulation environments are engineered, and a series of tests performed. Results indicate this approach can quickly meet network operator objectives.
Finding optimum base station locations for a cellular radio network is considered as a mathematical optimization problem.
Dependent on the channel assignment policy, the minimization of interferences or the number of blocked channels, respectively,
may be more favourable. In this paper, a variety of according analytical optimization problems are introduced. Each is formalized
as an integer linear program, and in most cases optimum solutions can be given. Whenever by the complexity of the problem
an exact solution is out of reach, simulated annealing is used as an approximate optimization technique. The performance of
the different approaches is compared by extensive numerical tests.
A highly practical guide rooted in theory to include the necessary background for taking the reader through the planning, implementation and management stages for each type of cellular network. Present day cellular networks are a mixture of the technologies like GSM, EGPRS and WCDMA. They even contain features of the technologies that will lead us to the fourth generation networks. Designing and optimising these complex networks requires much deeper understanding. Advanced Cellular Network Planning and Optimisation presents radio, transmission and core network planning and optimisation aspects for GSM, EGPRS and WCDMA networks with focus on practical aspects of the field. Experts from each of the domains have brought their experiences under one book making it an essential read for design practitioners, experts, scientists and students working in the cellular industry. Key Highlights Focus on radio, transmission and core network planning and optimisation Covers GSM, EGPRS, WCDMA network planning & optimisation Gives an introduction to the networks/technologies beyond WCDMA, and explores its current status and future potential Examines the full range of potential scenarios and problems faced by those who design cellular networks and provides advice and solutions all backed up with real-world examples This text will serve as a handbook to anyone engaged in the design, deployment, performance and business of Cellular Networks. "Efficient planning and optimization of mobile networks are key to guarantee superior quality of service and user experience. They also form the essential foundation for the success of future technology development, making this book a valuable read on the road towards 4G." -Tero Ojanperä, Chief Technology Officer, Nokia Networks.
This paper deals with the automatic selection and configuration of
base station sites for mobile cellular networks. An optimization
framework based on simulated annealing is used for site selection and
for base-station configuration. Realistic path-loss estimates
incorporating terrain data are used. The configuration of each base
station involves selecting antenna type, power control, azimuth, and
tilt. Results are presented for several design scenarios with between
250 and 750 candidate sites and show that the optimization framework can
generate network designs with desired characteristics such as high area
coverage and high traffic capacity. The work shows that cellular network
design problems are tractable for realistic problem instances
Multi-objective evolutionary algorithm for 4G base station planning
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Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem
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