An Efficient Geometry-Induced Genetic Algorithm for Base Station Placement in Cellular Networks

Liu, Yaxi; Huangfu, Wei; Zhang, Haijun; Wang, Haobin; Wei, An Jian; Long, Keping

doi:10.1109/access.2019.2933284

Cited by 18 publications

(9 citation statements)

References 38 publications

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“…For example, [32] introduced a method based on GA and deep learning to predict financial behaviours. Moreover, the genetic algorithm was indeed applied in the cellular communications related research field such as facilitating terrestrial base station placement and showed excellent efficiency [33].…”

Section: Genetic Algorithm Based 2d Placementmentioning

confidence: 99%

QoS-Compliant 3D Deployment Optimization Strategy for UAV Base Stations

Zhong,

Huo,

Dong

et al. 2020

Preprint

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Unmanned aerial vehicle (UAV) is being integrated as an active element in 5G and beyond networks. Because of its flexibility and mobility, UAV base stations (UAV-BSs) can be deployed according to the ground user distributions and their quality of service (QoS) requirement. Although there has been quite some prior research on the UAV deployment, no work has studied this problem in a 3 dimensional (3D) setting and taken into account the UAV-BS capacity limit and the quality of service (QoS) requirements of ground users. Therefore, in this paper, we focus on the problem of deploying UAV-BSs to provide satisfactory wireless communication services, with the aim to maximize the total number of covered user equipment (UE) subject to user data rate requirements and UAV-BSs' capacity limit. First, we model the relationship between the air-to-ground (A2G) path loss (PL) and the location of UAV-BSs in both horizontal and vertical dimensions which has not been considered in previous works. Unlike the conventional UAV deployment problem formulation, the 3D deployment problem is decoupled into a 2D horizontal placement and altitude determination connected by path loss requirement and minimization. Then, we propose a novel genetic algorithm (GA) based 2D placement approach in which UAV-BSs are placed to have maximum coverage of the users with consideration of data rate distribution. Finally, numerical and simulation results show that the proposed approach has enabled a better coverage percentage comparing with other schemes.Index Terms-Unmanned aerial vehicle (UAV), wireless communications, broadcasting, user equipment (UE), air-to-ground (A2G), channel models, 3D deployment, genetic algorithm (GA). I. INTRODUCTIONU NMANNED aerial vehicle (UAV)-assisted communications have recently gained fast popularity as an effective solution to complement traditional stationary base stations. Unmanned aerial vehicle base stations (UAV-BSs) have the rapid-deployment and reconfiguration advantages compared to terrestrial ones [1]. The roadmap of telecommunication infrastructure provider [2] and 3GPP technical reports [3] have demonstrated promising field trials results of wireless connectivity to the UAVs, and discussed the future ubiquitous mobile broadband coverage both on the ground and in the sky. The fifth generation (5G) and beyond wireless communications and Internet-of-Things (IoT) application scenarios can X. Zhong, Y. Huo and X. Dong are with the

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Section: Genetic Algorithm Based 2d Placementmentioning

confidence: 99%

QoS-Compliant 3D Deployment Optimization Strategy for UAV Base Stations

Zhong,

Huo,

Dong

et al. 2020

Preprint

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“…The resulting approach turns out to be an efficient one as the outage probability is quite low [8]. A novel geometry-induced genetic algorithm was proposed in [9] and gives promising results. The main approach used in that work was to divide the whole region of interest into sub-regions and then the local coverage ratio was ensured to be higher to give better performance.…”

Section: Introductionmentioning

confidence: 99%

A New Simulation-Optimization Approach Using Hybrid Radial Basis Function and Particle Swarm Optimization in Multi-Transmitter Placement Planning

Parnianifard¹,

Saadi²,

Pengnoo³

et al. 2020

Preprint

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With the every passing day, the demand for data traffic is increasing and this demand forces the research community not only to look for alternating spectrum for communication but also urges the radio frequency planners to use the existing spectrum smartly. Cell size is shrinking with the every upcoming communication generation which makes the base station placement planning complex and cumbersome. In order to make the next-generation cost-effective, it is important to design the network in such a way which utilizes minimum number of base stations while ensure coverage and quality of service. This paper aims at develop a new approach using hybrid metaheuristic and metamodel applied in multi-transmitter placement planning (MTPP) problem. We apply radial basis function (RBF) metamodel to assist particle swarm optimizer (PSO) in a constrained simulation-optimization (SO) of MTPP to mitigate the associated computational burden of optimization procedure. We evaluate the effectiveness and applicability of proposed algorithm in a case study by simulating MTPP model with two, three, four and five transmitters.

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“…NSGA-II with a new problem-specific chromosome makes significant performance improvement in oil refinery scheduling [9] and production-distribution optimization [10]. Better performance also produced by problem-specific chromosome on the base station placement in cellular networks [11]. Variable-length chromosome outperforms fixed-length in satellite constellations [12] and road traffic coordination as a multipath optimization problem [13].…”

Section: Introductionmentioning

confidence: 99%

An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II

Jauhari

Mahmudy

Basuki

2019

JITeCS

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Proportional tuition fees assessment is an optimization process to find a compromise point between student willingness to pay and institution income. Using a genetic algorithm to find optimal solutions requires effective chromosome representations, parameters, and operator genetic to obtain efficient search. This paper proposes a new chromosome representation and also finding efficient genetic parameters to solve the proportional tuition fees assessment problem. The results of applying the new chromosome representation are compared with another chromosome representation in the previous study. The evaluations show that the proposed chromosome representation obtains better results than the other in both execution time required and the quality of the solutions.

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An Efficient Geometry-Induced Genetic Algorithm for Base Station Placement in Cellular Networks

Cited by 18 publications

References 38 publications

QoS-Compliant 3D Deployment Optimization Strategy for UAV Base Stations

QoS-Compliant 3D Deployment Optimization Strategy for UAV Base Stations

A New Simulation-Optimization Approach Using Hybrid Radial Basis Function and Particle Swarm Optimization in Multi-Transmitter Placement Planning

An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II

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