Open Access

ARTICLE

Hybrid Metamodeling/Metaheuristic Assisted Multi-Transmitters Placement Planning

Amir Parnianifard¹, Muhammad Saadi², Manus Pengnoo¹, Muhammad Ali Imran³, Sattam Al Otaibi⁴, Pruk Sasithong¹, Pisit Vanichchanunt⁵, Tuchsanai Polysuwan⁶, Lunchakorn Wuttisittikulkij^1,*

1 Wireless Communication Ecosystem Research Unit, Chulalongkorn University, Bangkok, 10330, Thailand
2 Department of Electrical Engineering, University of Central Punjab, Lahore, Pakistan
3 School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
4 Head-of-Innovation-and-Entrepreneurship-Center, College of Engineering, Taif University, Taif, KSA
5 Department of Electrical and Computer Engineering, King Mongkut University of Technology North Bangkok, Bangkok, Thailand
6 Department of Electrical Engineering, Siam University, Bangkok, Thailand

* Corresponding Author: Lunchakorn Wuttisittikulkij. Email:

(This article belongs to the Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)

Computers, Materials & Continua 2021, 68(1), 569-587. https://doi.org/10.32604/cmc.2021.015730

Received 04 December 2020; Accepted 13 January 2021; Issue published 22 March 2021

Abstract

With every passing day, the demand for data traffic is increasing, and this urges the research community not only to look for an alternating spectrum for communication but also urges radio frequency planners to use the existing spectrum efficiently. Cell sizes are shrinking with every upcoming communication generation, which makes base station placement planning even more complex and cumbersome. In order to make the next-generation cost-effective, it is important to design a network in such a way that it utilizes the minimum number of base stations while ensuring seamless coverage and quality of service. This paper aims at the development of a new simulation-based optimization approach using a hybrid metaheuristic and metamodel applied in a novel mathematical formulation of the multi-transmitter placement planning (MTPP) problem. We first develop a new mathematical programming model for MTPP that is flexible to design the locations for any number of transmitters. To solve this constrained optimization problem, we propose a hybrid approach using the radial basis function (RBF) metamodel to assist the particle swarm optimizer (PSO) by mitigating the associated computational burden of the optimization procedure. We evaluate the effectiveness and applicability of the proposed algorithm by simulating the MTPP model with two, three, four and five transmitters and estimating the Pareto front for optimal locations of transmitters. The quantitative results show that almost maximum signal coverage can be obtained with four transmitters; thus, it is not a wise idea to use higher number of transmitters in the model. Furthermore, the limitations and future works are discussed.

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Citations