Open Access

ARTICLE

Optimization of Cognitive Femtocell Network via Oppositional Beetle Swarm Optimization Algorithm

K. Rajesh Kumar^1,*, M. Vijayakumar²

1 Department of ECE, Adhiyamaan College of Engineering, Hosur, Tamilnadu, India
2 Department of EEE, K.S.R. College of Engineering, Tiruchengode, Tamilnadu, India

* Corresponding Author: K. Rajesh Kumar. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 819-832. https://doi.org/10.32604/iasc.2023.030961

Received 07 April 2022; Accepted 08 June 2022; Issue published 29 September 2022

Abstract

In past decades, cellular networks have raised the usage of spectrum resources due to the victory of mobile broadband services. Mobile devices create massive data than ever before, facing the way cellular networks are installed presently for satisfying the increased traffic requirements. The development of a new exclusive spectrum offered to meet up the traffic requirements is challenging as spectrum resources are limited, hence costly. Cognitive radio technology is presented to increase the pool of existing spectrum resources for mobile users via Femtocells, placed on the top of the available macrocell network for sharing the same spectrum. Nevertheless, the concurrent reuse of spectrum resources from Femto networks poses destructive interference on macro networks. To resolve this issue, this paper introduces an optimal channel allocation model using the Oppositional Beetle Swarm Optimization Algorithm (OBSOA) to allocate the channel with interference avoidance. A new OBSOA is derived in this paper by the inclusion of opposition-based learning (OBL) in BSOA. This algorithm allocates the channels used by PUs (PUs) to the secondary users (SUs) in such a way that interference is minimized. This proposed approach is implemented in the MATrix LABoratory (MATLAB) platform. The performance of this proposed approach is evaluated in terms of several measures and the experimental outcome verified the superior nature of the OBSOA-based channel allocation model. OBSOA model has resulted in a maximum signal-to-interference-plus-noise ratio value of 86.42 dB.

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Keywords

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