Detail publikace

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks

USMAN ALI KHAN, M. INAYATULLAH BABAR, M. REHMAN, S. KOMOSNÝ, D. HAN JOO CHONG, P.

Originální název

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals’ line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Klíčová slova

light fidelity; WiFi; handover; DNN; HLWNet

Autoři

USMAN ALI KHAN, M.; INAYATULLAH BABAR, M.; REHMAN, S.; KOMOSNÝ, D.; HAN JOO CHONG, P.

Vydáno

22. 3. 2024

Nakladatel

MDPI

ISSN

1424-8220

Periodikum

SENSORS

Ročník

24

Číslo

7

Stát

Švýcarská konfederace

Strany od

1

Strany do

14

Strany počet

14

URL

https://www.mdpi.com/1424-8220/24/7/2021

Plný text v Digitální knihovně

http://hdl.handle.net/11012/245517

BibTex

@article{BUT188324,
  author="Mohammad {Usman Ali Khan} and Mohammad {Inayatullah Babar} and Saeed {Rehman} and Dan {Komosný} and Peter {Han Joo Chong}",
  title="Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks
",
  journal="SENSORS",
  year="2024",
  volume="24",
  number="7",
  pages="1--14",
  doi="10.3390/s24072021",
  issn="1424-8220",
  url="https://www.mdpi.com/1424-8220/24/7/2021"
}