Evolution of Hybrid LiFi–WiFi Networks: A Survey

Besjedica, Toni; Fertalj, Krešimir; Lipovac, Vlatko; Zakarija, Ivona

doi:10.3390/s23094252

Cited by 10 publications

(2 citation statements)

References 99 publications

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“…This is primarily attributed to the rapid development of emerging technologies, for example, the IoT (Internet of Things) and VBR (Virtual Mobile Reality) [1,2]. Moreover, there is a significant surge in the need for mobile traffic, with a sevenfold growth from 2017 to 2022, reaching around 77.5 Exabyte each month [3]. In contrast to 5G, the upcoming wireless networks, commonly referred to as the 6G wireless networks, are anticipated to prioritize human-centric features.…”

Section: Introductionmentioning

confidence: 99%

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

Khan,

Babar,

Rehman

et al. 2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Khan,

Babar,

Rehman

et al. 2024

Sensors

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“…A hybrid network that combines both LiFi and WiGig APs is feasible because LiFi and WiGig operate in separate frequency bands. This ensures that their applications will not interfere with each other [ 8 , 19 , 20 , 21 ]. A single light-emitting diode (LED) can produce data at a rate of more than 3 Gb/s, whereas WiGig AP offers a data throughput of 7 Gb/s [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Conditional Most-Correlated Distribution-Based Load-Balancing Scheme for Hybrid LiFi/WiGig Network

Farrag,

Al Ayidh,

Hussein

2023

Sensors

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A hybrid network has recently been proposed as a framework for a high-speed wireless communication network. Basically, it integrates light fidelity (LiFi) with radio frequency wireless gigabit alliance (WiGig) networks that operate, simultaneously, in a completely different frequency band. To assign the best access point (AP) and provide enough resources for each user, an effective load-balancing (LB) strategy is needed. However, the traditional LB strategies involve sophisticated iterative computing procedures whenever the user distribution changes. Hence, the first contribution of this work is to offer a more adaptable, two-step, conditional, and most-correlated distribution (CMCD) algorithm. Thus, the low-complexity most-correlated distribution (MCD) LB scheme is applied, and the average data rates for all users are then calculated. If the results achieve the predefined performance threshold (PDT), the decisions will be confirmed; otherwise, the proposed scheme automatically switches to the more accurate, but more complex, consecutive assign WiGig first separate optimization algorithms (CAWFS) algorithm. The suggested algorithm provides a clear performance-complexity trade-off, which could be simply controlled by choosing the suitable performance tolerance factor. The second contribution of this paper is the correlation-weighted majority voting (CWMV) method, which attempts to benefit from as many prior decision votes as possible, instead of relying just on one vote. In the CWMV technique, the weight of each vote is calculated based on the correlation between the history distribution vectors and the new user distribution vector. A significant increase in the system performance is evident from the simulation results.

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