Flow-based Rate Maximization for Link Aggregation Enabled Hybrid LiFi-WiFi Network

Light Fidelity (LiFi) is one of the most promising techniques to meet such high demand for indoor users by utilizing the visible light spectrum. A major challenge of LiFi is that its coverage is relatively limited, as the surrounding walls, objects, and other surfaces mostly absorb visible light. Th...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2024-10, p.1-13
Main Authors: Paramita, Saswati, Bhattacharya, Arani, Ahmad, Rizwana, Bohara, Vivek Ashok, Srivastava, Anand
Format: Article
Language:English
Subjects:
Aggregation
Backhaul networks
Ethernet
Heterogeneous networks
Hybrid LiFi-WiFi Network
LiFi
Light fidelity
Linear programming
Link aggregation
Link aggregation overhead
Load management
Radio frequency
Resource management
Throughput
WiFi
Wireless fidelity
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Summary:Light Fidelity (LiFi) is one of the most promising techniques to meet such high demand for indoor users by utilizing the visible light spectrum. A major challenge of LiFi is that its coverage is relatively limited, as the surrounding walls, objects, and other surfaces mostly absorb visible light. Thus, a number of studies have proposed aggregating the bandwidth of WiFi and LiFi to serve all users within a room. However, complementing LiFi with WiFi via bandwidth aggregation typically comes with an overhead in terms of both aggregation and computation, which reduces the data rates that can be used. Furthermore, data provided to users are often also limited by the backhaul capacity, which is typically wired Ethernet in indoor settings. In this work, we model the utilization of the functioning of WiFi and LiFi access points as a two-dimensional flow graph and show that the problem of maximizing the sum of data rate across all users is NP-Hard in practice. We then design an algorithm FLADA to solve this problem by solving its relaxed version where the variables are treated as real numbers, and then rounding to the nearest integer. We prove formally that it provides a solution that is at least 0.5ˆ the optimal. We further compare it with a greedy baseline approach through extensive simulations and show that it outperforms it by up to 81.6%.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3477310