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

LightFidelity (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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.5\times$</tex-math></inline-formula> 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%.

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-10-16
DOI
https://doi.org/10.1109/tvt.2024.3477310
Citations
5
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
1.04
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article

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

Rizwana Ahmad, Saswati Paramita, Anand Srivastava, Vivek Ashok Bohara et al.
5 citations
IEEE Transactions on Vehicular Technology
Advanced MIMO Systems Optimization
1.04
article

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

Rizwana Ahmad, Saswati Paramita, Anand Srivastava, Vivek Ashok Bohara, Arani Bhattacharya
article en
5 citations

Abstract

LightFidelity (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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.5\times$</tex-math></inline-formula> 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%.

IEEE Transactions on Vehicular TechnologyVol. 74(2)
Indraprastha Institute of Information Technology Delhi (IN), University of Cambridge (GB), Indian Institute of Technology Delhi (IN)
Openalex Percentile: Top 23%
Advanced MIMO Systems Optimization
1.04
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