QoE-Driven Optimized Load Balancing Design for Hybrid LiFi and WiFi Networks

Ma, Weibin; Zhang, Lin

doi:10.1109/lcomm.2018.2869388

Cited by 19 publications

(16 citation statements)

References 11 publications

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“…In [107], an LB method was proposed to achieve proportional fairness (PF) among users, in forms of both centralized and distributed resource-allocation algorithms. To improve quality of service, the LB issue is formulated as a mixed-integer non-linear programming problem in [108], which considers different data rate requirements among users. The two above methods both construct an NP-hard problem, and solving the problem requires an excessive amount of computational complexity that exponentially increases with the number of APs.…”

Section: A Stationary Channel Load Balancingmentioning

confidence: 99%

Hybrid LiFi and WiFi Networks: A Survey

Soltani

Zhou

et al. 2021

IEEE Commun. Surv. Tutorials

177

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In order to tackle the rapidly growing number of mobile devices and their expanding demands for Internet services, network convergence is envisaged to integrate different technology domains. For indoor wireless communications, one promising approach is to coordinate light fidelity (LiFi) and wireless fidelity (WiFi), namely hybrid LiFi and WiFi networks (HLWNets). This hybrid network combines the high-speed data transmission of LiFi and the ubiquitous coverage of WiFi. In this paper, we present a survey-style introduction to HLWNets, starting with a framework of system design in the aspects of network architectures, cell deployments, multiple access and modulation schemes, illumination requirements and backhaul. Key performance metrics and recent achievements are then reviewed to demonstrate the superiority of HLWNets against stand-alone networks. Further, the unique challenges facing HLWNets are elaborated on key research topics including user behavior modeling, interference management, handover and load balancing. Moreover, the potential of HLWNets in the application areas is presented, exemplified by indoor positioning and physical layer security. Finally, the challenges and future research directions are discussed.

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Section: A Stationary Channel Load Balancingmentioning

confidence: 99%

Hybrid LiFi and WiFi Networks: A Survey

Soltani

Zhou

et al. 2021

IEEE Commun. Surv. Tutorials

177

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show abstract

“…It has been reported in the literature that an appropriately designed hybrid LiFi WiFi network can provide higher data rate, better user satisfaction, and better outage performance [14]- [16]. Efficient load balancing (LB) is a major challenge for hybrid LiFi WiFi networks [17]; the two main issues are user access point (AP) selection and resource allocation. For hybrid networks, AP selection is a complex process as the coverage area of LiFi and WiFi network overlaps with each other and WiFi covers larger area but has lower capacity, thus, WiFi is more prone to overload.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

et al. 2020

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Light fidelity (LiFi) is an emerging communication technology, which utilizes the lightemitting diodes (LEDs) for high-speed wireless communications. Due to its huge unlicensed bandwidth, LiFi is capable of supporting high data rates. The quality of the LiFi channel fluctuates across the room due to interference, reflection from walls or blockage. On the other hand, WiFi is another wireless communication technology that is capable of providing moderate data rates with ubiquitous coverage. As the electromagnetic spectrum of LiFi does not overlap with WiFi, both of them can coexist to form a hybrid LiFi and WiFi network for seamless and high-throughput connectivity. The performance of a hybrid system significantly depends upon the access point (AP) assignment and resource allocation strategies. In this paper, a downlink hybrid system with one WiFi AP and four LiFi APs is considered, and a reinforcement learning (RL) algorithm is implemented in order to determine an optimal AP assignment strategy, which maximizes the long-term system throughput while ensuring the required users fairness and satisfaction. Furthermore, two different scenarios based on the random waypoint model with uniform and non-uniform distribution of users have been studied. The performance of the proposed system is compared against state-of-the-art benchmark approaches e.g., signal strength strategy (SSS), exhaustive search, and an iterative optimization method. The results are reported in terms of the average system throughput, user satisfaction, fairness, and capacity outage probability. It is shown that the proposed RL method performs closer to the exhaustive search scheme at fairly low complexity. The RL method also outperforms the SSS scheme and the iterative algorithm in most scenarios INDEX TERMS Hybrid LiFi WiFi, Light Fidelity (LiFi), Load balancing, Reinforcement learning (RL), Trust region policy optimization (TRPO).

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“…Moreover, an indoor VLC system consists of multiple VLC APs, so the AP selection has not been investigated in integrated VLCP systems or networks. Although the literatures [59,66,73,141,193,194] investigated efficient AP selection schemes in multi-cell VLC networks to maximize the network transmission data rate and satisfy QoS requirements of devices with considering the ICI mitigation, the assumption of perfect location information of devices may be not practical.…”

Section: Discussionmentioning

confidence: 99%

“…In such a network, due to the specific statistical delay constraints of devices, the resource allocation under delay-guarantee constraints was considered into the optimization problem [230,231]. In addition, the authors in [73,232,233] proposed RF/VLC access point selection approaches to ensure users' QoS requirements in heterogeneous networks, especially, the reinforcement learning (RL) tool is applied to solve the decision making problem in heterogeneous RF/VLC networks [232,233].…”

Section: Discussionmentioning

confidence: 99%

“…The traditional max-RSSI approach [203] is widely adopted to address the AP selection problem, but it fails to enable devices to achieve the desired service qualities, because the devices may simply associate with the nearest AP with the largest RSSI value, which may result in the intense channel contention and significantly unbalanced data rate distributions. In addition, some optimal AP selection approaches [59,66,73,141,193] were presented in RF networks or VLC networks, but they have the high complexity and the perfect assumption of devices' location information.…”

Section: Ap Selection Formationmentioning

confidence: 99%

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Visible light communication systems supporting wireless data access and indoor positioning applications

Yang¹

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QoE-Driven Optimized Load Balancing Design for Hybrid LiFi and WiFi Networks

Cited by 19 publications

References 11 publications

Hybrid LiFi and WiFi Networks: A Survey

Hybrid LiFi and WiFi Networks: A Survey

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

Visible light communication systems supporting wireless data access and indoor positioning applications

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