Reinforcement Learning Based Network Selection for Hybrid VLC and RF Systems

Wang, Handong; Wu, Guofeng; Du, Zhiyong; Jiang, Bin

doi:10.1051/matecconf/201817303014

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Various methods have been applied to solve the problem of LB in HLWNs, they can be broadly classified into following categories: optimization [6], machine learning [7][8][9][10][11] and fuzzy logic [12]. Limited studies have explored the application of machine learning for solving the LB problem in HLWN.…”

Section: A Related Workmentioning

confidence: 99%

Reinforcement Learning-Based Near-Optimal Load Balancing for Heterogeneous LiFi WiFi Network

Ahmad

Soltani

Safari

et al. 2022

IEEE Systems Journal

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Owing to the non-overlapping spectrum, Light fidelity (LiFi) and WiFi technologies can coexist and form a heterogeneous LiFi WiFi network (HLWN). The performance of HLWN significantly depends upon the load balancing strategies. Since load balancing of HLWN is a non-convex mixed-integer nonlinear programming (MINLP) optimization problem, it is mathematically intractable, and therefore, the conventional optimization methods fail to provide an optimal global solution. Although an optimal solution can be obtained using the exhaustive search method, it would be computationally complex. Therefore, in this paper, a reinforcement learning (RL) based algorithm is explored for solving the load balancing problem for the downlink HLWN at reasonably low complexity and near optimal performance. We have proposed three different reward functions for RL; the first and second reward functions work toward maximizing average network throughput and user satisfaction, respectively. The third reward function is designed to maximize the long-term system throughput and ensure at least 50% user's satisfaction for all users. In order to study the effects of link aggregation on system performance, this work considers two different types of receiver schemes, namely, single access point (SAP) and link aggregation (LA) scheme. While the SAP allows the user to receive data only from a single AP, the LA scheme allows the user to receive data simultaneously from both LiFi and WiFi AP. This paper also includes effect of random orientation of the receiver device and handover overhead. Further, concepts of domain knowledge have been included in this work to reduce the computational complexity of the algorithm. The proposed system performance is compared with the two benchmarks: received signal strength (RSS) and exhaustive search based on the computational complexity, average system throughput, and user satisfaction. It is shown that the proposed RL scheme outperforms the RSS scheme in average system throughput and user satisfaction. The RL scheme with an appropriate reward function provides a matching performance to the exhaustive search at reasonably low complexity.

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Section: A Related Workmentioning

confidence: 99%

Reinforcement Learning-Based Near-Optimal Load Balancing for Heterogeneous LiFi WiFi Network

Ahmad

Soltani

Safari

et al. 2022

IEEE Systems Journal

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“…Various studies have employed a variety of approaches for the network selection. A reinforcement learning approach in [35] considered a dynamic environment, taking into account both the uplink and downlink performance requirements of traffic for network selection in RF/VLC hybrid systems. The context-aware reinforcement learning solution for the RF/VLC hybrid network selection in [134] was able to tackle the challenges associated with dynamic environments and complicated service requirements.…”

Section: H Literatures Surveys On Rf/optical Wireless Hybrid Networkmentioning

confidence: 99%

“…Throughput enhancement is the main goal for the deployment of RF/VLC and RF/LiFi hybrid wireless systems depicted in [7], [31], [34], [39], [44], [46], [53], [137], [142], [143], [213], [214]. For the successful deployment of these hybrid systems, some issues have been considered as important research topics, including handover performance improvement [18], [34], [38], [48], [54], [207], enhancing the energy efficiency [32], [33], [140], [203], intelligent network selection [35], [134], coverage extension [40], [49], [127], outage probability reduction [160], error rate reduction [160], efficient load-balancing technique [140], [208], spectral efficiency maximizing [206], security enhancement [138], efficient resource allocation [39], and cost minimization [51]. To date, no significant research has been performed on OCC based wireless hybrid systems.…”

Section: B Summary and Lessons Learnedmentioning

confidence: 99%

Optical Wireless Hybrid Networks: Trends, Opportunities, Challenges, and Research Directions

Chowdhury

Hasan

Shahjalal

et al. 2020

IEEE Commun. Surv. Tutorials

237

103

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Optical wireless communication (OWC) is an excellent complementary solution to its radio frequency (RF) counterpart. OWC technologies have been demonstrated to be able to support high traffic generated by massive connectivity of the Internet of Things (IoT) and upcoming 5 th generation (5G) wireless communication systems. As the characteristics of OWC and RF are complementary, a combined application is regarded as a promising approach to support 5G and beyond communication systems. Hybrid RF/optical and optical/optical wireless systems offer an excellent solution for recovering the limitations of individual systems as well as for providing positive features of each of the technologies. An RF/optical wireless hybrid system consists both RF and optical-based wireless technologies, whereas an optical/optical wireless hybrid system consists two or more types of OWC technologies. The co-deployment of wireless systems can improve system performance in terms of throughput, reliability, and energy efficiency of individual networks. This study surveys the state of the art and key research directions regarding optical wireless hybrid networks, being the first extensive survey dedicated to this topic. We provide a technology overview of existing literature on optical wireless hybrid networks, such as RF/optical and optical/optical systems. We consider the RFbased macrocell, small cell, wireless fidelity, and Bluetooth, as well as optical-based visible light communication, light fidelity, optical camera communication, and free-space optical communication technologies for different combinations of hybrid systems. Moreover, we consider underwater acoustic communication for hybrid acoustic/optical systems. The opportunities brought by hybrid systems are presented in detail. We outline important challenges that need to be addressed for successful deployment of optical wireless hybrid network systems for 5G and IoT paradigms.

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“…They have reported the results in terms of delay versus throughput trade-offs. In [27], authors proposed transfer learning based network selection algorithm, i.e., reinforcement learning with knowledge transfer. Specifically, in this work, context information is leveraged to tackle the network selection on two aspects.…”

Section: A Related Workmentioning

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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Reinforcement Learning Based Network Selection for Hybrid VLC and RF Systems

Cited by 10 publications

References 13 publications

Reinforcement Learning-Based Near-Optimal Load Balancing for Heterogeneous LiFi WiFi Network

Reinforcement Learning-Based Near-Optimal Load Balancing for Heterogeneous LiFi WiFi Network

Optical Wireless Hybrid Networks: Trends, Opportunities, Challenges, and Research Directions

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

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