Beam Domain Massive MIMO for Optical Wireless Communications With Transmit Lens

Sun, Chen; Gao, Xiqi; Wang, Jiaheng; Ding, Zhi; Xia, Xiang-Gen

doi:10.1109/tcomm.2018.2883622

Cited by 29 publications

(19 citation statements)

References 31 publications

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“…is the channel gain between interfering LiFi APs β and the user µ. The achievable data rate between LiFi AP α and user µ, is calculated by the capacity lowerbound [30], [31] and is given as:…”

Section: A Lifi Channel Modelmentioning

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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Section: A Lifi Channel Modelmentioning

confidence: 99%

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

et al. 2020

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“…Instead of using RF-MIMO signals, optical-MIMO signals can be used for the wireless transmission, such as free-space optics (FSO) and infrared (IR) light. Optical wireless communication (OWC) systems depend on optical radiations to transmit information with wavelengths ranging from infrared to ultraviolet [87].…”

Section: Optical Mimo Signals Over Fibrementioning

confidence: 99%

A Review on MIMO Wireless Signals over Fibre for Next Generation Fibre Wireless (FiWi) Broadband Networks

et al. 2020

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Next-generation access/mobile networks have set high standards in terms of providing wireless services at high data rates in order to keep up with the vast demands for other mobility and multiple services. Wireless-optical broadband access network (WOBAN) technology, also known as fibre-wireless (FiWi), has uncovered incredible opportunities for the future of next-generation networks because it gets the best of both domains: huge bandwidth provided by the optical fibre and high ubiquity of the wireless domain. The objective of FiWi networks is to integrate the high data rate and long reach provided by optical networks and the ubiquity and mobility of wireless networks, with the target to decrease their expense and complexity. Multiple-input–multiple-output (MIMO) is an inevitable technique for most of the new mobile/wireless networks that are driven by the huge data rates required by today’s users. Consequently, to construct any FiWi system for next-generation (NG) access/broadband networks, an MIMO technique has to be considered. This article presents a comprehensive, contemporary review of the latest subsystems, architectures and integrated technologies of MIMO wireless signals backhauling using optical fibre or fibre access networks, such as passive optical networks (PONs). An overview for FiWi, PONs and MIMO wireless systems is provided. In addition, advanced techniques of accommodating the MIMO wireless signals over optical fibre are explained and compared. Different types of wireless MIMO signals over fibre, such as 5G, WiFi and related transport technologies, are reviewed. Moreover, future research trends are also discussed.

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“…A thin-lens imaging detector can partially or completely separate different APs with small distortion, but has a narrower FOV in comparison to non-imaging detectors [27] It is apparent that the divergence of channels from different APs is increased with the aid of a lens. Moreover, the lens can be added at the transmitter side if LED array is deployed, to improve the channel divergence between LEDs in an LED array [28].…”

Section: Optical Multiple-input-multiple-output (A) System Model Of Omentioning

confidence: 99%

“…Therefore, MU-MIMO-OFDM systems were proposed for OWC networks [9,38]. Moreover, [28,38,39] used lens at transmitters or receivers to separate signals from different APs in spatial domain. The lens could reduce the channel correlation due to its high spatial resolution.…”

Section: (A) Multi-user Multiple-input-multiple-output Under High Chamentioning

confidence: 99%