Achievable Rate With Closed-Form for SISO Channel and Broadcast Channel in Visible Light Communication Networks

Ma, Shuai; Yang, Ruixin; Li, Hang; Dong, Zhi-Long; Gu, Huaxi; Li, Shiyin

doi:10.1109/jlt.2017.2704619

Cited by 41 publications

(17 citation statements)

References 26 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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“…where the inequality (23b) follows the EPI [21] and hðQÞ ≤ 1/2 log 2πe var ðxÞ and the equality (23c) holds because s i follows the ABG distribution [34]. Let ℛ outer con denote the channel capacity region of NOMA VLC IoT networks bounded by (23c), which is given by…”

Section: Abg Inner Boundmentioning

confidence: 99%

Nonorthogonal Multiple Access for Visible Light Communication IoT Networks

et al. 2020

Wireless Communications and Mobile Computing

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In this study, we investigated the nonorthogonal multiple access (NOMA) for visible light communication (VLC) Internet of Things (IoT) networks and provided a promising system design for 5G and beyond 5G applications. Specifically, we studied the capacity region of a practical uplink NOMA for multiple IoT devices with discrete and continuous inputs, respectively. For discrete inputs, we proposed an entropy approximation method to approach the channel capacity and obtain the discrete inner and outer bounds. For the continuous inputs, we derived the inner and outer bounds in closed forms. Based on these results, we further investigated the optimal receiver beamforming design for the multiple access channel (MAC) of VLC IoT networks to maximize the minimum uplink rate under receiver power constraints. By exploiting the structure of the achievable rate expressions, we showed that the optimal beamformers are the generalized eigenvectors corresponding to the largest generalized eigenvalues. Numerical results show the tightness of the proposed capacity regions and the superiority of the proposed beamformers for VLC IoT networks.

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“…From [25], [26], a lower bound and an upper bound of the achievable sum rate are given respectively by…”

Section: A Mrt/rzf Linear Precodingmentioning

confidence: 99%

Beam Domain Massive MIMO for Optical Wireless Communications With Transmit Lens

Sun

Gao

Wang

et al. 2019

IEEE Trans. Commun.

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This paper presents a novel massive multiple-input multiple-output (MIMO) transmission in beam domain for optical wireless communications. The optical base station equipped with massive optical transmitters communicates with a number of user terminals (UTs) through a transmit lens. Focusing on LED transmitters, we analyze light refraction of the lens and establish a channel model for optical massive MIMO transmissions. For a large number of LEDs, channel vectors of different UTs become asymptotically orthogonal. We investigate the maximum ratio transmission and regularized zero-forcing precoding in the optical massive MIMO system, and propose a linear precoding design to maximize the sum rate. We further design the precoding when the number of transmitters grows asymptotically large, and show that beam division multiple access (BDMA) transmission achieves the asymptotically optimal performance for sum rate maximization. Unlike optical MIMO without a transmit lens, BDMA can increase the sum rate proportionally to 2K and K under the total and per transmitter power constraints, respectively, where K is the number of UTs. In the non-asymptotic case, we prove the orthogonality conditions of the optimal power allocation in beam domain and propose efficient beam allocation algorithms. Numerical results confirm the significantly improved performance of our proposed beam domain optical massive MIMO communication approaches. Index TermsOptical massive MIMO communication, transmit lens, Beam division multiple access (BDMA). Optical wireless communication systems rely on optical radiations to transmit information with wavelengths ranging from infrared to ultraviolet [1]. Base station (BS) commonly employs optical transmitters, such as light-emitting diodes (LEDs), to convert the electrical signals to optical signals. Recently, laser diodes (LDs) are considered as potential sources for optical communication due to high modulation bandwidth, efficiency, and beam convergence [2]. User terminals (UTs) employ photodetectors like photodiodes as optical receivers to convert the optical power into electrical current. Optical communications can significantly relieve the crowed radio frequency (RF) spectrum, provide high speed data transmission [3], and achieve simple and lowcost modulation and demodulation through intensity modulation and direct detection (IM/DD) [4]. Thus, optical wireless communication has attracted increasing attention from both academia and industry [5]-[7]. To achieve high data rate in optical communications, multiple separate LED/LD arrays are usually utilized in the BS to provide higher data rate by means of spatial multiplexing. As a result, multiple-input multiple-output (MIMO) technique is a natural progression for optical communication systems [7], [8]. As the nature of optical downlink communication is broadcast network, multiple UTs should be well supported. BSs simultaneously transmit signals to all UTs, resulting in the so-called multi-user interference, consequently degrades the performance. Thus, mul...

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Achievable Rate With Closed-Form for SISO Channel and Broadcast Channel in Visible Light Communication Networks

Cited by 41 publications

References 26 publications

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

Nonorthogonal Multiple Access for Visible Light Communication IoT Networks

Beam Domain Massive MIMO for Optical Wireless Communications With Transmit Lens

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