Indoor Non-Line of Sight Visible Light Communication with a Bi-LSTM Neural Network

Yonghe, Zhu; Gong, Chen; Luo, Jun; Jin, Meiyu; Jin, Xianqing; Xu, Zhengyuan

doi:10.1109/iccworkshops49005.2020.9145317

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…In this paper, an effective system is proposed to realize BSto-ground channel reconstruction. Our method bases on the [17] Figure 8 The BERs under different receiver locations for the second-order reflection communication [17] 提高检测性能. 图 9 BER随直射链路SNR β d 的变化(假设环境射频信号源采用QPSK调制方式) [18] Figure 9 BER versus direct link SNR β d under the assumption of QPSK modulated ambient RF source [18] 未标记信号.…”

Section: Discussionmentioning

confidence: 99%

Big data-based wireless communication technologies

梁应敞¹,

谭俊杰²,

龚晨³

et al. 2021

Sci. Sin.-Inf.

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Section: Discussionmentioning

confidence: 99%

Big data-based wireless communication technologies

梁应敞¹,

谭俊杰²,

龚晨³

et al. 2021

Sci. Sin.-Inf.

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“…Because the learning ability of FC-DNN for memory nonlinearity is limited, the bit error rate (BER) performance is not good. The recurrent neural network (RNN) with long short-term memory (LSTM) cell [6]- [8] is powerful for modeling the nonlinear channel with memory since it could handle the long-term dependencies and store the memory parameters, which are directly related with the channel characteristics. Nevertheless, the convergence speed of the simple LSTM is merely adequate, and the equalization accuracy doesn't possess a good robustness to the noise variation.…”

Section: Introductionmentioning

confidence: 99%

Deep Hybrid Neural Network-Based Channel Equalization in Visible Light Communication

et al. 2022

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In this letter, the channel impairments compensation of visible light communication is formulated as a time sequence with memory prediction. Then we propose efficient nonlinear post equalization, using a combined long-short term memory (LSTM) and deep neural network (DNN), to learn the complicated channel characteristics and recover the original transmitted signal. We leverage the long-term memory parameters of LSTM to represent the sequence causality within the memory channel and refine the results by DNN to improve the reconstruction accuracy. Results demonstrate that the proposed scheme can robustly address the overall channel impairments and accurately recover the original transmitted signal with fairly fast convergence speed. Besides, it can achieve better balance between performance and complexity than that of the conventional competitive approaches, which demonstrates the potential and validity of the proposed methodology for channel equalization.

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“…In addition, LiFi can be integrated into the existing lighting infrastructure and provide illumination and communication simultaneously, rendering energy-saving potentials. Meanwhile, LiFi faces the challenges of light-path blockage caused by opaque objects such as human bodies and furniture [4], despite the non-line-of-sight (NLoS) paths can facilitate the data transmission to some extent [5]. Also, LiFi offers a relatively small coverage area with a single access point (AP), usually around a few metres in diameter.…”

Section: Introductionmentioning

confidence: 99%

Mobility Management for Hybrid LiFi and WiFi Networks in the Presence of Light-Path Blockage

Chen

Haas

2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) are an emerging indoor wireless communication paradigm, which combines the advantages of the capacious optical spectra of LiFi and ubiquitous coverage of WiFi. Meanwhile, load balancing (LB) becomes a key challenge in resource management for such hybrid networks. The existing LB methods are mostly network-centric, relying on a central unit to make a solution for the users all at once. Consequently, the solution needs to be updated for all users at the same pace, regardless of their moving status. This would affect the network performance in two aspects: i) when the update frequency is low, it would compromise the connectivity of fast-moving users; ii) when the update frequency is high, it would cause unnecessary handovers as well as hefty feedback costs for slow-moving users. Motivated by this, we investigate user-centric LB which allows users to update their solutions at different paces. The research is developed upon our previous work on adaptive target-condition neural network (ATCNN), which can conduct LB for individual users in quasi-static channels. In this paper, a deep neural network (DNN) model is designed to enable an adaptive update interval for each individual user. This new model is termed as mobility-supporting neural network (MSNN). Associating MSNN with ATCNN, a user-centric LB framework named mobilitysupporting ATCNN (MS-ATCNN) is proposed to handle resource management and mobility management simultaneously. Results show that at the same level of average update interval, MS-ATCNN can achieve a network throughput up to 215% higher than conventional LB methods such as game theory, especially for a larger number of users. In addition, MS-ATCNN costs an ultra low runtime at the level of 100s µs, which is two to three orders of magnitude lower than game theory.

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Indoor Non-Line of Sight Visible Light Communication with a Bi-LSTM Neural Network

Cited by 10 publications

References 17 publications

Big data-based wireless communication technologies

Big data-based wireless communication technologies

Deep Hybrid Neural Network-Based Channel Equalization in Visible Light Communication

Mobility Management for Hybrid LiFi and WiFi Networks in the Presence of Light-Path Blockage

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