Square Geometrical Shaping 128QAM Based Time Domain Hybrid Modulation in Visible Light Communication System

We proposed two kinds of visible light communication(VLC)systems which respectively based on 64QAM/square geometrical shaping(SGS)128QAM time domain hybrid modulation scheme(SGSHY)and 64QAM/128QAM time domain hybrid modulation scheme(REGHY).These two systems can operate around specific forward error correction(FEC)threshold and maximize the achievable information rate(AIR)of the system.The principles of SGSHY and REGHY are proposed in detail,which has very low computation complexity compared with probabilistic shaping.The SGSHY outperforms REGHY at high peak to peak voltage(Vpp).Experimental results show that at high Vpp like 1.4V,which means the system is suffering from high nonlinear distortion,the AIR of SGSHY outperforms that of REGHY by 0.12Gb/s at the 2×10-2 FEC threshold.The AIR of the REGHY is at most 0.36Gb/s higher than that of 64QAM at 0.8V Vpp and 7%FEC threshold,while the(achievable information rate)AIR of SGSHY is at most 0.40Gb/s higher than that of 64QAM at 1.4V Vpp and 20%FEC threshold.

Demonstration of a low‐complexity memory‐polynomial‐aided neural network equalizer for CAP visible‐light communication with superluminescent diode

Visible-light communication(VLC)stands as a promising component of the future communication network by providing high-capacity,low-latency,and high-security wireless communication.Superluminescent diode(SLD)is proposed as a new light emitter in the VLC system due to its properties of droop-free emission,high optical power density,and low speckle-noise.In this paper,we analyze a VLC system based on SLD,demonstrating effective implementation of carrierless amplitude and phase modulation(CAP).We create a low-complexity memory-polynomial-aided neural network(MPANN)to replace the traditional finite impulse response(FIR)post-equalization filters of CAP,leading to significant mitigation of the linear and nonlinear distortion of the VLC channel.The MPANN shows a gain in Q factor of up to 2.7 dB higher than other equalizers,and more than four times lower complexity than a standard deep neural network(DNN),hence,the proposed MPANN opens a pathway for the next generation of robust and efficient neural network equalizers in VLC.We experimentally demonstrate a proof-of-concept 2.95-Gbit/s transmission using MPANN-aided CAP with 16-quadrature amplitude modulation(16-QAM)through a 30-cm channel based on the 442-nm blue SLD emitter.

Common-anode LED on a Si substrate for beyond 15 Gbit/s underwater visible light communication

Visible light communication based on light-emitting diodes(LEDs) has become a promising candidate by providing high data rates, low latency, and secure communication for underwater environments. In this paper,a self-designed common-anode GaN-based five-primary-color LED(RGBYC LED) on a Si substrate is proposed and fabricated. The design of a common anode is used to mitigate the saturation effect for a low-frequency component. Additionally, compared with commercially available LEDs that suffer from nonlinearity distortion,applying the designed LED can provide much better and broader linearity according to the measurement results.Therefore, the modulation depth and system performance can be further improved to implement a high-speed underwater visible light communication(UVLC) system. There is no nonlinearity compensation algorithm applied due to the good linearity of the proposed LED;thus, the offline digital signal processing is simplified. We experimentally demonstrate 14.81 Gbit/s 64 quadrature amplitude modulation(QAM)-discrete multitone(DMT)and 15.17 Gbit/s bit-loading-DMT transmissions through a 1.2-m-long underwater channel based on the proposed RGBYC LED with an intrasymbol frequency-domain averaging channel estimation and zero-forcing equalization.As far as we know, this is the highest data rate for an LED-based UVLC system.

Visible light communication for Vehicle to Everything beyond 1 Gb/s based on an LED car headlight and a 2 × 2 PIN array

Visible light communication(VLC) shows great potential in Internet of Vehicle applications. A single-input multi-output VLC system for Vehicle to Everything is proposed and demonstrated. A commercial car headlight is used as transmitter. With a self-designed 2 × 2 positive-intrinsic-negative(PIN) array, four independent signals are received and equalized by deep-neural-network post-equalizers, respectively. Maximum-ratio combining brings high signal-to-noise ratio and data rate gain. The transmission data rate reaches 1.25 Gb/s at 1 m and exceeds 1 Gb/s at 4 m. To the best of our knowledge, it is the first-time demonstration of beyond 1 Gb/s employing a commercial car headlight.

Nonlinear adaptive filters for high-speed LED based underwater visible light communication [Invited]

Underwater visible light communication(UVLC)is expected to act as an alternative candidate in nextgeneration underwater 5G wireless optical communications.To realize high-speed UVLC,the challenge is the absorption,scattering,and turbulence of a water medium and the nonlinear response from imperfect optoelectronic devices that can bring large attenuations and a nonlinearity penalty.Nonlinear adaptive filters are commonly used in optical communication to compensate for nonlinearity.In this paper,we compare a recursive least square(RLS)-based Volterra filter,a least mean square(LMS)-based digital polynomial filter,and an LMS-based Volterra filter in terms of performance and computational complexity in underwater visible light communication.We experimentally demonstrate 2.325 Gb/s transmission through 1.2 m of water with a commercial blue light-emitting diode.Our goal is to assist the readers in refining the motivation,structure,performance,and cost of powerful nonlinear adaptive filters in the context of future underwater visible light communication in order to tap into hitherto unexplored applications and services.

Si-substrate LEDs with multiple superlattice interlayers for beyond 24 Gbps visible light communication

High-speed visible light communication(VLC)using light-emitting diodes(LEDs)is a potential complementary technology for beyond-5 G wireless communication networks.The speed of VLC systems significantly depends on the quality of LEDs,and thus various novel LEDs with enhanced VLC performance increasingly emerge.Among them,In Ga N/Ga N-based LEDs on a Si-substrate are a promising LED transmitter that has enabled VLC data rates beyond 10 Gbps.The optimization on the period number of superlattice interlayer(SL),which is a stressrelief epitaxial layer in a Si-substrate LED,has been demonstrated to be an effective method to improve Si-substrate LED’s luminescence properties.However,this method to improve LED’s VLC properties is barely investigated.Hence,we for the first time experimentally studied the impact of SL period number on VLC performance.Accordingly,we designed and fabricated an integrated 4×4 multichromatic Si-substrate wavelength-divisionmultiplexing LED array chip with optimal SL period number.This chip allows up to 24.25 Gbps/1.2 m VLC transmission using eight wavelengths,which is the highest VLC data rate for an In Ga N/Ga N LED-based VLC system to the best of our knowledge.Additionally,a record-breaking data rate of 2.02 Gbps over a 20-m VLC link is achieved using a blue Si-substrate LED with the optimal SL period number.These results validate the effectiveness of Si-substrate LEDs for both high-speed and long-distance VLC and pave the way for Si-substrate LED design specially for high-speed VLC applications.

Challenges and Prospects of Machine Learning in Visible Light Communication

Visible light communication(VLC)is a promising research field in modern wireless communication.VLC has its irreplaceable strength including rich spectrum resources,no electromagnetic disturbance,and high-security guarantee.However,VLC systems suffer from the non-linear effects that exist in almost every part of the system.As a part of artificial intelligence,machine learning(ML)is showing its potential in non-linear mitigating for its natural ability to fit all kinds of transfer functions,which may dramatically push the research in VLC.This paper introduces the application of ML in VLC,describes five recent research of deep learning applications in VLC,and analyses the performance.