A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival

Data rate and communication distance are two important criteria for measuring the performance of optical camera communication (OCC) systems. Intensive researches have been made to find possible solutions to improve these two performance indicators. However, most existing literature either provides solutions which closely related to the experimental parameters of their own designed OCC systems or provides models which are suitable for visible light communication (VLC) and cannot be directly used in OCC. Motivated by this, we propose a simplified system model for OCC in this paper. By jointly applying Lambertian model for the transmitter light-emitting diode (LED), channel direct current (DC) gain for free space channel, and mathematical descriptions for the imaging process of the camera receiver, the proposed model provides the relationship between the optical transmission power and the final pixel values on the image sensor. We specifically explore the parameters of the transmission distance and the exposure time based on the proposed model. Simulation results qualitatively and quantitatively explore the influences of these two parameters on the final pixel values. Our proposed model can provide guidance for the design of a generalized OCC system as well as the selection of demodulating threshold, thus it can help to realize a highperformance OCC system.

To enable a higher transmission rate in optical camera communication (OCC) systems, severe inter-symbol interference (ISI) occurs owing to the reduction of the number of pixel-row-per-bit (PPB). Therefore, those pixels representing data bit 0 or 1 may have same gray scale values, significantly deteriorating the bit decision when using the conventional thresholding scheme. In this Letter, a simple yet efficient scheme, referred to as nearest neighbor bit assisted decision (NNBAD) scheme, is proposed and experimentally demonstrated for signal decision in OCC systems. NNBAD leverages the nearest neighbor bit to jointly assist bit decision for pixels with severe ISI. Experimental results show that, for OCC systems with on-off keying (OOK) modulation, those pixels with severe ISI cannot be distinguished by the conventional thresholding scheme. Yet, the NNBAD scheme exhibits strong robustness against ISI, remarkably improving the bit error rate (BER) performance. The proposed scheme can achieve a throughput of 8.2 kbps with OOK modulation under an illuminance of 600 lx.

In order to increase the data rate of the optical camera communication (OCC) system, the 8-composite-amplitude-shift-keying modulation (8CASK) OCC system is used in this work. However, if the static decision thresholds are employed to demodulate multi-level ASK signal, uneven illumination of LED lamps and LED light-emitting instability lead to the fluctuation of the gray range in the picture and degrade the bit-error-rate (BER) performance. In this work, we propose and demonstrate a demodulation scheme, using the uneven illumination compensation algorithm, the pixel matrix threshold overall update algorithm and the secondary decision algorithm, to mitigate the impact of illumination unevenness and LED light-emitting instability. The BER performance is evaluated and compared with other demodulation schemes. The experimental results demonstrate that the communication rate of our proposed scheme can reach 9kbit/s at a distance of 250 cm where the illumination is 135lux, and the BER is 8.01 × 10−5.

Nowadays, underwater activities are becoming more and more important. As the number of underwater sensing devices grows rapidly, the amount of bandwidth needed also increases very quickly. Apart from underwater communication, direct communication across the water–air interface is also highly desirable. Air-to-water wireless transmission is crucial for sending control information or instructions from unmanned aerial vehicles (UAVs) or ground stations above the sea surface to autonomous underwater vehicles (AUVs). On the other hand, water-to-air wireless transmission is also required to transmit real-time information from AUVs or underwater sensor nodes to UAVs above the water surface. Previously, we successfully demonstrated a water-to-air optical camera-based OWC system, which is also known as optical camera communication (OCC). However, the reverse transmission (i.e., air-to-water) using OCC has not been analyzed. It is worth noting that in the water-to-air OCC system, since the camera is located in the air, the image of the light source is magnified due to diffraction. Hence, the pixel-per-symbol (PPS) decoding of the OCC pattern is easier. In the proposed air-to-water OCC system reported here, since the camera is located in the water, the image of the light source in the air will be diminished in size due to diffraction. Hence, the PPS decoding of the OCC pattern becomes more difficult. In this work, we propose and experimentally demonstrate a wide field-of-view (FOV) air-to-water OCC system using CUDA Deep-Neural-Network Long-Short-Term-Memory (CuDNNLSTM). Due to water turbulence and air turbulence affecting the AUV and UAV, a precise line-of-sight (LOS) between the AUV and the UAV is difficult to achieve. OCC can provide wide FOV without the need for precise optical alignment. Results revealed that the proposed air-to-water OCC system can support a transmission rate of 7.2 kbit/s through a still water surface, and 6.6 kbit/s through a wavy water surface; this satisfies the hard-decision forward error correction (HD-FEC) bit-error-rate (BER).

As mobile payments become increasingly popular, the security of the transaction process should be taken more seriously. Many mobile phones are equipped with complementary metal-oxide-semiconductor (CMOS) cameras, which can be used as visible light communication (VLC) receivers. An optical camera communication (OCC) system is a special VLC system that uses an optical camera as a receiver. OCC offers acceptable security, but challenges remain. In this paper, we propose an OCC system that uses an optical camera with a rolling shutter as a receiver and a light-emitting diode (LED) panel light as a transmitter. The feature of an LED panel light serving as a surface light source reduces the possibility of overexposure and avoids the blooming effect of the camera. The rolling shutter effect of the CMOS camera is used to increase the data rate, which is numerically much higher than the frame rate. Finally, the results of a symbol error rate (SER) test show that the VLC system exhibits error-free performance. The data rate is approximately 4.2kb/s, while the net data rate is approximately 2.46kb/s due to the redundancy of the 3-9 code bar code and the redundant cycle. This data rate is sufficient for mobile payments, which require only 60bits of data at a time.

Optical camera communication (OCC) shows promise for optical wireless communication (OWC) in vehicular networks. However, vehicle mobility-induced angular distortions hinder system throughput by degrading non-isotropic vehicular OCC channel gain. Few of the prior works have ever made a comprehensive analysis of their impact, especially based on the pixel value which reflects the camera imaging features. To address this knowledge gap, a pixel value-described vehicular OCC system model accounting for transmitter imaging location and intensity from the geometry and radiometry aspects is presented in this paper with common types of the offset and rotation angles included. We integrate a MATLAB-based simulated vehicular OCC system with an experimentally designed testbed for validation and performance analysis. For a single-time snapshot, we investigate the impacts of common angular distortion types in vehicular OCC systems on maximum pixel value, imaging location, and communication-related metrics. Furthermore, we statistically analyze their influences by considering two driving scenarios with respective angular distributions. The angular distortion characterization from this work is expected to lay a stepping stone to addressing mobility in vehicular OCC systems.

This paper presents a method for performing traffic light detection using computer vision. Reliable traffic light detection and classification is crucial for automated driving in urban environments. By using big data and artificial intelligence, a complex dataset belonging to an urban area in China is preprocessed to determine the level of vehicular congestion, and then different machine learning algorithms are applied to a dataset of traffic light images in order to validate them in the urban environment to be studied, this process is explained step by step. The models obtained in this work can be applied in optical camera communication (OCC) systems, and also in intelligent transportation systems (ITS), using tracking channels for visible light communication (VLC). The two optical channels, VLC and OCC, are simulated in terms of the quality of information received in order to apply the previously generated datasets. In this work, a traffic light feature dataset has been generated from images and two traffic light classification models present in images and video frames have been generated from their features, obtaining a maximum accuracy of 94.52 %.

In this work, we propose a multiple inputs multiple outputs (MIMO) non-line-of-sight (NLOS) optical camera communication (OCC) system with multi-level pulse width modulation (MPWM) and difference-based pulse width recognition (DBPWR) schemes. The MPWM signals transmitted over a NLOS link are recovered using the DBPWR algorithm. Compared with the conventional threshold-based demodulation scheme, the proposed DBPWR scheme has lower complexity, higher reliability, and improved sampling frequency offset tolerance. A total data rate of 3.6 kb/s is experimentally demonstrated for more than 2 m NLOS link using the proposed MIMO OCC system, which is sufficient in many Internet of things applications.

Real-time implementation of OFDM modulation for an OCC system: UNet-based equalizer for signal denoising and BER optimization

This paper experimentally investigates the performance of visible light based optical camera communications (OCC) link with motion detection (MD) for the optical Internet of things applications. This efficient MD can be considered another functionality of OCC in addition to traditional features of vision, illumination data communications and sensing. The experiments were conducted in an indoor static downlink OCC system employing a mobile phone front camera is employed as the receiver and an $8\times 8$ red, green, and blue (RGB) light-emitting diode array as the transmitter. The motion is detected by observing the user's finger movement in the form of centroid through the OCC link via a camera. The experiment results demonstrate that, the proposed scheme can detect all considered motions accurately with acceptable bit error rate (BER) performances at a transmission distance of up to 80 cm. We show a BER of $1.7\times 10^{-3}$ below the forward error correction limit of $3.8\times 10^{-3}$ over a transmission distance of up to 1 m. The proposed neuron based MD combined together with OCC can be considered an efficient system, which provides illumination, communications, and motion detection in a convenient smart home environment.

Optical wireless communication (OWC) has received increasing attention recently. One application of OWC uses image sensor as optical receiver, which is known as optical camera communication (OCC). Rolling shutter mode in OCC allows data rate higher than the camera frame-rate operation. However, the row by row exposure delay will create decoding challenge, and high inter-symbol interference (ISI) can be observed in the rolling shutter pattern. In this work, we propose and demonstrate an OCC system employing pixel-per-bit (PPB) as label for the deep neural network (PPB-NN). Besides, two rolling shutter patterns emitted by two LED light panels can be decoded simultaneously. The proposed PPB-NN is also compared with other decoding schemes in the literature, and the experimental results revealed that the proposed scheme can achieve a better bit-error ratio (BER) performance.

This research presents a novel approach by applying convolutional neural networks (CNNs) to enhance optical camera communication (OCC) signal detection under challenging indoor lighting conditions. The study utilizes a smartphone app to capture images of an LED lamp that emits 25 unique optical codes at distances of up to four meters. The developed CNN model demonstrates superior accuracy and outperforms traditional methodologies, which often struggle under variable illumination. This advancement provides a robust solution for reliable OCC detection where previous methods underperform, particularly in the tourism industry, where it can be used to create a virtual museum on the Unity platform. This innovation showcases the potential of integrating the application with a virtual environment to enhance tourist experiences. It also establishes a comprehensive visible light positioning (VLP) system, marking a significant advance in using CNN for OCC technology in various lighting conditions. The findings underscore the effectiveness of CNNs in overcoming ambient lighting challenges, paving the way for new applications in museums and similar environments and laying the foundation for future OCC system improvements.

Mitigation of PAM4 rolling shuttered pattern grayscale ambiguity in demodulation utilizing long short term memory neural network (LSTM-NN) in optical wireless communication systems

LEDs are quickly becoming the standard lighting solution. Their ability to be turned on and off fast determined scientists to create novel communication methods, such as Visible Light Communication (VLC), in which the transmitter is commonly represented by LEDs and the receiver is represented by photodetectors. The receiver in Optical Camera Communications (OCC) is an image sensor. Designing a communication system that can use low frame rate cameras (30 frames per second) while preventing light flickering at the transmitter is a specific problem in OCC. This work proposes and experimentally confirms an improvement of an OCC solution that does not cause flickering at the transmitter and does not require synchronization between the transmitter and the receiver, while improving the bit rate.

Optical camera communication (OCC) provides license-free communication channels without radio waves. The rolling shutter mechanism is expected to enhance the capacity of wireless links in OCC. However, signal erasure occurs due to waiting times between frames in consecutive signal transmission. To address this issue, we propose a symbol timing synchronization for OCC using a rolling shutter camera. The receiver camera synchronizes to the transmitter with additional waiting time which is computed based on the defined preamble signals. We analyzed the performance to clarify the theoretical limit. The feasibility of the proposed idea was confirmed with experimental results.