In color-multiplexed optical camera communications (OCC) systems, data acquisition is restricted by the image processing algorithm capability for fast source recognition, region-of-interest (ROI) detection and tracking, packet synchronization within ROI, estimation of inter-channel interference and threshold computation. In this work, a novel modulation scheme for a practical RGB-LED-based OCC system is presented. The four above-described tasks are held simultaneously. Using confined spatial correlation of well-defined reference signals within the frame’s color channels is possible to obtain a fully operating link with low computational complexity algorithms. Prior channel adaptation also grants a substantial increase in the attainable data rate, making the system more robust to interferences.

In rolling shutter-based optical camera communication (OCC), the camera’s exposure time limits the achievable reception bandwidth. In long-exposure settings, the image sensor pixels average the incident received power, producing inter-symbol interference (ISI), which is perceived in the images as a spatial mixture of the symbol bands. Hence, the shortest possible exposure configuration should be selected to alleviate ISI. However, in these conditions, the camera produces dark images with impracticable light conditions for human or machine-supervised applications. In this paper, a novel convolutional autoencoder-based equalizer is proposed to alleviate exposure-related ISI and noise. Furthermore, unlike other systems that use artificial neural networks for equalization and decoding, the training procedure is conducted offline using synthetic images for which no prior information about the deployment scenario is used. Hence the training can be performed for a wide range of cameras and signal-to-noise ratio (SNR) conditions, using a vast number of samples, improving the network fitting and the system decoding robustness. The results obtained in the experimental validation record the highest ISI mitigation potential for Manchester encoded on-off keying signals. The system can mitigate the ISI produced by exposure time windows that are up to seven times longer than the transmission symbol duration, with bit error rates (BER) lower than 10⁻⁵ under optimal SNR conditions. Consequently, the reception bandwidth improves up to 14 times compared to non-equalized systems. In addition, under harsh SNRs conditions, the system achieves BERs below the forward error correction limit for 1dB and 5 dB while operating with exposure times that are 2 and 4 times greater than the symbol time, respectively.

Optical wireless communications in outdoor scenarios are challenged by uncontrollable atmospheric conditions that impair the channel quality. In this paper, different optical camera communications (OCC) equipment are experimentally studied in the laboratory and the field, and a sub-pixel architecture is raised as a potential solution for outdoor wireless sensor networks (WSN) applications, considering its achievable data throughput, the spatial division of sources, and the ability of cameras to overcome the attenuation caused by different atmospheric conditions such as rain, turbulence and the presence of aerosols. Sub-pixel OCC shows particularly adequate capabilities for some of the WSN applications presented, also in terms of cost-effectiveness and scalability. The novel topology of sub-pixel projection of multiple transmitters over the receiver using small optical devices is presented as a solution using OCC that re-uses camera equipment for communication purposes on top of video-monitoring.

In rolling shutter (RS)-based optical camera communication (OCC) links, selecting the appropriate camera’s exposure time is critical, as it limits the reception bandwidth. In long exposures, the pixels accumulate over time the incoming irradiance of several consecutive symbols. As a result, a harmful intersymbol interference corrupts the received signal. Consequently, reducing the exposure time is required to increase the reception bandwidth at the cost of producing dark images with impracticable light conditions for human or machine-supervised applications. Alternatively, deep learning (DL) equalizers can be trained to mitigate the exposure-related ISI. These equalizers must be trained considering the transmitter clock and the camera’s exposure, which can be exceptionally challenging if those parameters are unknown in advance (e.g., if the camera does not reveal its internal settings). In those cases, the receiver must estimate those parameters directly from the images, which are severely distorted by the exposure time. This work proposes a DL estimator for this purpose, which is trained using synthetic images generated for thousands of representative cases. This estimator enables the receiver operation under multiple possible configurations, regardless of the camera used. The results obtained during the validation, using more than 7000 real images, registered relative errors lower than 1% and 2% when estimating the transmitter clock and the exposure time, respectively. The obtained errors guarantee the optimal performance of the following equalization and decoding receiver stages, keeping bit error rates below the forward error correction limit. This estimator is a central component of any OCC receiver that operates over moderate exposure conditions. It decouples the reception routines from the cameras used, ultimately enabling cloud-based receiver architectures.

The rolling shutter (RS) acquisition mechanism performed by the majority of the digital cameras included in portable devices has been exploited to make receivers for visible light communications (VLC). They are able to decode signals of frequencies higher than the frame rate of the camera, which is a limitation when using global shutter sensors. Based on experience with an experimental optical camera communications system, a simulation of rolling shutter receiver for OCC has been developed. This simulator uses a color-multiplexed on-off keying (OOK) modulation, along with a radiance pattern for the LED source, to generate transmitted signals. Then, a simplified camera optics model translates them into received signals, to which the RS simulation is applied, delivering synthetic camera frames.

Visible light communications (VLC) technology is emerging as a candidate to meet the demand for interconnected devices’ communications. However, the costs of incorporating specific hardware into end-user devices slow down its market entry. Optical camera communication (OCC) technology paves the way by reusing cameras as receivers. These systems have generally been evaluated under static conditions, in which transmitting sources are recognized using computationally expensive discovery algorithms. In vehicle-to-vehicle networks and wearable devices, tracking algorithms, as proposed in this work, allow one to reduce the time required to locate a moving source and hence the latency of these systems, increasing the data rate by up to 2100%. The proposed receiver architecture combines discovery and tracking algorithms that analyze spatial features of a custom RGB LED transmitter matrix, highlighted in the scene by varying the cameras’ exposure time. By using an anchor LED and changing the intensity of the green LED, the receiver can track the light source with a slow temporal deterioration. Moreover, data bits sent over the red and blue channels do not significantly affect detection, hence transmission occurs uninterrupted. Finally, a novel experimental methodology to evaluate the evolution of the detection’s performance is proposed. With the analysis of the mean and standard deviation of novel K parameters, it is possible to evaluate the detected region-of-interest scale and centrality against the transmitter source’s ideal location.