Data-efficient Gaussian process regression for accurate visible light positioning

In the field of indoor localization systems, Received Signal Strength (RSS) based Visible Light Positioning (VLP) has gained increased attention due to the dual functionality of lighting and localization. Previously geometrical models have been used to determine the position of a mobile entity, however these are unsuited when dealing with tilted surfaces and non-Lambertian sources. For this reason, machine learning techniques like Multi Layer Perceptrons (MLPs) have been considered recently. In this work, Gaussian Processes (GPs) are introduced in the context of RSS-based VLP, since they have proven to work well when using small, noisy datasets for different applications. Their performance is evaluated using both simulated data with a small transmitter tilt tolerance and measurements. It is demonstrated that the GP model outperforms both the multilateration approach and the MLP approach for the simulations and measurements data

Comprehensive Investigation and Evaluation of an Indoor 3D System Performance Based on Visible Light Communication

The abstract discusses the significance of Visible Light Communication (VLC) as an efficient and cost-effective solution in the era of green technology. VLC not only provides illumination but also high-speed data transmission through existing infrastructure, making it ideal for indoor positioning systems (IPS) with minimal interference with the Radio Frequency (RF) spectrum and enhanced security. While previous research has mainly focused on positioning accuracy, this paper delves into the performance evaluation of a VLC-based indoor system. The study examines key performance parameters, namely Signal-to-Noise Ratio (SNR) and path loss, in a Line of Sight (LOS) scenario. It employs a single LED and ten different photodiode (PD) locations in a 3D room. MATLAB simulations demonstrate the system's effectiveness, achieving a good SNR with low path loss. Additionally, the research highlights the importance of optimizing the PD's position to maximize signal strength while minimizing noise and losses

Experimental evaluation of machine learning methods for robust received signal strength-based visible light positioning

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios

How Well Sensing Integrates with Communications in MmWave Wi-Fi?

The development of integrated sensing and communication (ISAC) systems has recently gained interest for its ability to offer a variety of services including resources sharing and new applications, for example, localization, tracking, and health care related. While the sensing capabilities are offered through many technologies, rending to their wide deployments and the high frequency spectrum they provide and high range resolution, its accessibility through the Wi-Fi networks IEEE 802.11ad and 802.11ay has been getting the interest of research and industry. Even though there is a dedicated standardization body, namely the 802.11bf task group, working on enhancing the Wi-Fi sensing performance, investigations are needed to evaluate the effectiveness of various sensing techniques. In this project, we, in addition to surveying related literature, we evaluate the sensing performance of the millimeter wave (mmWave) Wi-Fi systems by simulating a scenario of a human target using Matlab simulation tools. In this analysis, we processed channel estimation data using the short time Fourier transform (STFT). Furthermore, using a channel variation threshold method, we evaluated the performance while reducing feedback. Our findings indicate that using STFT window overlap can provide good tracking results, and that the reduction in feedback measurements using 0.05 and 0.1 threshold levels reduces feedback measurements by 48% and 77%, respectively, without significantly degrading performance.Comment: arXiv admin note: substantial text overlap with arXiv:2207.04859 by other author

Automated Guidance Vehicles Controlled by Visible ​Light Communication

The advent of devices with wireless communication capabilities has generated increased interest in indoor navigation. Several wireless technologies have been proposed for indoor location, as the traditional Global Positioning System has a poor performance in a closed space. This research proposes the use of an indoor localization system based on Visible Light Communication (VLC) to support guidance and operational tasks of Autonomous Guided Vehicles (AVG). The research is focused on the development of the guidance VLC system, transmission of control data information and decoding techniques. Trichromatic white LEDs are used as transmitters and photodiodes with selective spectral sensitivity are used as receivers. The downlink channel establishes an infrastructure-to-vehicle link (I2V) and provides position information to the vehicle. The decoding strategy is based on accurate calibration of the output signal. Characterization of the transmitters and receivers, description of the coding schemes and the use of different modulations will be discussed

Sparsity Signal Detection for Indoor GSSK-VLC System

In this paper, the signal detection problem in indoor visible light communication (VLC) system aided by generalized space shift keying (GSSK) is modeled as a sparse signal reconstruction problem, which has lower computational complexity by exploiting the sparse reconstruction algorithms in compressed sensing (CS). In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed based on singular value decomposition (SVD), which theoretically guarantees the feasibility of utilizing CS based sparse signal detection method in indoor GSSK-VLC system. Then, by adopting classical orthogonal matching pursuit (OMP) algorithm and compressed sampling matching pursuit (CoSaMP) algorithm, the GSSK signals are efficiently detected in the considered indoor GSSK-VLC system. Furthermore, a more efficient detection algorithm combined with OMP and maximum likelihood (ML) is also presented especially for SSK scenario. Finally, the effectiveness of the proposed sparsity aided detection algorithms in indoor GSSK-VLC system are verified by computer simulations. The results show that the proposed algorithms can achieve better bit error rate (BER) and lower computation complexity than ML based detection method. Specifically, a signal-to-noise ratio (SNR) gain as high as 12 dB is observed in the SSK scenario and about 5 dB in case of a GSSK scenario upon employing our proposed detection methods