Visible Light and Camera-based Receiver Employing Machine Learning for Indoor Positioning Systems and Data Communications

Indoor location-based services have played a crucial role in the development of various Internet of Things applications over the last few decades. The use of radio frequency (RF)-based systems in indoor environments suffers from additional interference due to the high penetration rate and reflections of the RF, which may severely affect positioning accuracy. Alternatively, the optical technology using the existing light-emitting diode (LED)-based lights, photodetectors (PDs), and/or image sensors could be utilised to provide indoor positioning with high accuracy. Because of its resilience to electromagnetic interference, license-free operation, large bandwidth, and dual-use for illumination and communication, visible light positioning (VLP) systems have shown great potential in achieving high-precision indoor positioning. This thesis focus is on investigating VLP systems based on employing a single PD, or an array of PDs in the form of a single image sensor (i.e. a camera) for both localization and data communication. Following a comprehensive literature review on VLP, the key challenges in existing positioning methods for achieving a low-cost, accurate, and less complex indoor positioning systems design are highlighted by considering the design characteristics of an indoor environment, position accuracy, number of light-emitting LED, PD, and any additional sensors utilized. The thesis focuses on the major constraints of VLP and provides novel contributions. In most reported VLP schemes, the assumptions of fixed transmitter (Tx) angle and height may not be valid in many physical environments. In this work, the impact of tilting Tx and multipath reflections are investigated. The findings demonstrated that tilting Tx can be beneficial in VLP by leveraging the influence of reflections from both near- and far-walls. It also showed that proposed system offers a significant accuracy improvement by up to ~66% compared with a typical non-tilted Tx VLP system.Furthermore, increasing robustness of image sensor-based receiver (Rx) is a major challenge, which is being addressed using a novel angle of arrival-received signal intensity and a single LED. Experimental results show that the proposed algorithm can achieve a three-dimensional root mean squared error of 7.56 cm. Visible light communications employing a camera-based Rx is best known as optical camera communications (OCC), which can also be used for VLP. However, in OCC the transmission data rate is mainly limited by the exposure time and the frame rate of the camera. In addition, the camera's sampling introduces intersymbol interference Indoor location-based services have played a crucial role in the development of various Internet of Things applications over the last few decades. The use of radio frequency (RF)-based systems in indoor environments suffers from additional interference due to the high penetration rate and reflections of the RF, which may severely affect positioning accuracy. Alternatively, the optical technology using the existing light-emitting diode (LED)-based lights, photodetectors (PDs), and/or image sensors could be utilised to provide indoor positioning with high accuracy. Because of its resilience to electromagnetic interference, license-free operation, large bandwidth, and dual-use for illumination and communication, visible light positioning (VLP) systems have shown great potential in achieving high-precision indoor positioning. This thesis focus is on investigating VLP systems based on employing a single PD, or an array of PDs in the form of a single image sensor (i.e. a camera) for both localization and data communication. Following a comprehensive literature review on VLP, the key challenges in existing positioning methods for achieving a low-cost, accurate, and less complex indoor positioning systems design are highlighted by considering the design characteristics of an indoor environment, position accuracy, number of light-emitting LED, PD, and any additional sensors utilized. The thesis focuses on the major constraints of VLP and provides novel contributions. In most reported VLP schemes, the assumptions of fixed transmitter (Tx) angle and height may not be valid in many physical environments. In this work, the impact of tilting Tx and multipath reflections are investigated. The findings demonstrated that tilting Tx can be beneficial in VLP by leveraging the influence of reflections from both near- and far-walls. It also showed that proposed system offers a significant accuracy improvement by up to ~66% compared with a typical non-tilted Tx VLP system.Furthermore, increasing robustness of image sensor-based receiver (Rx) is a major challenge, which is being addressed using a novel angle of arrival-received signal intensity and a single LED. Experimental results show that the proposed algorithm can achieve a three-dimensional root mean squared error of 7.56 cm. Visible light communications employing a camera-based Rx is best known as optical camera communications (OCC), which can also be used for VLP. However, in OCC the transmission data rate is mainly limited by the exposure time and the frame rate of the camera. In addition, the camera's sampling introduces intersymbol interference

Data rate enhancement in optical camera communications using an artificial neural network equaliser

In optical camera communication (OCC) systems leverage on the use of commercial off-the-shelf image sensors to perceive the spatial and temporal variation of light intensity to enable data transmission. However, the transmission data rate is mainly limited by the exposure time and the frame rate of the camera. In addition, the camera’s sampling will introduce intersymbol interference (ISI), which will degrade the system performance. In this paper, an artificial neural network (ANN)-based equaliser with the adaptive algorithm is employed for the first time in the field of OCC to mitigate ISI and therefore increase the data rate. Unlike other communication systems, training of the ANN network in OCC is done only once in a lifetime for a range of different exposure time and the network can be stored with a look-up table. The proposed system is theoretically investigated and experimentally evaluated. The results record the highest bit rate for OCC using a single LED source and the Manchester line code (MLC) non-return to zero (NRZ) encoded signal. It also demonstrates 2 to 9 times improved bandwidth depending on the exposure times where the system’s bit error rate is below the forward error correction limit.publishe

The Usage of ANN for Regression Analysis in Visible Light Positioning Systems

In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error (εp) in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum εp of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that εp is low even for lower values of SNR, i.e., εp values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively

Electro‐optical spiking neural networks using an enhanced optical axon with pulse amplitude modulation and automatic gain controller

Abstract Visible light communication can be leveraged to establish a wireless link between neurons in spiking networks even when neural areas are in relative motions. In electro‐optical spiking neural networks (SNN), parallel transmission is often achieved through wavelength division multiplexing (WDM). However, WDM can be prohibitive in certain applications due to the need for multiple narrow‐band transmitters and receivers with optical bandpass filters. Instead of WDM, an alternative approach of using non‐orthogonal multiple access is explored (NOMA) with a pulse amplitude modulation (PAM) scheme in optical axons to enable parallel neural paths in an SNN. To evaluate NOMA with PAM, the authors implement an electro‐optical SNN that controls the force of two anthropomorphic fingers actuated by the shape memory alloy‐based actuators. An optical reference channel is used to dynamically adjust the optical receiver's gain to improve the receiver's decoding performance. Experimental results demonstrate that the electro‐optical SNN can maintain control over the fingers and hold an object under varying channel conditions. Hence, the proposed system offers robustness against dynamic optical channels induced by the relative motion of neurons

Optical camera communications with convolutional neural network for vehicle-tovehicle links

This paper describes a vehicle-to-vehicle (V2V) communication system, employing optical camera communications (OCC). The system comprises the light emitting diode (LED)-based taillights and a raspberry camera used as the transmitter (Tx) and the receiver (Rx), respectively. The sectorized taillights (i.e., Tx) are intensity modulated at different frequencies, and a convolutional neural network (CNN) at the Rx is used for scene analysis, the region of interest (RoI) selection, and symbol detection. Results show that, the system data rates are constrained by the camera frame rate and symbol duration. The link performance is dependent on the CNN training set and we show that, the use of CNN allows a robust implementation, able to provide response under multiple situations: taillight obstruction, variable link distances, and misaligned Tx-Rx. Furthermore, CNN enables multiple input multiple output (MIMO) signal detection without the need for dedicated training.publishe

A unilateral 3D indoor positioning system employing optical camera communications

Abstract This article investigates the use of a visible light positioning system in an indoor environment to provide a three dimensional (3D) high‐accuracy solution. The proposed system leveraged the use of a single light‐emitting diode and an image sensor at the transmitter and the receiver (Rx) respectively. The proposed system can retrieve the 3D coordinate of the Rx using a combination of the angle of arrival and received signal strength (RSS). To mitigate the error induced by the lens at the Rx, a novel method is proposed and experimentally tested. The authors show that, the proposed method outperforms previously reported RSS under all circumstances and it is immune to varying exposure times within the standard range of 250 µs to 4 ms. The authors experimentally demonstrate that the proposed algorithm can achieve a 3D root mean squared error of 7.56 cm

An artificial neural network equalizer for constant power 4-PAM in optical camera communications

In this paper, for the first time, we propose and validate a nonlinear artificial neural network (ANN) equalizer for constant power pulse amplitude modulation (CP 4-PAM) in an optical camera communication (OCC) system. Using the proposed equalizer, we demonstrate experimental based non-flickering transmission at a data rate of up to 7 kbps using a single light-emitting diode and an image sensor with 30 frames per second rate. The quality of the received signal is introduced based on the eye-diagram opening where we have proved an ability to transmit data with up to 232 bits per frame after the equalizer deployment.publishe