We present a real-time indoor visible light positioning system based on optical camera communication, where the coordinate data in the on and off keying format is transmitted via light emitting diode (LED) based lights and captured using a smartphone camera. The position of camera is determined using a novel Perspective-n-Point problem algorithm, which determines the geometric perspective of the LED coordinates. The experimental results show that the proposed system offers mean position errors of 4.81 and 6.58 cm for the heights of 50 cm and 80 cm, respectively.

We propose a non-orthogonal multiple access (NOMA) scheme combined with orthogonal frequency division multiplexing access (OFDMA) for visible light communications (VLC), which offers a high throughput, flexible bandwidth allocation and a higher system capacity for a larger number of users. Bidirectional NOMA-OFDMA VLC is experimentally demonstrated. The effects of power allocation and channel estimation on the bit error rate performance are investigated. The experiment results indicate that accurate channel estimation can eliminate the inter-user interference more effectively. The optimum power allocation ratios for uplink and downlink are both about 0.25 in the case of two users.

We experimentally demonstrate an indoor visible light positioning system based on optical camera communications, in which the transmitted coordinate data are spatially separated and demodulated by a camera. The receiver's position is calculated based on the coordinates of light-emitting diodes in the real word and in the image. The experimental results show that the proposed system with under-sampled phase shift keying modulation offers an error free data transmission and mean positioning errors of 5.0 and 6.6 cm for $h$ of 120 and 180 cm, respectively.

Non-orthogonal multiple access (NOMA) is a promising scheme to improve the spectral efficiency, user fairness, and overall throughput in visible light communication (VLC) systems. However, the error propagation problem together with linear and nonlinear distortions induced by multipath, limited modulation bandwidth, and nonlinearity of light emitting diode significantly limit the transmission performance of NOMA-VLC systems. In addition, having an accurate channel state information, which is important in the recovery of NOMA signal, in mobile wireless VLC is challenging. In this work, we propose a convolutional neural network (CNN) based demodulator for NOMA-VLC, in which signal compensation and recovery are jointly realized. Both simulation and experiment results show that, the proposed CNN based demodulator can effectively compensate for both linear and nonlinear distortions, thus achieving improved bit error ratio performance compared with the successive interference cancellation and joint detection based receivers. Compared to SIC, the performance gains are 1.9, 2.7, and 2.7 dB for User1 for the power allocation ratios (PARs) of 0.16, 0.25, and 0.36, respectively, which are 4, 4 and 2.6 dB for User2 for PARs of 0.16, 0.25, and 0.36, respectively.