In this paper, we apply the non-orthogonal multiple access (NOMA) technique to improve the achievable sum rate of multiple-input multiple-output (MIMO) based multi-user visible light communication (VLC) systems. To ensure efficient and low- complexity power allocation in indoor MIMO-NOMA based VLC systems, a normalized gain difference power allocation (NGDPA) method is firstly proposed by exploiting users’ channel conditions. We investigate the performance of an indoor 2×2 MIMO-NOMA based multi-user VLC system through numerical simulations. The obtained results show that the achievable sum rate of the 2×2 MIMO-VLC system can be significantly improved by employing NOMA with the proposed NGDPA method. It is demonstrated that NOMA with NGDPA achieves a sum rate improvement of up to 29.1% compared to NOMA with the gain ratio power allocation (GRPA) method in the 2×2 MIMO-VLC system with three users.

Smart factory under Industry 4.0 and industrial Internet of Thighs (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, massive Machine Type Communication (mMTC) can effectively support a variety of IoT applications, where industrial IoT (IIoT) and IoT devices have different quality-of-service (QoS) requirements, ranging from high reliability and low-latency to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this paper presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS) based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.

In this article, we study the application of unmanned aerial vehicle (UAV) for data collection with wireless charging, which is crucial for providing seamless coverage and improving system performance in the next-generation wireless networks. To this end, we propose a reinforcement learning-based approach to plan the route of UAV to collect sensor data from sensor devices scattered in the physical environment. Specifically, the physical environment is divided into multiple grids, where one spot for UAV hovering as well as the wireless charging of UAV is located at the center of each grid. Each grid has a spot for the UAV to hover, and moreover, there is a wireless charger at the center of each grid, which can provide wireless charging to UAV when it is hovering in the grid. When the UAV lacks energy, it can be charged by the wireless charger at the spot. By taking into account the collected data amount as well as the energy consumption, we formulate the problem of data collection with UAV as a Markov decision problem, and exploit Q-learning to find the optimal policy. In particular, we design the reward function considering the energy efficiency of UAV flight and data collection, based on which Q-table is updated for guiding the route of UAV. Through extensive simulation results, we verify that our proposed reward function can achieve a better performance in terms of the average throughput, delay of data collection, as well as the energy efficiency of UAV, in comparison with the conventional capacity-based reward function.

In this paper, a low-complexity time-difference-of-arrival (TDOA)-based indoor visible light positioning (VLP) system using an enhanced practical localization scheme based on cross-correlation is proposed and experimentally demonstrated. The proposed TDOA scheme offers two advantages: 1) the use of virtual local oscillator (VLO) to replace the real local oscillator (RLO) for cross-correlation at the receiver side so as to reduce the hardware complexity; 2) the application of cubic spline interpolation on the correlation function to reduce the rigorous requirement on the sampling rate and to enhance the time-resolution of cross-correlation. In order to achieve high positioning accuracy with minimum implementation complexity, parameter optimization is first performed in terms of sampling rate, interpolation factor and data length for correlation. Using the obtained optimal parameters, we demonstrate a low-complexity indoor two-dimensional (2D) VLP system using the correlation-based TDOA scheme in a coverage area of 1.2$\times$1.2 m$^{2}$ with a height of 2 m. The experimental results validate the feasibility of the proposed TDOA scheme, and an average positioning accuracy of 9.2 cm is achieved with a sampling rate of 500 MSa/s, an interpolation factor of 100 and a data length of 250k samples.