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Extant indoor positioning techniques based on received signal strength (RSS) fingerprinting have achieved accurate positioning results. They leverage existing open-source datasets for training and comparing their model performance. However, the models trained on one building cannot be used for another since these models learn the relationship of a specific set of RSSs to the building and floor locations necessitating the expensive, time-consuming process of fingerprinting. Even when we consider the individual datasets producing these excellent results, a lot of painstaking optimization is required which precludes a lot of people trying to implement indoor positioning quickly. Most of the input RSS vector is empty with redundant information and the static class labels used for buildings and floors make the models unusable on other buildings. We propose a machine learning-based framework that uses RSS values from the strongest access point (AP) signals and normalized output labels to combat this issue. The framework was used on the open-source UJI dataset using less than 5% of the 520 APs to achieve 94.15% and 8.45 m floor prediction accuracy and mean positioning error respectively without any optimization. This technique was reused on ten other public datasets and achieved an average floor estimation accuracy of 91.93% when trained with new data and 88.68% without any new data compared to 87.1% of the closest competitor.

Visible light communication (VLC) is an emerging wireless technology to support high transmission rate for indoor devices by using existing lighting infrastructure, and VLC-based indoor localization is capable of providing high-accuracy localization. However, current VLC-based localization systems suffer from several key challenges such as sensitivity to random tilting of the receiver, which limits its full potential in real-world applications. In this paper, we design an integrated visible light communication and localization (VLCL) system to simultaneously support accurate real-time localization and communication services for indoor devices. To achieve this, an advanced differential phase difference of arrival (A-DPDOA) localization design is developed to simplify hardware and improve tracking robustness. In addition, a joint adaptive modulation, subcarrier and power allocation scheme is also proposed, which aims to improve the communication data rate and localization accuracy. Extensive experiments are performed to demonstrate that the proposed integrated VLCL system achieves higher localization accuracy and transmission data rate, compared to existing systems and schemes. Experiments also illustrate that the localization algorithm is more robust against the random tilting of the receiver under device movement in two-dimensional and three-dimensional scenarios.

Multiple-input multiple-output (MIMO) is a promising technology to efficiently improve the achievable rate of white light-emitting diodes (LEDs) enabled visible light communication (VLC) systems. Nevertheless, for conventional MIMO techniques such as spatial diversity (SD) and spatial multiplexing (SMP), the working mode of each LED transmitter is independent of users’ spatial positions. In this article, we propose and investigate three user-centric MIMO techniques, including SD/SMP switching, adaptive SMP (aSMP), and SD-aided aSMP (SD-aSMP), for achievable rate improvement of indoor MIMO-VLC systems, by exploiting users’ spatial positions as a new degree of diversity. The analytical and simulation results show that the achievable rate of a 4 × 4 MIMO-VLC system in a typical indoor environment can be significantly improved by applying the user-centric MIMO techniques compared with the conventional ones. More specifically, SD/SMP switching can enjoy both high diversity gain of SD and high multiplexing gain of SMP at different signal-to-noise ratio (SNR) regions, while aSMP mitigates the adverse effect of noise amplification in conventional SMP and SD-aSMP can benefit from additional diversity gain. Furthermore, we also show that the proposed user-centric MIMO techniques outperform conventional MIMO techniques under the condition of only slightly increased computational complexity with limited feedback information.

In this letter, an intelligent resource allocation framework based on model-free reinforcement learning (RL) is first presented for multi-user integrated visible light communication and positioning (VLCP) systems, in order to maximize the sum rate of users while guaranteeing the users’ minimum data rates and positioning accuracy constraints. The learning framework can learn the optimal policy under unknown environment’s dynamics and the continuous-valued space, and a reward function is proposed to take into account the strict communication and positioning constraints. Moreover, a modified experience replay actor–critic (MERAC) RL approach is proposed to improve the learning efficiency and convergence speed, which efficiently collects the reliable experience and utilizes the most useful knowledge from the memory. Numerical results show that the MERAC approach can effectively learn to satisfy the strict constraints and achieve the fast convergence speed.

A novel structure combining defected ground plane structures and complementary split ring resonators (CSRRs) is presented in this paper. It is shown that the DGS-CSRR low pass filters suppress the spurious response in the stopband and exhibit a much wider stopband compared to conventional DGS filters. The DGS-CSRR low pass filters are designed and fabricated. Good agreement between the simulated and measured data has been observed. The 3 to 10 GHz suppression can be exploited in suppressing the interference from UWB bands in the coexistence scenario of mobile, WLAN and UWB systems.