This article reports the implementation and experimental performance investigation of a DSP-based flexible-waveform fiber-wireless (FiWi) system for 5G enhanced mobile broadband (eMBB) and new vertical applications. Our radio over fiber-based fronthaul solution uses a wavelength-division-multiplexing passive optical network (WDM-PON) infrastructure, from a commercial Internet service provider, to enable 5G operation over multiple frequency bands, including: a DSP and flexible waveform-based signal at 788 MHz, which can be set as generalized frequency division multiplexing (GFDM) or filtered orthogonal frequency division multiplexing (F-OFDM); 5G new radio (NR) signals at 3.5 or 26 GHz in accordance to 3GPP Release 15; an additional vector 26 GHz signal with bandwidth of up to 800 MHz. The DSP-based functionality provides digital pre-distortion (DPD), besides the real-time waveform generation. Experimental results demonstrate 4.41 Gbit/s total throughput in the air in accordance to the 3GPP requirements, as well as an innovative low-latency M2M application based on PROFINET standard.

We report the experimental implementation of optically-powered wireless sensor nodes based on the power-over-fiber (PoF) technology, aiming at Industrial Internet of Things (IIoT) applications. This technique employs optical fibers to transmit power and is proposed as a solution to address the hazardous industrial environment challenges, e.g., electromagnetic interference and extreme temperatures. The proposed approach enables two different IIoT scenarios, in which wireless transmitter (TX) and receiver (RX) nodes are powered by a PoF system, enabling local and remote temperature data monitoring, with the purpose of achieving an intelligent and reliable process management in industrial production lines. In addition, the system performance is investigated as a function of the delivered electrical power and power transmission efficiency (PTE), which is the primary performance metric of a PoF system. We report 1.4 W electrical power deliver with PTE = 24%. Furthermore, we carry out a voltage stability analysis, demonstrating that the PoF system is capable of delivering stable voltage to a wide range of applications. Finally, we present a comparison of temperature measurements between the proposed approach and a conventional industrial programmable logic controller (PLC). The obtained results demonstrate that PoF might be considered as a potential technology to power and enhance the energy efficiency of IIoT sensing systems.

This work reports the development of an efficient and precise indoor positioning system utilizing two-dimensional (2D) light detection and ranging (LiDAR) technology, aiming to address the challenging sensing and positioning requirements of the beyond fifth-generation (B5G) mobile networks. The core of this work is the implementation of a 2D-LiDAR system enhanced by an artificial neural network (ANN), chosen due to its robustness against electromagnetic interference and higher accuracy over traditional radiofrequency signal-based methods. The proposed system uses 2D-LiDAR sensors for data acquisition and digital filters for signal improvement. Moreover, a camera and an image-processing algorithm are used to automate the labeling of samples that will be used to train the ANN by means of indicating the regions where the pedestrians are positioned. This accurate positioning information is essential for the optimization of B5G network operation, including the control of antenna arrays and reconfigurable intelligent surfaces (RIS). The experimental validation demonstrates the efficiency of mapping pedestrian locations with a precision of up to 98.787%, accuracy of 95.25%, recall of 98.537%, and an F1 score of 98.571%. These results show that the proposed system has the potential to solve the problem of sensing and positioning in indoor environments with high reliability and accuracy.

This work reports the implementation of a simple and accurate indoor sensing and positioning system using a two-dimensional (2D) Light Detecting and Ranging (LiDAR) to fulfill the vigorous requirements from the Beyond Fifth Generation of mobile networks (B5G), including the Sixth Generation of Mobile Networks (6G). Particularly, we present the development of an Artificial Neural Network (ANN)-based 2D-LiDAR system, renowned for its electromagnetic interference resilience and superior accuracy compared to radiofrequency signal methodologies. The proposed framework integrates 2D-LiDARs and Artificial Intelligence (AI) functionalities to enhance the performance of pedestrian sensing and positioning systems in indoor environments. The proposed system architecture incorporates an array of up to four LiDAR sensors, taking advantage of combined data as the input for the exploited ANN aiming to obtain precise user positions within the indoor environment. Those positional data are pivotal for B5G systems, enabling optimized control and management of antenna arrays and Reconfigurable Intelligent Surfaces (RIS), significantly improving the user experience. The main contributions include the development and implementation of the proposed system; the demonstration of this innovative system applicability for 6G, tested in a 16 m² research laboratory space divided into up to 64 quadrants; and the experimental performance analysis under real indoor conditions, evaluated in terms of accuracy, precision, recall, and F1 score. Experimental results underscore and demonstrate the proposed system efficiency and applicability for accurately mapping pedestrian locations, achieving remarkable accuracy, precision, recall and F1 Score rates of up to 99%.