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Although the fifth-generation (5G) is not yet officially launched, researchers worldwide have turned to the sixth-generation (6G) communications system. The 3G has opened the gap to fourth-generation (4G). It will be the same for 5G, which will facilitate the path to 6G. The technology 5G provides a high-level infrastructure enabling various technologies such as autonomous cars, artificial intelligence, drone networking, mobile broadband communication, and, most importantly, the Internet of Things (IoT) and the concept of smart cities. We are, therefore, in the middle of the fourth industrial revolution (Industry 4.0). However, as new technologies gain traction, networks become increasingly complex and difficult to pin down to keep networks operating at the level prescribed by evolving services. The ultimate goal of 6G is to move from the concept of the Internet of intelligent things to the new idea of the intelligent Internet of intelligent things. This article shows the features and tools of 6G technology that will help meet these traffic needs. Besides, we highlight the main feature of the 6G, in terms of architecture and services, scheduled as recommended by the International Telecommunications Union (ITU) in its current technical specifications and discussions on the latest research in this area.

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Mobile Backhauling provides an interface between radio controller and base stations, mostly realized with a physical medium such as optical fibers or microwave radio links. With the huge mobile traffic due to an increase in mobile subscribers as well as deployment of 4G and 5G cellular network technologies, better solutions for capacity and coverage should be provided in order to enhance spectral efficiency. For 4G cellular networks, mobile backhaul networks deal with capacity, availability, deployment cost, and long-distance reaches. In addition, mobile backhaul networks based on the 5G network incurs additional challenges that include 1 ms or less ultralow latency time requirements and ultra-dense nature of the network capabilities. Therefore, for 5G technologies, latency delay, QoS, packet efficiency, noise suppression, and mitigation techniques, efficient modulation schemes, and packet network timing synchronization are some aspects that are to be dealt with while designing efficient backhaul approaches (wired/wireless). Current backhaul systems typically use cost-effective solutions (eg,-Wi-Fi and WiMAX)-based packet-switched technologies, especially Ethernet/Internet technologies and high-speed optical fiber links.

Accurate channel models are extremely important for the design of communications systems. Knowledge of the features of the channel provides communications system designers with the ability to predict the performance of the system for specific modulations, channel coding, and signal processing. This paper presents a statistical characterization of an Ultra-Wideband (UWB) propagation channel in an underground mine. Measurements were carried out in the 2-5 GHz frequency band. Various communication links were considered including both line-of-sight (LOS) and non-LOS (NLOS) scenarios. The measurement procedure allows us to characterize both the large-scale and the small-scale statistics of the channel. The aim here is to study in more details the statistical characteristics of the UWB propagation channel in an underground mine and to provide insight for future statistical channel modeling works. Channel characteristics examined include the distance and frequency dependency of path loss, shadowing fading statistics, and multipath temporal-domain parameter statistics such as the mean excess delay and the RMS delay spread. This work has been carried out by the underground communications research laboratory LRCS (The LRCS laboratory aims to develop research programs related to wireless telecommunications in underground mines. Research is conducted at its own facility as well as the CANMET experimental mine in Val-d'Or, Quebec, Canada), and the experimental mine CANMET (Canadian Center for Minerals and Energy Technology) in Val-d'or, Canada.

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented.

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