Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs), 1 have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multiantenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks. 1 Without loss of generality, we use the name of RIS in the remainder of this paper.

https://hal.archives-ouvertes.fr/hal-03357962/document

Reconfigurable Intelligent Surfaces: Principles and Opportunities

Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multi-antenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for application to next-generation networks.

Providing connectivity to around half of the world population living in rural or underprivileged areas is a tremendous challenge, but, at the same time, a unique opportunity. Access to the Internet would provide the population living in these areas a possibility to progress on the educational, health, environment, and business levels. In this article, a survey of technologies for providing connectivity to rural areas, which can help address this challenge, is provided. Although access/fronthaul and backhaul techniques are discussed in this article, it is noted that the major limitation for providing connectivity to rural and underprivileged areas is the cost of backhaul deployment. In addition, energy requirements and cost-efficiency of the studied technologies are analyzed. In fact, the challenges faced for deploying an electricity network, as a prerequisite for deploying communication networks, are huge in these areas, and they are granted an important share of the discussions in this article. Furthermore, typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. The main initiatives by key international players aiming to provide rural connectivity are also described. Moreover, directions for the future evolution of rural connectivity are outlined in this article. Although there is no single solution that can solve all rural connectivity problems, building gradually on the current achievements in order to reach ubiquitous connectivity, while taking into account the particularities of each region and tailoring the solution accordingly, seems to be the most suitable path to follow.

/pdf/a-key-6g-challenge-and-opportunity-connecting-the-base-of-385d3hceqi.pdf

A Key 6G Challenge and Opportunity—Connecting the Base of the Pyramid: A Survey on Rural Connectivity

Next-generation of the cellular network will attempt to overcome the limitations of the current Fifth Generation (5G) networks and equip itself to address the challenges which become obvious in the future. Currently, academia and industry have focused their attention on the Sixth Generation (6G) network, which is anticipated to be the next big game-changer in the telecom industry. The outbreak of COVID’19 has made the whole world to opt for virtual meetings, live video interactions ranging from healthcare, business to education. However, we miss an immersive experience due to the lack of supporting technology. Experts have anticipated that starting from the post-pandemic age, the performance requirements of technology for virtual and real-time communication, the rise of several verticals such as industrial automation, robotics, and autonomous driving will increase tremendously, and will skyrocket during the next decade. In this manuscript, we study the latest perspectives and future megatrends that are most likely to drive 6G. Initially, we describe the instances that lead us to the vision of 6G. Later, we narrate some of the use cases and the KPIs essential to meet their performance requirement. Further, we highlight the key requirements of 6G based on contemporary research such as UN sustainability goals, business model, edge intelligence, digital divide, and the trends in machine learning for 6G.

/pdf/6g-ecosystem-current-status-and-future-perspective-qbffuyw5z7.pdf

6G Ecosystem: Current Status and Future Perspective

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

This paper proposes an energy management methodology for a Fusion Grid concept. The Fusion Grid concept integrates a Long-Term Evolution (LTE) base station and a power system based on photo-voltaic (PV) power generation and a battery energy storage (BES). The Fusion Grid concept can be scaled up to an off-grid system. The system studied in this paper provides connectivity in the form of an LTE mobile network and electricity access in the form of a charging station for mobile devices. The energy management and control methods are applied as part of the system. The methods are aimed at the effective use of the installed BES capacity and reducing the total life cycle costs (LCC) of the system. Furthermore, the implemented methods will prevent overdimensioning of the system, thereby having an effect on the system capital expenditure costs (CAPEX). The developed methods are evaluated in a simulation environment with quantification of their effects on the life span of the BES.

Energy Management Methodology for Fusion Grid

This paper introduces a general approach to design a tailored solution to detect rare events in different industrial applications based on Internet of Things (IoT) networks and machine learning algorithms. We propose a general framework based on three layers (physical, data and decision) that defines the possible designing options so that the rare events/anomalies can be detected ultra-reliably. This general framework is then applied in a well-known benchmark scenario, namely Tennessee Eastman Process. We then analyze this benchmark under three threads related to data processes: acquisition, fusion and analytics. Our numerical results indicate that: (i) event-driven data acquisition can significantly decrease the number of samples while filtering measurement noise, (ii) mutual information data fusion method can significantly decrease the variable spaces and (iii) quantitative association rule mining method for data analytics is effective for the rare event detection, identification and diagnosis. These results indicates the benefits of an integrated solution that jointly considers the different levels of data processing following the proposed general three layer framework, including details of the communication network and computing platform to be employed.

Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning

This article describes high level details of an experimental cognitive network that was implemented at CPqD campus (Telecommunication research center from Brazil) . A cognitive network makes possible maximize radio electric spectrum usage, specially channels that are used for telecommunication services that do not have permanent usage, however are licensed to specific customers, which provides certain priority. It was implemented a telecommunication system controlled by a module called cognitive controller, which defines the rules that govern wireless routers, these devices should change of frequency when it is detected the presence of the licensed user. In this way the cognitive controller adds independence to a typical system based in WiFi technology, re-using empty channels, focusing in maintain permanent connectivity between terminals. In this paper are described evaluation system scenarios, cognitive algorithms inside and outside of the interest band, hardware and software elements used to implement the system, and experimental results.

Red experimental cognitiva: Algoritmos y resultados

Wireless technology is one of the enabler for Internet of Things (IoT) with a tremendous expected growth in the next years. The variety of wireless technologies to support different requirements like coverage or network architecture transform the wireless industry in a very competitive segment. There are many solutions for short and middle range IoT communication however, there is not still a long range solution that is broader adopted by the industry, which is generally know as Low-Power Wide-Area (LPWAN) technology. There LPWAN has not been designed for mobile network but the 3rd Generation Partnership Project (3GPP), that is responsible for 3G and 4G standardization, created the new technology that is named Narrow-Band IoT (NB-IoT) which intends to be widely adopted by the Operators. This article presents a summary of the NB-IoT specifications and some comparisons with others LPWAN technologies.

Evolution of long term narrowband-IoT

The concept of Internet of Things (IoT) implies that objects are connected to share information with each other and/or with a platform based on standardized communication protocols such as MQTT and HTTP. Recently, their performance has received much attention due to more widespread utilization. But, the vital aspect of the adoption of these IoT protocols is not just performance per se but also their feature set. However, comparative analysis that takes into account both the features and performance aspects are rarely presented. This paper investigates a combination of a qualitative comparison model (based on communication, operational and security attributes) with quantitative metrics in terms of CPU/memory utilization and time consumption. Quantitative metrics are collected for two of the four IoT protocols. Our analysis shows that there is no onesize-fits-all solution in terms of application layer IoT protocols. Additionally we emphasize that, on a higher level, both qualitative and quantitative measures are necessary for IoT stakeholders to consider before selecting the most appropriate protocol for the specific application to be deployed. We illustrate the results using a smart grid example simulating a collection of 1000 smart meters connected to the grid.

Dick Carrillo

Papers

Energy Management Methodology for Fusion Grid

Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning

Red experimental cognitiva: Algoritmos y resultados

Evolution of long term narrowband-IoT

IoT Protocol Selection for Smart Grid Applications: Merging Qualitative and Quantitative Metrics