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.

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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.

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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

The LTE Base Station (BS) testing process requires a large amount of investment, time and skilled resources. One important feature of LTE BS physical layer is the timing advance (TA) procedure, which is responsible for aligning the signal received from multiple User Equipment (UEs) at different cell distances. The BS informs the UE of the correct time to send the uplink frame in order to synchronize all UE frames and mitigate the interference between the terminals. Some vendors provide test platforms to emulate multiple UEs combined in a single box test system. These platforms are very sophisticated and allow the emulation of more than 500 UEs connected simultaneously, with different channels and at different distance conditions, handover, etc. Related costs can easily reach some hundreds of thousands of dollars, which are usually outside the project's budget. Besides, some features, including timing advance procedures cannot be obtained separately. Therefore, in order to test the LTE timing advance procedure with an affordable investment of less than five thousand dollars, a creative test environment was built with fiber optics and electrical/optical transceivers to emulate geographic distances inserting a delay on the RF uplink signal. This work describes the low-cost test environment and presents some lab results compared with field tests.

A low-cost test platform to estimate the LTE timing advance procedure

A cognitive radio can be defined by the capability of being aware of its environment and the internal state and, based on the knowledge of these elements and any stored pre-defined objectives, can dynamically adapt, make, and implement decisions about its behavior. Among the applications of cognitive radio, the most widely explored regards the improvement of spectrum bands usage, also known as white spaces. This paper presents a review of cognitive radio framework that facilitates the understanding and implementation of spectrum management functions of a typical-cognitive radio network project.

Cognitive Radio Networks

The CPqD Cognitive Mesh Network (CCMN) has been developed with traditional cognition elements, such as energy detection based sensing and with the ability to switch the data channel to take advantage of any available channels on a given radio-electric spectrum band. Many of the solutions that utilize cognitive mesh networks are based on the fact that it uses a common control channel (CCC) to manage the system. Therefore the Multi-Channel - One Interface Manager (MC-OIM) alg orithm was developed to maximize channel usage with a system that does not depend of any CCC . The MC-OIM algorithm was evaluated using network simulator ns-3 and simulation results were validated with testes that were done in a real scenario.

Cognitive wireless mesh network without common control channel evaluated in NS-3

This paper investigates the achievable sum rate and the outage capacity of generalized frequency division multiplexing systems (GFDMs) with minimum mean-square error (MMSE) receivers over frequency-selective Rayleigh fading channels. To this end, a Gamma-based approximation approach for the probability density function of the signal-to-interference-plus-noise ratio is presented, based on which accurate analytical formulations for the achievable sum rate and outage capacity are proposed. The accuracy of our analysis is corroborated through Monte Carlo simulation assuming different GFDM parameters. Illustrative numerical results are depicted in order to reveal the impact of the key system parameters, such as the number of subcarriers, number of subsymbols, and roll-off factors, on the overall system performance.

Achievable Sum Rate and Outage Capacity of GFDM Systems with MMSE Receivers

Despite the immense progress in the recent years, efficient solutions for monitoring remote areas are still missing today. This is especially notable in the context of versatile maritime and offshore use cases, owing to a broader span of operating regions and a lack of radio network infrastructures. In this article, we address the noted challenge by delivering a conceptual solution based on the convergence of three emerging technologies -- unmanned aerial vehicles (UAVs), battery-less sensors, and wireless powered communication networks (WPCNs). Our contribution offers a systematic description of the ecosystem related to the proposed solution by identifying its key actors and design dimensions together with the relevant resources and performance metrics. A system-level modeling-based evaluation of an illustrative scenario delivers deeper insights into the considered operation and the associated trade-offs. Further, unresolved challenges and perspective directions are underpinned for a subsequent study.

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Dick Carrillo

Papers

A low-cost test platform to estimate the LTE timing advance procedure

Cognitive Radio Networks

Cognitive wireless mesh network without common control channel evaluated in NS-3

Achievable Sum Rate and Outage Capacity of GFDM Systems with MMSE Receivers

Understanding UAV-Based WPCN-Aided Capabilities for Offshore Monitoring Applications.