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Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are innovative technologies that aim to enhance the quality of communication in next-generation networks. These technologies are individually tailored to specific applications and have demonstrated impressive results. UAVs are known for their flexibility and ease of deployment, while RIS can adapt to changing environments by altering the traditional propagation and reflection schemes. Recent research has combined these two technologies for various applications, improving communication and data transmission in next-generation networks. This paper surveys recent advancements in using tethered and untethered UAVs and RIS in 5G/6G networks. The main objective of this survey is to inspire further solutions that support RIS-assisted UAVs and to provide insights not covered in published UAV–RIS surveys. The survey summarizes how UAVs and RIS can be used to address communication issues. Two case studies are presented to further illustrate this integration's potential benefits. The first case study demonstrates a UAV mounting an RIS to mitigate the impact of base station fronthaul failure. The results suggest that co-locating tethered UAVs with a selected number of base stations can proactively restore any failed fronthaul links. The second case study explores the 3D trajectory of the UAV and the phase shift design of the RIS based on the number of RIS elements and height placement of the UAV. The study considers an untethered UAV with a terrestrial RIS and shows that the propulsion energy of the UAV can be reduced by changing and adjusting the height of the UAV. [ABSTRACT FROM AUTHOR]

Welcome to the July issue of the Artificial Intelligence and Data Science for Communications Series of IEEE Communications Magazine. The Series continues its growth in popularity among academicians, researchers, and practitioners, as well as its increase in diversity in the topics it handles that relate to theory and applications of artificial intelligence (AI) and machine learning (ML) techniques to many communications systems and network domains. Due to the abundant availability of data, and the continued development and refinement of AI and ML techniques to process this data, AI and ML have established themselves as practical and viable approaches to provide efficient and practical solutions to many of the challenges in tethered and untethered communications environments at different scales and in different domains. [ABSTRACT FROM AUTHOR]

In this paper, we present a new technique that uses deterministic binary network coding in a distributed manner to enhance the resiliency of sensor-to-base information flow against packet loss. First, we show how to use network coding to tolerate a single packet loss by combining the data units from k sensor nodes to produce k+1 combinations such that any k of them are solvable. After that, we extend the solution to tolerate multiple losses. Moreover, we study the coding efficiency issue and introduce the idea of relative indexing to reduce the coding coefficients overhead. To tolerate node or link failures, we introduce a simple routing protocol that can find maximally disjoint paths from the k sensor nodes to the base station (BS). We study the relationship between the probability of successful recovery of all data units at the BS, and the number of sources protected together taking into consideration their hop distance from the BS. From this study, we can decide on the appropriate number of sources to be protected together, so that the probability of successful recovery is higher than a certain threshold. Finally, we show through a simulation study that our approach is highly scalable and performs better as the network size increases. [ABSTRACT FROM AUTHOR]

The articles in this special section are dedicated to the application of artificial intelligence AI), machine learning (ML), and data analytics to address different problems of communication systems, presenting new trends, approaches, methods, frameworks, systems for efficiently managing and optimizing networks related operations. Even though AI/ML is considered a key technology for next generation networks, still many research challenges need to be solved before it could reach its full potential. [ABSTRACT FROM AUTHOR]

Several of the new applications in high-performance networks are of the multicast traffic type. Since such networks employ an optical network infrastructure, and since most of these applications require subwavelength bandwidth, several streams are usually groomed on the same wavelength. This article presents an account of recent advances in the design of optical networks for multicast traffic grooming in WDM mesh networks. The article addresses network design and session provisioning under both static and dynamic multicast traffic. Under static traffic conditions, the objective is to accommodate a given set of multicast traffic demands, while minimizing the implementation cost. Optimal and heuristic solution techniques for mesh network topologies are presented. Under dynamic traffic conditions, techniques for dynamic routing and session provisioning of multicast sessions whose objective is to minimize session blocking probabilities are explained. The article also presents a number of open research issues. [ABSTRACT FROM AUTHOR]

Welcome to the February issue of the Data Science and Artificial Intelligence for Communications series of IEEE Communications Magazine. The series continues to grow in popularity among academicians, researchers and practitioners who apply artificial intelligence (AI) and machine learning (ML) techniques to solving problems across diverse domains of networking and communications. With the rapid developments in AI and ML theory and practice, and the abundant availability of data, this trend continues to establish itself as a viable approach to providing efficient and practical solutions for automation in tethered and untethered environments, in small or large systems, and in local or wide areas. [ABSTRACT FROM AUTHOR]