A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

Joint Aerial-Terrestrial Resource Management in UAV-Aided Mobile Radio Networks

This article addresses the issue of joint aerial- terrestrial resource management in mobile radio networks supported by a UAV operating as network node and discusses the potential of true integration between the terrestrial and UAV components of the network. A simulation campaign shows that, by properly optimizing the system parameters related to the UAV flight, a single UAV can bring significant improvement in network throughput for a wide service area. The use of a joint radio resource management approach, where the UAV and terrestrial base stations operate in a coordinated manner, brings significant advantages with respect to different algorithms

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

Trajectories and resource management of flying base stations for C-V2X

In a vehicular scenario where the penetration of cars equipped with wireless communication devices is far from 100% and application requirements tend to be challenging for a cellular network not specifically planned for it, the use of unmanned aerial vehicles (UAVs), carrying mobile base stations, becomes an interesting option. In this article, we consider a cellular-vehicle-to-anything (C-V2X) application and we propose the integration of an aerial and a terrestrial component of the network, to fill the potential unavailability of short-range connections among vehicles and address unpredictable traffic distribution in space and time. In particular, we envision a UAV with C-V2X equipment providing service for the extended sensing application, and we propose a UAV trajectory design accounting for the radio resource (RR) assignment. The system is tested considering a realistic scenario by varying the RRs availability and the number of active vehicles. Simulations show the results in terms of gain in throughput and percentage of served users, with respect to the case in which the UAV is not present

Antennas and Propagation for UAV-Assisted Wireless Networks Towards Next Generation Mobile Systems

Unmanned Aerial Vehicles (UAV), also known as "drones", are attracting increasing attention as enablers for many technical applications and services, and this trend is likely to continue in the near future. UAVs are expected to be used extensively in civil and military applications where aerial surveillance and assistance in emergency situations are key factors. UAVs can be more useful and flexible in reaction to specific events, like natural disasters and terrorist attacks since they are faster to deploy, easier to reconfigure and assumed to have better communication means due to their improved position in the sky, improved visibility over ground, and reduced hindrance for propagation. In this regard, UAV enabled communications emerge as one of the most promising solutions for setting-up the next-generation mobile networks, with a special focus on the extension of coverage and capacity of mobile radio networks for 5G applications and beyond. However, air-to-ground (A2G) propagation conditions are likely to be different and more challenging than those experienced by traditional piloted aircraft. For this reason, knowledge of this specific propagation channel – together with the UAV antenna design and placement - is paramount for defining an efficient communication system and for evaluating its performance. This PhD thesis tackles this challenge, and it aims at further investigating the narrowband properties of the air-to-ground propagation channel by means of GPU accelerated ray launching simulations for 5G communications and beyond. As a conclusion, this PhD thesis might bring deep insights into the air-to-ground channel characteristics and UAV antenna design, which can be helpful for designing UAV communication networks and evaluating or optimising their performances in a fast and reliable manner, with no need for exhausting – multiple - in-field measurement campaigns

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

Integrated Sensing and Communication (ISAC), combined with data-driven approaches, has emerged as a highly significant field, garnering considerable attention from academia and industry. Its potential to enable wide-scale applications in the future sixth-generation (6G) networks has led to extensive recent research efforts. Machine learning (ML) techniques, including $K$-nearest neighbors (KNN), support vector machines (SVM), deep learning (DL) architectures, and reinforcement learning (RL) algorithms, have been deployed to address various design aspects of ISAC and its diverse applications. Therefore, this paper aims to explore integrating various ML techniques into ISAC systems, covering various applications. These applications span intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs) and autonomous cars, as well as radar applications, localization and tracking, millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming. The contributions of this paper lie in its comprehensive survey of ML-based works in the ISAC domain and its identification of challenges and future research directions. By synthesizing the existing knowledge and proposing new research avenues, this survey serves as a valuable resource for researchers, practitioners, and stakeholders involved in advancing the capabilities of ISAC systems in the context of 6G networks.Comment: ISAC-ML surve