Abstract
Next generation communication networks will be characterized by the coexistence of multiple technologies and user devices in an integrated fashion. The increasing number of devices owned by a single user will lead to a new communication paradigm: users owning multiple devices that form cooperative networks, and networks of different users that communicate with each other, e.g., acquiring Internet access through each other. In this communication scenario no user intervention should be required and technology should seamlessly adapt to the user's context, preferences, and needs. In this paper we address one of those scenarios, interworking between Personal Area Networks, using legacy technologies and the Ambient Network and Network Composition concepts, herein explained. We argue that new functionalities should be introduced to enable effortless use of legacy technologies in such dynamic and heterogeneous environments. © 2005 IEEE.

Abstract
Ubiquitous Internet access is becoming a major requirement for end-users due to the increasing number of services and applications supported over the Internet. Extending the coverage of current Wi-Fi infrastructures installed in companies, universities and cities, has been considered a solution to help in fulfilling this requirement, namely when it comes to wireless and nomadic Internet access. This paper describes and analyses a new and simple solution, called Wi-Fi network Infrastructure eXtension (WiFIX), aimed at extending current Wi-Fi infrastructures. WiFIX is based on standard IEEE 802.1D bridges and a single-message protocol that is able to self-organize the network, and it only requires software changes in IEEE 802.11 access points (APs); no changes to IEEE 802.11 stations are needed. Overhead analysis and experimental results show both the higher efficiency of the solution compared to the IEEE 802.11s draft standard and its good performance as far as data throughput, delay and packet loss are concerned. Copyright (C) 2010 John Wiley & Sons, Ltd.

Abstract
Unmanned Aerial Vehicles (UAVs) offer promising potential as communications node carriers, providing on-demand wireless connectivity to users. While existing literature presents various wireless channel models, it often overlooks the impact of UAV heading. This paper provides an experimental characterization of the Air-to-Ground (A2G) and Ground-to-Air (G2A) wireless channels in an open environment with no obstacles nor interference, considering the distance and the UAV heading. We analyze the received signal strength indicator and the TCP throughput between a ground user and a UAV, covering distances between 50 m and 500 m, and considering different UAV headings. Additionally, we characterize the antenna’s radiation pattern based on UAV headings. The paper provides valuable perspectives on the capabilities of UAVs in offering on-demand and dynamic wireless connectivity, as well as highlights the significance of considering UAV heading and antenna configurations in real-world scenarios.

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Abstract
Digital twins have been emerging as a hybrid approach that combines the benefits of simulators with the realism of experimental testbeds. The accurate and repeatable set-ups replicating the dynamic conditions of physical environments, enable digital twins of wireless networks to be used to evaluate the performance of next-generation networks. In this paper, we propose the Position-based Machine Learning Propagation Loss Model (P-MLPL), enabling the creation of fast and more precise digital twins of wireless networks in ns-3. Based on network traces collected in an experimental testbed, the P-MLPL model estimates the propagation loss suffered by packets exchanged between a transmitter and a receiver, considering the absolute node's positions and the traffic direction. The P-MLPL model is validated with a test suite. The results show that the P-MLPL model can predict the propagation loss with a median error of 2.5 dB, which corresponds to 0.5x the error of existing models in ns-3. Moreover, ns-3 simulations with the P-MLPL model estimated the throughput with an error up to 2.5 Mbit/s, when compared to the real values measured in the testbed.