SUPPLY: Sustainable multi-UAV Performance-aware Placement Algorithm for Flying Networks

Unmanned Aerial Vehicles (UAVs) are used for a wide range of applications. Due to characteristics such as the ability to hover and carry cargo on-board, rotary-wing UAVs have been considered suitable platforms for carrying communications nodes, including Wi-Fi Access Points and cellular Base Stations. This gave rise to the concept of Flying Networks (FNs), now making part of the so-called Non-Terrestrial Networks (NTNs) defined in 3GPP. In scenarios where the deployment of terrestrial networks is not feasible, the use of FNs has emerged as a solution to provide wireless connectivity. However, the management of the communications resources in FNs imposes significant challenges, especially regarding the positioning of the UAVs so that the Quality of Service (QoS) offered to the Ground Users (GUs) and devices is maximized. Moreover, unlike terrestrial networks that are directly connected to the power grid, UAVs typically rely on on-board batteries that need to be recharged. In order to maximize the UAVs' flying time, the energy consumed by the UAVs needs to be minimized. When it comes to multi-UAV placement, most state-of-the-art solutions focus on maximizing the coverage area and assume that the UAVs keep hovering in a fixed position while serving GUs. Also, they do not address the energy-aware multi-UAV placement problem in networking scenarios where the GUs may have different QoS requirements and may not be uniformly distributed across the area of interest. In this work, we propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm. SUPPLY defines the energy and performance-aware positioning of multiple UAVs in an FN. To accomplish this, SUPPLY defines trajectories that minimize UAVs' energy consumption, while ensuring the targeted QoS levels. The obtained results show up to 25% energy consumption reduction with minimal impact on throughput and delay.

Vision-Radio Experimental Infrastructure Architecture Towards 6G

Telecommunications and computer vision have evolved separately so far. Yet, with the shift to sub-terahertz (sub-THz) and terahertz (THz) radio communications, there is an opportunity to explore computer vision technologies together with radio communications, considering the dependency of both technologies on Line of Sight. The combination of radio sensing and computer vision can address challenges such as obstructions and poor lighting. Also, machine learning algorithms, capable of processing multimodal data, play a crucial role in deriving insights from raw and low-level sensing data, offering a new level of abstraction that can enhance various applications and use cases such as beamforming and terminal handovers. This paper introduces CONVERGE, a pioneering vision-radio paradigm that bridges this gap by leveraging Integrated Sensing and Communication (ISAC) to facilitate a dual "View-to-Communicate, Communicate-to-View" approach. CONVERGE offers tools that merge wireless communications and computer vision, establishing a novel Research Infrastructure (RI) that will be open to the scientific community and capable of providing open datasets. This new infrastructure will support future research in 6G and beyond concerning multiple verticals, such as telecommunications, automotive, manufacturing, media, and health.

Joint Traffic and Obstacle-aware UAV Positioning Algorithm for Aerial Networks

Unmanned Aerial Vehicles (UAVs) are increasingly used as cost-effective and flexible Wi-Fi Access Points (APs) and cellular Base Stations (BSs) to enhance Quality of Service (QoS). In disaster management scenarios, UAV-based networks provide on-demand wireless connectivity when traditional infrastructures fail. In obstacle-rich environments like urban areas, reliable high-capacity communications links depend on Line-of-Sight (LoS) availability, especially at higher frequencies. Positioning UAVs to consider obstacles and enable LoS communications represents a promising solution that requires further exploration and development. The main contribution of this paper is the Traffic- and Obstacle-aware UAV Positioning Algorithm (TOPA). TOPA takes into account the users' traffic demand and the need for LoS between the UAV and the ground users in the presence of obstacles. The network performance achieved when using TOPA was evaluated through ns-3 simulations. The results show up to 100% improvement in the aggregate throughput without compromising fairness.

On the Analysis of Computational Delays in Reinforcement Learning-based Rate Adaptation Algorithms

Several research works have applied Reinforcement Learning (RL) algorithms to solve the Rate Adaptation (RA) problem in Wi-Fi networks. The dynamic nature of the radio link requires the algorithms to be responsive to changes in link quality. Delays in the execution of the algorithm may be detrimental to its performance, which in turn may decrease network performance. This aspect has been overlooked in the state of the art. In this paper, we present an analysis of common computational delays in RL-based RA algorithms, and propose a methodology that may be applied to reduce these computational delays and increase the efficiency of this type of algorithms. We apply the proposed methodology to an existing RL-based RA algorithm. The obtained experimental results indicate a reduction of one order of magnitude in the execution time of the algorithm, improving its responsiveness to link quality changes.

UAV-Assisted Wireless Communications: An Experimental Analysis of Air-to-Ground and Ground-to-Air Channels in Open Environments

Unmanned Aerial Vehicles (UAVs) offer promising potential as communications node carriers, providing on-demand wireless connectivity to users. While existing literature presents various channel models, they often overlook the impact of UAV heading. This paper experimentally characterizes Air-to-Ground (A2G) and Ground-to-Air (G2A) wireless channels in obstacle-free, interference-free open environments, accounting for distance and UAV heading. We analyze the Received Signal Strength Indicator (RSSI) and TCP throughput between ground users and UAVs, covering distances between 50 m and 500 m, and considering different UAV headings. Our study offers a more accurate channel model characterization compared to deterministic models such as Friis and two-ray. Additionally, we characterize the antenna's radiation pattern based on UAV headings.