Abstract
Despite the trend towards ubiquitous wireless connectivity, there are scenarios where the communications infrastructure is damaged and wireless coverage is insufficient or does not exist, such as in natural disasters and temporary crowded events. Flying networks, composed of Unmanned Aerial Vehicles (UAV), have emerged as a flexible and cost-effective solution to provide on-demand wireless connectivity in these scenarios. UAVs have the capability to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware. The state of the art in the field of flying networks is mainly focused on the optimal positioning of the flying nodes, while the wireless link parameters are configured with default values. On the other hand, current link adaptation algorithms are mainly targeting fixed or low mobility scenarios. We propose a novel rate adaptation approach for flying networks, named Trajectory Aware Rate Adaptation (TARA), which leverages the knowledge of flying nodes’ movement to predict future channel conditions and perform rate adaptation accordingly. Simulation results of 100 different trajectories show that our solution increases throughput by up to 53% and achieves an average improvement of 14%, when compared with conventional rate adaptation algorithms such as Minstrel-HT. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
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 due to implementional details may be detrimental to its performance, which in turn may decrease network performance. These delays can be avoided to a certain extent. However, this aspect has been overlooked in the state of the art when using simulated environments, since the computational delays are not considered. In this paper, we present an analysis of computational delays and their impact on the performance of RL-based RA algorithms, and propose a methodology to incorporate the experimental computational delays of the algorithms from running in a specific target hardware, in a simulation environment. Our simulation results considering the real computational delays showed that these delays do, in fact, degrade the algorithm’s execution and training capabilities which, in the end, has a negative impact on network performance. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract

Abstract
The increasing complexity of recent Wi-Fi amendments is making the use of traditional algorithms and heuristics unfeasible to address the Rate Adaptation (RA) problem. This is due to the large combination of configuration parameters along with the high variability of the wireless channel. Recently, several works have proposed the usage of Reinforcement Learning (RL) techniques to address the problem. However, the proposed solutions lack sufficient technical explanation. Also, the lack of standard frameworks enabling the reproducibility of results and the limited availability of source code, makes the fair comparison with state of the art approaches a challenge. This paper proposes a framework, named RateRL, that integrates state of the art libraries with the well-known Network Simulator 3 (ns-3) to enable the implementation and evaluation of RL-based RA algorithms. To the best of our knowledge, RateRL is the first tool available to assist researchers during the implementation, validation and evaluation phases of RL-based RA algorithms and enable the fair comparison between competing algorithms.