Abstract
The increasing complexity of recent Wi-Fi amendments is making optimal Rate Adaptation (RA) a challenge. The use of classic algorithms or heuristic models to address RA is becoming unfeasible due to the large combination of configuration parameters along with the variability of the wireless channel. We propose a simple Deep Reinforcement Learning approach for the automatic RA in Wi-Fi networks, named Data-driven Algorithm for Rate Adaptation (DARA). DARA is standard-compliant. It dynamically adjusts the Wi-Fi Modulation and Coding Scheme (MCS) solely based on the observation of the Signal-to-Noise Ratio (SNR) of the received frames at the transmitter. Our simulation results show that DARA achieves higher throughput when compared with Minstrel High Throughput (HT)

Abstract

Abstract

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
Currently, the bandwidth utilization of residential broadband links is highly inefficient. Providing the means for sharing these links will increase the total bandwidth and reduce the fixed costs per client thus promoting the use of Internet services. This paper proposes a low cost communal residential gateway for sharing and remotely managing broadband access links. We first identify and characterize existing problems on residential asymmetric link sharing scenarios and then propose and evaluate the CommGate prototype that overcomes these problems. This solution contributes to an efficient broadband link sharing, a cost breakdown and an increasing Internet access dissemination, particularly in developing countries.

Abstract
Applications and products currently available for the broadcasting market are vertically integrated or proprietary They are based on components requiring specific and costly development to interoperate and typically rely on a single manufacturer or system integrator Hence, they are not fully compliant with broadcasters' requirements. Architectural Solutions for Services Enhancing digital Television (ASSET) is a European-funded project. Its main goal is to overcome the limitations of custom-specific implementations in a digital system for TV content creation. These limitations are generally due to the misfit of interfaces between software layers, proprietary application program interfaces (APIs) of equipment from different vendors, and the lack of a generalized middleware for multimedia content management with openly defined interfaces. Besides presenting the ASSET-proposed architecture and concepts, this paper describes the prototype under development to test and demonstrate the project proposals.