Abstract

Abstract
Device-free Human Activity Recognition (HAR) presents a significant challenge, offering a privacy-preserving alternative to vision-based systems. This work proposes a novel methodology that leverages the rich motion dynamics captured in Doppler traces derived from Channel State Information (CSI). We introduce a hybrid deep learning architecture, InceptionBiLSTM, specifically engineered to process these traces. The Inception module excels at extracting salient, multi-scale local features from the Doppler data, while the Bidirectional Long Short-Term Memory (BiLSTM) network subsequently models the long-range temporal dependencies inherent in complex human activities. To further enhance classification performance, a Support Vector Machine (SVM) with a non-linear kernel is integrated as a post-processing stage. This step refines the decision boundaries learned by the deep neural network, significantly improving generalization. The proposed methodology achieves outstanding accuracy rates approaching 99 % in identifying distinct human movements. These results are validated through comprehensive performance metrics, including confusion matrices, confirming the robustness and high efficacy of this hybrid approach for CSI-based HAR.

Abstract
<div>Unmanned Aerial Vehicles (UAVs) have emerged as suitable platforms for transporting and positioning communications nodes on demand, including Wi-Fi Access Points and cellular Base Stations. This paved the way for the deployment of flying networks capable of temporarily providing wireless connectivity and reinforcing coverage and capacity of existing networks. Several solutions have been proposed for the positioning of UAVs acting as Flying Access Points (FAPs). Yet, the positioning of Flying Communications Relays (FCRs) in charge of forwarding the traffic to/from the Internet has not received equal attention. In addition, state of the art works are focused on optimizing both the flying network performance and the energy-efficiency from the communications point of view, leaving aside a relevant component: the energy spent for the UAV propulsion.</div><div>We propose the Energy and Performance Aware relay Positioning (EPAP) algorithm. EPAP defines target performance-aware Signal-to-Noise Ratio (SNR) values for the wireless links established between the FCR UAV and the FAPs and, based on that, computes the trajectory to be completed by the FCR UAV so that the energy spent for the UAV propulsion is minimized. EPAP was evaluated in terms of both the flying network performance and the FCR UAV endurance, considering multiple networking scenarios. Simulation results show gains up to 25% in the FCR UAV endurance, while not compromising the Quality of Service offered by the flying network.</div>

Abstract
<p>Flying networks, composed of Unmanned Aerial Vehicles (UAVs) acting as mobile Base Stations and Access Points, have emerged to provide on-demand wireless connectivity, especially due to their positioning capability. Still, existing solutions are focused on improving aggregate network performance using a best-effort approach. This may compromise the use of multiple services with different performance requirements. Network slicing has emerged in 5G networks to address the problem, allowing to meet different Quality of Service (QoS) levels on top of a shared physical network infrastructure. However, Mobile Network Operators typically use fixed Base Stations to satisfy the requirements of different network slices, which may not be feasible due to limited resources and the dynamism of some scenarios.</p> <p>We propose an algorithm for enabling the joint placement and allocation of communications resources in Slicing-aware Flying Access and Backhaul networks – SurFABle. SurFABle allows the computation of the amount of communications resources needed, namely the number of UAVs acting as Flying Access Points and Flying Gateways, and their placement. The performance evaluation carried out by means of ns-3 simulations and an experimental testbed shows that SurFABle makes it possible to meet heterogeneous QoS levels of multiple network slices using the minimum number of UAVs.</p>