Dynamic Standalone Drone-Mounted Small Cells

This paper investigates the feasibility of Dynamic Horizontal Opportunistic Positioning (D-HOP) use in Drone Small Cells (DSCs), with a central analysis on the impact of antenna equipment efficiency onto the optimal DSC altitude that has been chosen in favor of maximizing coverage. We extend the common urban propagation model of an isotropic antenna to account for a directional antenna, making it dependent on the antenna's ability to fit the ideal propagation pattern. This leads us to define a closed-form expression for calculating the Rate improvement of D-HOP implementations that maintain constant coverage through antenna tilting. Assuming full knowledge of the uniformly distributed active users' locations, three D-HOP techniques were tested: in the center of the Smallest Bounding Circle (SBC); the point of Maximum Aggregated Rate (MAR); and the Center-Most Point (CMP) out of the two aforementioned. Through analytic study and simulation we infer that DSC D-HOP implementations are feasible when using electrically small and tiltable antennas. Nonetheless, it is possible to achieve average per user average rate increases of up to 20-35% in low user density scenarios, or 3-5% in user-dense scenarios, even when using efficient antennas in a DSC that has been designed for standalone coverage.Comment: To be published in proceedings of EuCNC'2

Enabling On-Demand Cyber-Physical Control Applications with UAV Access Points

Achieving cyber-physical control over a wireless channel requires satisfying both the timeliness of a single packet and preserving the latency reliability across several consecutive packets. To satisfy those requirements as an ubiquitous service requires big infrastructural developments, or flexible on-demand equipment such as UAVs. To avoid the upfront cost in terms of finance and energy, this paper analyzes the capability of UAV access points (UAVAPs) to satisfy the requirements for cyber-physical traffic. To investigate this, we perform a Gilbert-Eliott burst-error analysis that is analytically derived as a combination of two separate latency measurement campaigns and provide an upper-bound analysis of the UAVAP system. The analysis is centered around a UAVAP that uses its LTE connection to reach the backhaul, while providing service to ground nodes (GNs) with a Wi-Fi access point (AP). Thus, we combine both measurement campaigns to analyze the plausibility of the described setup in casual, crowded or mixed network settings.Comment: To be published in proceedings of VTC-fall 202

Analysis of LoRaWAN Uplink with Multiple Demodulating Paths and Capture Effect

Low power wide area networks (LPWANs), such as the ones based on the LoRaWAN protocol, are seen as enablers of large number of IoT applications and services. In this work, we assess the scalability of LoRaWAN by analyzing the frame success probability (FSP) of a LoRa frame while taking into account the capture effect and the number of parallel demodulation paths of the receiving gateway. We have based our model on the commonly used {SX1301 gateway chipset}, which is capable of demodulating {up to} eight frames simultaneously; however, the results of the model can be generalized to architectures with arbitrary number of demodulation paths. We have also introduced and investigated {three} policies for Spreading Factor (SF) allocation. Each policy is evaluated in terms of coverage {probability}, {FSP}, and {throughput}. The overall conclusion is that the presence of multiple demodulation paths introduces a significant change in the analysis and performance of the LoRa random access schemes

On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator

In this paper we envision a federated learning (FL) scenario in service of amending the performance of autonomous road vehicles, through a drone traffic monitor (DTM), that also acts as an orchestrator. Expecting non-IID data distribution, we focus on the issue of accelerating the learning of a particular class of critical object (CO), that may harm the nominal operation of an autonomous vehicle. This can be done through proper allocation of the wireless resources for addressing learner and data heterogeneity. Thus, we propose a reactive method for the allocation of wireless resources, that happens dynamically each FL round, and is based on each learner’s contribution to the general model. In addition to this, we explore the use of static methods that remain constant across all rounds. Since we expect partial work from each learner, we use the FedProx FL algorithm, in the task of computer vision. For testing, we construct a non-IID data distribution of the MNIST and FMNIST datasets among four types of learners, in scenarios that represent the quickly changing environment. The results show that proactive measures are effective and versatile at improving system accuracy, and quickly learning the CO class when underrepresented in the network. Furthermore, the experiments show a tradeoff between FedProx intensity and resource allocation efforts. Nonetheless, a well adjusted FedProx local optimizer allows for an even better overall accuracy, particularly when using deeper neural network (NN) implementations