Energy-Efficient UAV Communications with Interference Management: Deep Learning Framework

| openaire: EC/H2020/815191/EU//PriMO-5GIn this paper, an interference-aware energy- efficient scheme for a network of coexisting aerial-terrestrial cellular users is proposed. In particular, each aerial user aims at achieving a trade-off between maximizing energy efficiency and spectral efficiency while minimizing the incurred interference on the terrestrial users along its path. To provide a solution, we first formulate the energy efficiency problem for UAVs as an optimization problem by considering different key performance indicators (KPIs) for the network, coexisting terrestrial users, and UAVs as aerial users. Then, leveraging tools from deep learning, we transform this problem into a deep queue learning problem and present a learning-powered solution that incorporates the KPIs of interest in the design of the reward function to solve energy efficiency maximization for aerial users while minimizing interference to terrestrial users. A broad set of simulations have been conducted in order to investigate how the altitude of UAVs, and the tolerable level of interference, shape the optimal energy-efficient policy in the network. Simulation results show that the proposed scheme achieves better energy and spectral efficiency for UAV and less interference to terrestrial users incurred from aerial users. The obtained results further provide insights on the benefits of leveraging intelligent energy-efficient scheme. For example, a significant increase in the energy efficiency of aerial users with respect to increases in their spectral efficiency, while a considerable decrease in incurred interference to the terrestrial users is achieved in comparison to the non-learning scheme.Peer reviewe

On the system-level performance evaluation of bluetooth 5 in IoT

The Internet of Things (IoT) has recently revolutionized the concept of connectivity from humans to surrounding objects through the Internet infrastructure. To Enable the wide range of IoT use cases, several communication technologies are introduced. Among the others, short range radio technology is an essential part of IoT for enabling the local area networks. Bluetooth Low Energy (BLE) version 5 is recently developed by Bluetooth Special Interest Group (SIG) which claims to be better suit for IoT use cases. However, the complexity of BLE 5 protocol and the lack of system-level simulator hinder the detailed analytical study of this new technology. To this end, we develop comprehensive system-level tool for simulating BLE 5. Some of the most important features of BLE 5 are developed and results are investigated in this paper. We investigate the BLE 5 with new physical (PHY) layer from networking perspective by analyzing end-to-end delay, battery life time, packet error rate and throughput in open office environment. To this end, we investigate the scalability of the network for different PHYs. The results show that, in this case study, the coded PHYs have weaker performance when network becomes congested.Peer reviewe

Drone Detection and Classification Using Cellular Network: A Machine Learning Approach

| openaire: EC/H2020/815191/EU//PriMO-5GThe main target of this paper is to propose a preferred set of features from a cellular network for using as predictors to do the classification between the flying drone User Equipments (UEs) and regular UEs for different Machine Learning (ML) models. Furthermore, the target is to study four different machine learning models i.e. Decision Tree (DT), Logistic Regression (LR). Discriminant Analysis (DA) and K- Nearest Neighbour (KNN) in this paper, and evaluate/compare their performance in terms of identifying the flying drone UE using three performance metrics i.e. True Positive Rate (TPR), False Positive Rate (FPR) and area under Receiver Operating Characteristic (ROC) curve. The simulations are performed using an agreed 3GPP scenario, and a MATLAB machine learning tool box. All considered ML models provide high drone detection probability for drones flying at 60 m and above height. However, the true drone detection probability degrades for drones at lower altitude. Whereas, the fine DT method and the coarse KNN model performs relatively better compared with LR and DA at low altitude, and therefore can be considered as a preferable choice for a drone classification problem.Peer reviewe

Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

| openaire: EC/H2020/815191/EU//PriMO-5GCellular connectivity for drones comes with a wide set of challenges as well as opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution tosolve HRRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions for different altitudes/speeds of drones have been presented, which promote a revision of the legacy handover schemes and boundaries of cells in the sky.Peer reviewe