WOS2: Aerial and V2X Networks

Wednesday, 17 June 2020, 12:15-14:30 CEST, Recommended re-viewing, https://www.youtube.com/playlist?list=PLjQu6nB1DfNB0YLEfDQXjLPTlsvsv0eN_

Wednesday, 17 June 2020, 12:15-16:00 CEST, Non-Live interaction (Chat),  link sent only to Registered people


Dynamic Standalone Drone-Mounted Small Cells

Igor Donevski and Jimmy J Nielsen (Aalborg University, Denmark)
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.


Actor-Critic Deep Reinforcement Learning for Energy Minimization in UAV-Aided Networks

Yaxiong Yuan, Lei Lei, Thang X. Vu and Symeon Chatzinotas (University of Luxembourg, Luxembourg); Björn Ottersten (University of Luxembourg, Luxembourg)
In this paper, we investigate a user-timeslot scheduling problem for downlink unmanned aerial vehicle (UAV)-aided networks, where the UAV serves as an aerial base station. We formulate an optimization problem by jointly determining user scheduling and hovering time to minimize UAV’s transmission and hovering energy. An offline algorithm is proposed to solve the problem based on the branch and bound method and the golden section search. However, executing the offline algorithm suffers from the exponential growth of computational time. Therefore, we apply a deep reinforcement learning (DRL) method to design an online algorithm with less computational time.To this end, we first reformulate the original user scheduling problem to a Markov decision process (MDP).Then, an actor-critic-based RL algorithm is developed to determine the scheduling policy under the guidance of two deep neural networks. Numerical results show the proposed online algorithm obtains a good tradeoff between performance gain and computational time.


5G-Sim-V2I/N: Towards a Simulation Framework for the Evaluation of 5G V2I/V2N Use Cases

Thomas Deinlein (University of Erlangen-Nürnberg, Germany); Reinhard German (University of Erlangen, Germany); Anatoli Djanatliev (University of Erlangen-Nuremberg, Germany)
In this paper we introduce the OMNeT++-framework 5G-Sim-V2I/N which enables to simulate 5G V2I (Vehicle to infrastructure) / V2N (Vehicle to network) use cases with applications comprising the whole 5G user plane. It enhances the well-known frameworks SimuLTE and Veins with several 5G specification features (e.g., ITU-Channel Models, numerology, calculation of the Transport Block Size, etc.) and comes by default with a motorway and an urban scenario modified for the use in dense road scenarios. As an application example we used the framework to simulate several hundred cars in both scenarios running different parallel applications in each car. By measuring several parameters on the application layer, which are relevant for evaluating the Quality-of-Service (QoS) of each application, we illustrate the abilities of the framework.


Data-Centric Node Selection for Machine-Type Communications with Lossy Links

Hung-Hsien Chen and Hung-Yun Hsieh (National Taiwan University, Taiwan)
While node selection has been popularly studied in the literature for wireless sensor networks, a majority of papers assume a simplistic wireless model without taking communication costs such as radio resource usage and link loss into consideration. In a lossy environment, since data sent back by the selected subset of sensors may suffer from random losses, it may become necessary to use more radio resource usage by either selecting more sensors than needed as backups or providing more transmission opportunities to the selected sensors. In this paper, we investigate how the limited radio resource can be effectively allocated to a selected subset of sensors using machine-type communications for minimizing the data reconstruction error in a data gathering application with lossy links. We first formulate a node selection problem and then investigate two algorithms as solutions. The first algorithm exploits meta-heuristic randomized search in the search space to find a near-optimal solution. The second one, on the other hand, incurs a much lower computation cost by greedily selecting most informative sensors one by one to represent the population. Through computer simulation, we show that providing more transmission opportunities to the selected subset of sensors can achieve a more desirable performance in terms of radio resource usage and energy conservation than selecting more sensors as backups for machine-type communications with lossy links.