Ahead-Me Coverage (AMC):on maintaining enhanced mobile network coverage for UAVs

Abstract This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach

Seamless replacement of UAV-BSs providing connectivity to the IoT

Abstract This paper considers the scenario of Unmanned Aerial Vehicles (UAVs) acting as flying base stations (UAV-BSs) to provide network connectivity to ground Internet of Things (IoT) devices. More precisely, we investigate the issue where a UAV-BS needs to be replaced by a new one in a seamless way. First, we formulate the issue as an optimization problem aiming to maximize the minimum transmission rate of the served IoT devices during the UAV-BS replacement process. This is translated into jointly optimizing the trajectory of the source UAV-BS (the one to be replaced) and the target UAV-BS (the replacing one), while pushing the IoT devices to seamlessly transfer their connections to the target UAV-BS. We therefore consider a target replacement zone where the UAV-BS replacement can happen, along with IoT connections transfer. Furthermore, we propose a solution based on Deep Reinforcement Learning (DRL). More precisely, we introduce a Multi-Heterogeneous Agent-based approach (MHA-DRL), where two types of agents are considered, namely the UAV-BS agents and the IoT agents. Each agent implements a DQN (Deep Q-Learning) algorithm, where UAV-BS agents learn optimal policies to perform replacement while IoT agents learn optimal policies to transfer their connections to the target UAV-BS. The conducted performance evaluations show that the proposed approach can achieve near optimal optimization

Base station energy use in dense urban and suburban areas

Abstract Growing energy consumption is a global problem. The information and communications technology (ICT) industry is in a critical role as an enabler of energy savings in other sectors. However, the power consumption of the ICT sector also needs to be addressed, to contribute to the overall reduction of power consumption and carbon emissions. A new era has begun as the fifth generation (5G) mobile data connection rollouts are advancing globally and are expected to reach a 10% share of end-user devices and connections by 2023. The available references on energy consumption in global mobile networks are rather old and highly averaged — only estimates of energy consumption relative to data volumes are available. There is an information gap regarding the energy consumption of emerging 5G and advanced 4G technologies. Therefore, it has been difficult to understand the actual electricity consumption differences between generations and spatially aggregated electricity consumption once these generations are combined to offer capacity and coverage. This article fills this gap by providing a reference on the energy consumption of base transceiver stations for reported mobile data usage for different Radio Access Technologies; 3G, 4G and 5G respectively. To the best of our knowledge, there is no reference to scientific research on the comparison of energy intensity per square kilometer for 3G, 4G and 5G mobile radio technologies, using actual operator data. The objective of this research was to improve the understanding of the actual energy consumption of different Radio Access Technologies (RAT). The results also give insight to decision makers on when to modernize the operator radio access network. The article reports on the results of field measurements on data and visitor volumes and shares of different RATs. The research contains two statistical RAT combination cases, one representing the European average and the other Finnish mobile networks. The analyses were done for dense urban (DU) and suburban (SU) areas