Technological Trends and Key Communication Enablers for eVTOLs

The world is looking for a new exciting form of transportation that will cut our travel times considerably. In 2021, the time has come for flying cars to become the new transportation system of this century. Electric vertical take-off and landing (eVTOL) vehicles, which are a type of flying cars, are predicted to be used for passenger and package transportation in dense cities. In order to fly safely and reliably, wireless communications for eVTOLs must be developed with stringent eVTOL communication requirements. Indeed, their communication needs to be ultra-reliable, secure with ultra-high data rate and low latency to fulfill various tasks such as autonomous driving, sharing a massive amount of data in a short amount of time, and high-level communication security. In this paper, we propose major key communication enablers for eVTOLs ranging from the architecture, air-interface, networking, frequencies, security, and computing. To show the relevance and the impact of one of the key enablers, we carried out comparative simulations to show the superiority compared to the current technology. We compared the usage of an air-based communication infrastructure with a tower mast in a realistic scenario involving eVTOLs, delivery drones, pedestrians, and vehicles.Comment: 8 pages, 10 figure

Resource allocation, user association and placement for uav-assisted communications

In the past few years, unmanned aerial vehicle (UAV)-assisted heterogeneous network has attracted significant attention due to its wide range of applications, such as disaster rescue and recovery, ground macro base station (MBS) traffic offloading, communications for temporary events, and data collection for further processing in Internet of Things (IoT). A UAV can act as a flying base station (BS) to quickly recover the communication coverage in the disaster area when the regular terrestrial infrastructure is malfunctioned. The UAV-assisted heterogeneous network can effectively provision line of sight (LoS) communication links and therefore can mitigate potential signal shadowing and blockage. The regulation relaxation and cost reduction of UAVs as well as communication equipment miniaturization make the practical deployment of highly mobile wireless relays more feasible than before. In fact, the 3GPP Rel-16 has included UAV-enabled wireless communications in the new radio standard, aiming to boost capacity and coverage of fifth generation (5G) wireless networks. However, the performance of UAV-assisted communications is greatly affected by the resource allocation scheme, user association policy and the UAV placement strategy. Also, the limited on-board energy and flight time of the UAV poses a great challenge on designing a robust and reliable UAV-enabled IoT network. To maximize the throughput in the UAV-assisted mobile access network, an optimization problem which determines the 3D UAV deployment and resource allocation in a given hotspot area under the constraints of user Quality of Service (QoS) requirements and total available resources is formulated. First, the primal problem is decomposed into two subproblems, i.e., the 3D UAV placement problem and the resource allocation problem. Second, a cyclic iterative algorithm which solves the two sub-problems separately and uses the output of one as the input of the other is proposed. An optimization problem that aims to minimize the average latency ratio of all users is formulated by determining the 3D location of the UAV, the user association and the bandwidth allocation policy between the MBS and the drone base station (DBS) with the constraint of each user’s QoS requirement and total available bandwidth. The formulated problem is a mixed integer non-convex optimization problem, a very challenging and difficult problem. To make formulated problem tractable, it is decomposed into two subproblems, i.e., the user association and bandwidth allocation problem and the 3D DBS placement problem. These two subproblems are alternatively optimized until no performance improvement can be further achieved. To address the challenge of limited on-board battery capacity and flight time, a tethered UAV (TUAV)-assisted heterogeneous network where the aerial UAV is connected with a ground charging station (GCS) through a tether is proposed. The objective of the formulated problem is to maximize the sum rate of all users by jointly optimizing the user association, resource allocation and placement of the GCSs and the aerial UAVs, constrained by each user’s QoS requirement and the total available resource. Since the primal problem is highly non-convex and non-linear and thus challenging to solve, it is decomposed into three subproblems, i.e., the TUAV placement problem, the resource allocation problem and the user association problem. Then, the three sub-problems are alternately and iteratively optimized by using the outputs of the first two as the input for the third. The future work comprises two parts. First, IoT devices usually are generally deployed at remote areas with limited battery capacities and computing power. Therefore, the generated data needs to be offloaded to a more powerful computing server for further processing. Unfortunately, the trajectory design in UAV data collection is generally NP-hard and difficult to obtain the optimal solution. Advances of machine learning (ML) provide a promising alternative approach to solve such problems that cannot be solved by traditional optimization methods. Hence, deep reinforcement learning (DRL) is proposed to be explored to obtain a near optimal solution. Second, the low earth orbit (LEO) satellite networks will revolutionize traditional communication networks with their promising benefits of service continuity, wide-area coverage, and availability for critical communications and emerging applications. However, the integration of LEO satellite networks and terrestrial networks will be another future research endeavor

Minimum Cost Design of Cellular Networks in Rural Areas with UAVs, Optical Rings, Solar Panels and Batteries

Bringing the cellular connectivity in rural zones is a big challenge, due to the large installation costs that are incurred when a legacy cellular network based on fixed Base Stations (BSs) is deployed. To tackle this aspect, we consider an alternative architecture composed of UAV-based BSs to provide cellular coverage, ground sites to connect the UAVs with the rest of the network, Solar Panels (SPs) and batteries to recharge the UAVs and to power the ground sites, and a ring of optical fiber links to connect the installed sites. We then target the minimization of the installation costs for the considered UAV-based cellular architecture, by taking into account the constraints of UAVs coverage, SPs energy consumption, levels of the batteries and the deployment of the optical ring. After providing the problem formulation, we derive an innovative methodology to ensure that a single ring of installed optical fibers is deployed. Moreover, we propose a new algorithm, called DIARIZE, to practically tackle the problem. Our results, obtained over a set of representative rural scenarios, show that DIARIZE performs very close to the optimal solution, and in general outperforms a reference design based on fixed BSs