68 research outputs found
A Survey on Cellular-connected UAVs: Design Challenges, Enabling 5G/B5G Innovations, and Experimental Advancements
As an emerging field of aerial robotics, Unmanned Aerial Vehicles (UAVs) have
gained significant research interest within the wireless networking research
community. As soon as national legislations allow UAVs to fly autonomously, we
will see swarms of UAV populating the sky of our smart cities to accomplish
different missions: parcel delivery, infrastructure monitoring, event filming,
surveillance, tracking, etc. The UAV ecosystem can benefit from existing 5G/B5G
cellular networks, which can be exploited in different ways to enhance UAV
communications. Because of the inherent characteristics of UAV pertaining to
flexible mobility in 3D space, autonomous operation and intelligent placement,
these smart devices cater to wide range of wireless applications and use cases.
This work aims at presenting an in-depth exploration of integration synergies
between 5G/B5G cellular systems and UAV technology, where the UAV is integrated
as a new aerial User Equipment (UE) to existing cellular networks. In this
integration, the UAVs perform the role of flying users within cellular
coverage, thus they are termed as cellular-connected UAVs (a.k.a. UAV-UE,
drone-UE, 5G-connected drone, or aerial user). The main focus of this work is
to present an extensive study of integration challenges along with key 5G/B5G
technological innovations and ongoing efforts in design prototyping and field
trials corroborating cellular-connected UAVs. This study highlights recent
progress updates with respect to 3GPP standardization and emphasizes
socio-economic concerns that must be accounted before successful adoption of
this promising technology. Various open problems paving the path to future
research opportunities are also discussed.Comment: 30 pages, 18 figures, 9 tables, 102 references, journal submissio
Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks
Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique
5G and beyond networks
This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications
Distributed algorithms for optimized resource management of LTE in unlicensed spectrum and UAV-enabled wireless networks
Next-generation wireless cellular networks are morphing into a massive Internet
of Things (IoT) environment that integrates a heterogeneous mix of wireless-enabled
devices such as unmanned aerial vehicles (UAVs) and connected vehicles.
This unprecedented transformation will not only drive an exponential growth in
wireless traffic, but it will also lead to the emergence of new wireless service
applications that substantially differ from conventional multimedia services. To
realize the fifth generation (5G) mobile networks vision, a new wireless radio
technology paradigm shift is required in order to meet the quality of service
requirements of these new emerging use cases. In this respect, one of the major
components of 5G is self-organized networks. In essence, future cellular networks
will have to rely on an autonomous and self-organized behavior in order to manage
the large scale of wireless-enabled devices. Such an autonomous capability can be
realized by integrating fundamental notions of artificial intelligence (AI) across
various network devices.
In this regard, the main objective of this thesis is to propose novel self-organizing
and AI-inspired algorithms for optimizing the available radio resources
in next-generation wireless cellular networks. First, heterogeneous networks that
encompass licensed and unlicensed spectrum are studied. In this context, a deep
reinforcement learning (RL) framework based on long short-term memory cells is
introduced. The proposed scheme aims at proactively allocating the licensed assisted
access LTE (LTE-LAA) radio resources over the unlicensed spectrum while
ensuring an efficient coexistence with WiFi. The proposed deep learning algorithm
is shown to reach a mixed-strategy Nash equilibrium, when it converges.
Simulation results using real data traces show that the proposed scheme can yield
up to 28% and 11% gains over a conventional reactive approach and a proportional
fair coexistence mechanism, respectively. In terms of priority fairness, results
show that an efficient utilization of the unlicensed spectrum is guaranteed when
both technologies, LTE-LAA and WiFi, are given equal weighted priorities for
transmission on the unlicensed spectrum. Furthermore, an optimization formulation
for LTE-LAA holistic traffic balancing across the licensed and the unlicensed
bands is proposed. A closed form solution for the aforementioned optimization
problem is derived. An attractive aspect of the derived solution is that it can be
applied online by each LTE-LAA small base station (SBS), adapting its transmission behavior in each of the bands, and without explicit communication with
WiFi nodes. Simulation results show that the proposed traffic balancing scheme
provides a better tradeoff between maximizing the total network throughput and
achieving fairness among all network
ows compared to alternative approaches
from the literature. Second, UAV-enabled wireless networks are investigated. In
particular, the problems of interference management for cellular-connected UAVs
and the use of UAVs for providing backhaul connectivity to SBSs are studied.
Speci cally, a deep RL framework based on echo state network cells is proposed
for optimizing the trajectories of multiple cellular-connected UAVs while minimizing
the interference level caused on the ground network. The proposed algorithm
is shown to reach a subgame perfect Nash equilibrium upon convergence. Moreover,
an upper and lower bound for the altitude of the UAVs is derived thus
reducing the computational complexity of the proposed algorithm. Simulation
results show that the proposed path planning scheme allows each UAV to achieve
a tradeoff between minimizing energy efficiency, wireless latency, and the interference
level caused on the ground network along its path. Moreover, in the context
of UAV-enabled wireless networks, a UAV-based on-demand aerial backhaul network
is proposed. For this framework, a network formation algorithm, which is
guaranteed to reach a pairwise stable network upon convergence, is presented.
Simulation results show that the proposed scheme achieves substantial performance
gains in terms of both rate and delay reaching, respectively, up to 3.8 and
4-fold increase compared to the formation of direct communication links with the
gateway node. Overall, the results of the different proposed schemes show that
these schemes yield significant improvements in the total network performance
as compared to current existing literature. In essence, the proposed algorithms
can also provide self-organizing solutions for several resource management problems
in the context of new emerging use cases in 5G networks, such as connected
autonomous vehicles and virtual reality headsets
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