10 research outputs found
Radio Resource Management for Unmanned Aerial Vehicle Assisted Wireless Communications and Networking
In recent years, employing unmanned aerial vehicles (UAVs) as aerial communication platforms or users is envisioned as a promising solution to enhance the performance of the existing wireless communication systems. However, applying UAVs for information technology applications also introduces many new challenges.
This thesis focuses on the UAV-assisted wireless communication and networking, and aims to address the challenges through exploiting and designing efficient radio resource management methods. Specifically, four research topics are studied in this thesis. Firstly, to address the constraint of network heterogeneity and leverage the benefits of diversity of UAVs, a hierarchical air-ground heterogeneous network architecture enabled by software defined networking is proposed, which integrates both high and low altitude platforms into conventional terrestrial networks to provide additional capacity enhancement and expand the coverage of current network systems. Secondly, to address the constraint of link disconnection and guarantee the reliable communications among UAVs as aerial user equipment to perform sensing tasks, a robust resource allocation scheme is designed while taking into account the dynamic features and different requirements for different UAV transmission connections. Thirdly, to address the constraint of privacy and security threat and motivate the spectrum sharing between cellular and UAV operators, a blockchain-based secure spectrum trading framework is constructed where mobile network operators and UAV operators can share spectrum in a distributed and trusted environment based on blockchain technology to protect users' privacy and data security. Fourthly, to address the constraint of low endurance of UAV and prolong its flight time as an aerial base station for delivering communication coverage in a disaster area, an energy efficiency maximization problem jointly optimizing user association, UAV's transmission power and trajectory is studied in which laser charging is exploited to supply sustainable energy to enable the UAV to operate in the sky for a long time
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
1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface
A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium