1,251 research outputs found
D3S: A Framework for Enabling Unmanned Aerial Vehicles as a Service
In this paper, we consider the use of UAVs to provide wireless connectivity
services, for example after failures of wireless network components or to
simply provide additional bandwidth on demand, and introduce the concept of
UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework,
dubbed D3S, which consists of four phases: demand, decision, deployment, and
service. The main objective of this framework is to develop efficient and
realistic solutions to implement these four phases. The technical problems
include determining the type and number of UAVs to be deployed, and also their
final locations (e.g., hovering or on-ground), which is important for serving
certain applications. These questions will be part of the decision phase. They
also include trajectory planning of UAVs when they have to travel between
charging stations and deployment locations and may have to do this several
times. These questions will be part of the deployment phase. The service phase
includes the implementation of the backbone communication and data routing
between UAVs and between UAVs and ground control stations
Machine Learning for Predictive Deployment of UAVs with Multiple Access
In this paper, a machine learning based deployment framework of unmanned
aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed
as flying base stations (BS) to offload heavy traffic from ground BSs. Due to
time-varying traffic distribution, a long short-term memory (LSTM) based
prediction algorithm is introduced to predict the future cellular traffic. To
predict the user service distribution, a KEG algorithm, which is a joint
K-means and expectation maximization (EM) algorithm based on Gaussian mixture
model (GMM), is proposed for determining the service area of each UAV. Based on
the predicted traffic, the optimal UAV positions are derived and three
multi-access techniques are compared so as to minimize the total transmit
power. Simulation results show that the proposed method can reduce up to 24\%
of the total power consumption compared to the conventional method without
traffic prediction. Besides, rate splitting multiple access (RSMA) has the
lower required transmit power compared to frequency domain multiple access
(FDMA) and time domain multiple access (TDMA)
Deep Learning for Optimal Deployment of UAVs with Visible Light Communications
In this paper, the problem of dynamical deployment of unmanned aerial
vehicles (UAVs) equipped with visible light communication (VLC) capabilities
for optimizing the energy efficiency of UAV-enabled networks is studied. In the
studied model, the UAVs can simultaneously provide communications and
illumination to service ground users. Since ambient illumination increases the
interference over VLC links while reducing the illumination threshold of the
UAVs, it is necessary to consider the illumination distribution of the target
area for UAV deployment optimization. This problem is formulated as an
optimization problem which jointly optimizes UAV deployment, user association,
and power efficiency while meeting the illumination and communication
requirements of users. To solve this problem, an algorithm that combines the
machine learning framework of gated recurrent units (GRUs) with convolutional
neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the
long-term historical illumination distribution and predict the future
illumination distribution. Given the prediction of illumination distribution,
the original nonconvex optimization problem can be divided into two
sub-problems and is then solved using a low-complexity, iterative algorithm.
Then, the proposed algorithm enables UAVs to determine the their deployment and
user association to minimize the total transmit power. Simulation results using
real data from the Earth observations group (EOG) at NOAA/NCEI show that the
proposed approach can achieve up to 68.9% reduction in total transmit power
compared to a conventional optimal UAV deployment that does not consider the
illumination distribution and user association.Comment: This paper has been accepted by IEEE Transactions on Wireless
Communications. arXiv admin note: text overlap with arXiv:1909.0755
Mobile Edge Computing
This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists
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