14 research outputs found
Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th
Generation (6G) networks, providing ubiquitous, continuous, and scalable
services. Satellites emerge as the primary enabler for NTN, leveraging their
extensive coverage, stable orbits, scalability, and adherence to international
regulations. However, satellite-based NTN presents unique challenges, including
long propagation delay, high Doppler shift, frequent handovers, spectrum
sharing complexities, and intricate beam and resource allocation, among others.
The integration of NTNs into existing terrestrial networks in 6G introduces a
range of novel challenges, including task offloading, network routing, network
slicing, and many more. To tackle all these obstacles, this paper proposes
Artificial Intelligence (AI) as a promising solution, harnessing its ability to
capture intricate correlations among diverse network parameters. We begin by
providing a comprehensive background on NTN and AI, highlighting the potential
of AI techniques in addressing various NTN challenges. Next, we present an
overview of existing works, emphasizing AI as an enabling tool for
satellite-based NTN, and explore potential research directions. Furthermore, we
discuss ongoing research efforts that aim to enable AI in satellite-based NTN
through software-defined implementations, while also discussing the associated
challenges. Finally, we conclude by providing insights and recommendations for
enabling AI-driven satellite-based NTN in future 6G networks.Comment: 40 pages, 19 Figure, 10 Tables, Surve
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Recent Advances in Social Data and Artificial Intelligence 2019
The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
AI meets CRNs : a prospective review on the application of deep architectures in spectrum management
The spectrum low utilization and high demand conundrum created a bottleneck towards
ful lling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated
as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting
temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became
the rst step towards the intelligentization of mobile and wireless networks, and in order to strengthen
its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of arti cial
intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application
of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at lling the
gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies
of spectrum management. This is a prospective review whose primary objective is to provide an in-depth
exploration of the recent trends in AI strategies employed in mobile and wireless communication networks.
The existing reviews in this area have not considered the relevance of incorporating the mathematical
fundamentals of each AI strategy and how to tailor them to speci c mobile and wireless networking
problems. Therefore, this reviewaddresses that problem by detailing howdeep architectures can be integrated
into spectrum management problems. Beyond reviewing different ways in which deep architectures can be
integrated into spectrum management, model selection strategies and how different deep architectures can
be tailored into the CR space to achieve better performance in complex environments are then reported in
the context of future research directions.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) at the University of Pretoria.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2022Electrical, Electronic and Computer Engineerin
A comprehensive survey on radio resource management in 5G HetNets: current solutions, future trends and open issues
The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and
user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions
for advanced mechanisms are presented
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Stable and Efficient Sparse Recovery for Machine Learning and Wireless Communication
Recent theoretical study shows that the sparsest solution to an underdetermined linear system is unique, provided the solution vector is sufficiently sparse, and the operator matrix has sufficiently incoherent column vectors. In addition, efficient algorithms have been discovered to find such solutions. This intriguing result opens a new door for many potential applications. In this thesis, we study the design of a class of greedy algorithms that are extremely efficient, e.g., Orthogonal Matching Pursuit (OMP). These greedy algorithms suffer from a stability issue that the greedy selection approach always make locally optimal decisions, thereby easily biasing and mistaking the solutions in particular under data noise. We propose a solution approach that in designing greedy algorithms, new constraints can be devised by leveraging application-specific insights and incorporated into the algorithms. Given that sparse recovery problems by definition are underdetermined, introducing additional constraints can significantly improve the stability of greedy algorithms, yet retain their efficiency.Engineering and Applied Science
Query-driven learning for automating exploratory analytics in large-scale data management systems
As organizations collect petabytes of data, analysts spend most of their time trying to extract insights. Although data analytic systems have become extremely efficient and sophisticated, the data exploration phase is still a laborious task with high productivity, monetary and mental costs. This dissertation presents the Query-Driven learning methodology in which multiple systems/frameworks are introduced to address the need of more efficient methods to analyze large data sets. Countless queries are executed daily, in large deployments, and are often left unexploited but we believe they are of immense value. This work describes how Machine Learning can be used to expedite the data exploration process by (a) estimating the
results of aggregate queries (b) explaining data spaces through interpretable Machine Learning models (c) identifying data space regions that could be of interest to the data analyst. Compared to related work in all the associated domains, the proposed solutions do not utilize any of the underlying data. Because of that, they are extremely efficient, decoupled from underlying infrastructure and can easily be adapted. This dissertation is a first account of
how the Query-Driven methodology can be effectively used to expedite the data exploration process focusing solely on extracting knowledge from queries and not from data
Advancing Robot Autonomy for Long-Horizon Tasks
Autonomous robots have real-world applications in diverse fields, such as
mobile manipulation and environmental exploration, and many such tasks benefit
from a hands-off approach in terms of human user involvement over a long task
horizon. However, the level of autonomy achievable by a deployment is limited
in part by the problem definition or task specification required by the system.
Task specifications often require technical, low-level information that is
unintuitive to describe and may result in generic solutions, burdening the user
technically both before and after task completion. In this thesis, we aim to
advance task specification abstraction toward the goal of increasing robot
autonomy in real-world scenarios. We do so by tackling problems that address
several different angles of this goal. First, we develop a way for the
automatic discovery of optimal transition points between subtasks in the
context of constrained mobile manipulation, removing the need for the human to
hand-specify these in the task specification. We further propose a way to
automatically describe constraints on robot motion by using demonstrated data
as opposed to manually-defined constraints. Then, within the context of
environmental exploration, we propose a flexible task specification framework,
requiring just a set of quantiles of interest from the user that allows the
robot to directly suggest locations in the environment for the user to study.
We next systematically study the effect of including a robot team in the task
specification and show that multirobot teams have the ability to improve
performance under certain specification conditions, including enabling
inter-robot communication. Finally, we propose methods for a communication
protocol that autonomously selects useful but limited information to share with
the other robots.Comment: PhD dissertation. 160 page
Future Transportation
Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others