Μηχανική μάθηση σε ασύρματα δίκτυα
View/
Keywords
Μηχανική μάθηση ; Ασύρματα δίκτυαAbstract
The scope of this thesis is to examines machine learning techniques applied to wireless
networks. The purpose of this paper is to present the basic techniques for machine learning, as
well as how they can be applied in wireless communications. Machine learning is a new
emerging scientific field with many applications, while it has the ability to predict and solve
more and more complex problems that they have integrated into almost every scientific field,
technological or not. On the other hand, wireless communications are now an integral part of
our daily lives, resulting the mobile devices and the demands of users are constantly increasing.
The new generation wireless networks are expected to support extremely high data rates but
also new applications which will have high requirements (QOS-quality of service) and will bring
new challenges in wireless technology. Dynamic resource allocation and the constant change of
the wireless channel has always been a field of research. So the challenge is to create a smart
network that will learn and adapt to the data but also will make decisions on its own with the
aim of the best QOS of the end user but also the best use of network resources. Artificial
intelligence and more specifically machine learning are the most suitable for creating such
networks, offering the intelligence they need with perhaps excellent results. It is therefore
inevitable that next generation networks will be assisted by these technologies, while as our
knowledge increases better applications will be created.
In the first chapter there is an introduction to machine learning, we refer to the three types of
machine learning , which analyze the basic techniques, the algorithms that use, their advantages
and disadvantages. In the second chapter we refer to existing wireless communication systems
giving their basic characteristics. For each system, a machine learning method is presented,
which is adapted to the respective system, while experimental results are presented, comparing
their performance with the traditional techniques so far. In the third and last one charter,
results from python simulations for two different systems are presented, showing their
performance.
Postgraduate Studies Programme
Ψηφιακές Επικοινωνίες και ΔίκτυαDepartment
Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών ΣυστημάτωνNumber of pages
180Language
GreekCollections
Except where otherwise noted, this item's license is described as
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα