Abstract
Wired or wireless, connectivity has been a vital commodity of life and more sorecently in the realm of information age. Those who have access to faster, more
reliable and more ubiquitous connectivity—put simply, those who are “better
connected”—will have significant advantages in commerce, research and a host
of other arenas. In regards to wireless communications, due to the explosion
in demand for higher capacity networks, availability of free spectrum resources
have become increasingly scarce. The UHF spectrum band in particular, due
to its excellent electromagnetic properties, has been reported as inefficiently
used and congested by many spectrum regulators of the world. This spectrum
resource scarcity issue combined with the ongoing research and development
for more intelligent, autonomous and self-aware radio communication led to a
vast amount of research on the concept of Cognitive Radio.
This thesis researches the learning unit of cognitive radios. The learning unit
is responsible for processing information and autonomous decision making. In
particular, the research is focused on the extraction and usage of contextual
information from the radio environment (e.g. Radio Access Technology type,
channel access pattern learning/recognition) and how such information could
be exploited to improve the performance of the cognitive radio. The key metrics
discussed will be based on information extraction under noise, channel
blocking and interference reduction to primary users.
We present a set of novel works involving Machine Learning, which is a branch
of Artificial Intelligence. New implementation and use cases of state-of-the-art
machine learning algorithms are presented that learn from real-life data. In a
testbed setup we program software defined radios to recognize different Radio
Access Technologies and their channel access patterns. The main technique
used in the majority of the thesis is Artificial Neural Networks, concretely:
Multi-Layer Perceptron Neural Nets, Self-Organizing Neural Nets, and Deep
Auto-Encoders. In some of the works these neural network architectures have
been combined in a novel way with Support Vector Machines, and Reinforcement
Learning algorithms for channel classification and access.
In this thesis we show that it is possible to achieve 95% correct classification
at -25 dB among three different radio access technologies, namely, DVB-T,
WCDMA and IEEE 802.11a, where, consequently, we can reason over the
outcome of this classification to differentiate between primary and secondary
transmissions. We also show that, through the use of the proposed autoencoder
approximate Q-learning technique, such context-aware cognitive radio
can achieve better key performance metrics in dynamic spectrum access as
compared to previously researched Q-learning algorithms.
| Date of Award | 2016 |
|---|---|
| Original language | English |
| Awarding Institution |
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| Supervisor | Abdol-Hamid Aghvami (Supervisor) & Fatin Said (Supervisor) |