| dc.description.abstract | We anticipate that there will be an enormous amount of wireless devices connected
to the Internet through the future-generation wireless networks. Those wireless devices vary
from self-driving vehicles to smart wearable devices and intelligent house- hold electrical
appliances. Under such circumstances, the network resource optimization faces the challenge of
the requirement of both flexibility and performance. Current wireless communication still
relies on one-size-fits-all optimization algorithms, which require meticulous design and
elaborate maintenance, thus not flexible and cannot meet the growing requirements well. The
future-generation wireless networks should be “smarter”, which means that the artificial
intelligence-driven software-level design will play a more significant role in network
optimization.
In this thesis, we present three different ways of leveraging artificial intelligence (AI) and
machine learning (ML) to design network optimization algorithms for three wireless Internet of
things network optimization problems. Our ML-based approaches cover the use of multi-layer
feed-forward artificial neural network and the graph convolutional network as the core of
our AI decision-makers. The learning methods are supervised learning (for static
decision-making) and reinforcement learning (for dynamic decision-making). We demonstrate the
viability of applying ML in future- generation wireless network optimizations through
extensive simulations. We summarize our discovery on the advantage of using ML in wireless
network optimizations as the following three aspects:
1. Enabling the distributed decision-making to achieve the performance that near a centralized
solution, without the requirement of multi-hop information;
2. Tackling with dynamic optimization through distributed self-learning decision- making agents,
instead of designing a sophisticated optimization algorithm;
3. Reducing the time used in optimizing the solution of a combinatorial optimization problem.
We envision that in the foreseeable future, AI and ML could help network service
designers and operators to improve the network quality of experience swiftly and less
expensively. | en_US |
