Internet of things (IoT) is envisioned as a promising paradigm to interconnect enormous
wireless devices. However, the success of IoT is challenged by the difficulty of access management
of the massive amount of sporadic and unpredictable user traffics. This thesis focuses
on the contention-based random access in massive cellular IoT systems and introduces two
novel frameworks to provide enhanced scalability, real-time quality of service management,
and resource efficiency. First, a local communication based congestion control framework
is introduced to distribute the random access attempts evenly over time under bursty traffic.
Second, a multi-agent reinforcement learning based preamble selection framework is
designed to increase the access capacity under a fixed number of preambles. Combining the
two mechanisms provides superior performance under various 3GPP-specified machine type
communication evaluation scenarios in terms of achieving much lower access latency and
fewer access failures. / Master of Science / In the age of internet of things (IoT), massive amount of devices are expected to be connected
to the wireless networks in a sporadic and unpredictable manner. The wireless connection
is usually established by contention-based random access, a four-step handshaking process
initiated by a device through sending a randomly selected preamble sequence to the base
station. While different preambles are orthogonal, preamble collision happens when two
or more devices send the same preamble to a base station simultaneously, and a device
experiences access failure if the transmitted preamble cannot be successfully received and
decoded. A failed device needs to wait for another random access opportunity to restart the
aforementioned process and hence the access delay and resource consumption are increased.
The random access control in massive IoT systems is challenged by the increased access
intensity, which results in higher collision probability. In this work, we aim to provide better
scalability, real-time quality of service management, and resource efficiency in random access
control for such systems. Towards this end, we introduce 1) a local communication based
congestion control framework by enabling a device to cooperate with neighboring devices
and 2) a multi-agent reinforcement learning (MARL) based preamble selection framework by
leveraging the ability of MARL in forming the decision-making policy through the collected
experience. The introduced frameworks are evaluated under the 3GPP-specified scenarios
and shown to outperform the existing standard solutions in terms of achieving lower access
delays with fewer access failures.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/111187 |
| Date | 14 January 2021 |
| Creators | Bai, Jianan |
| Contributors | Electrical Engineering, Liu, Lingjia, Yi, Yang, Zeng, Haibo |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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