Random Access Control In Massive Cellular Internet of Things: A Multi-Agent Reinforcement Learning Approach

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2021

Author dc:creator

• Bai, Jianan

Chair dc:contributor.committeechair

• Liu, Lingjia

Committee members dc:contributor.committeemember

• Yi, Yang
• Zeng, Haibo

• internet-of-things
• multi-agent reinforcement learning
• massive connectivity
• random access

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:28877

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/111187