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

dc.contributor.authorBai, Jiananen
dc.contributor.committeechairLiu, Lingjiaen
dc.contributor.committeememberYi, Yangen
dc.contributor.committeememberZeng, Haiboen
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2022-07-09T06:00:16Zen
dc.date.available2022-07-09T06:00:16Zen
dc.date.issued2021-01-14en
dc.description.abstractInternet 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.en
dc.description.abstractgeneralIn 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.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:28877en
dc.identifier.urihttp://hdl.handle.net/10919/111187en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectinternet-of-thingsen
dc.subjectmulti-agent reinforcement learningen
dc.subjectmassive connectivityen
dc.subjectrandom accessen
dc.titleRandom Access Control In Massive Cellular Internet of Things: A Multi-Agent Reinforcement Learning Approachen
dc.typeThesisen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen
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