Electrical and Computer Engineering ETDs

Publication Date

Fall 10-3-2023

Abstract

This Ph.D. dissertation presents a pioneering Multi-Agent System (MAS) approach for intelligent network management, particularly suited for next-generation networks like 5G and 6G. The thesis is segmented into four critical parts. Firstly, it contrasts the benefits of agent-based design over traditional micro-service architectures. Secondly, it elaborates on the implementation of network service agents in Python Agent Development Environment (PADE), employing machine learning and deep learning algorithms for performance evaluation. Thirdly, a new scalable approach, Scalable and Efficient DevOps (SE-DO), is introduced to optimize agent performance in resource-constrained settings. Fourthly, the dissertation delves into Quality of Service (QoS) and Radio Resource Management using reinforcement learning agents. Lastly, an Autonomous, Intelligent AI/ML Framework is proposed for proactive management and dynamic routing in 6G networks, using advanced algorithms like Speed Optimized LSTM. Overall, the work holds substantial promise for transforming network management through automation, adaptability, and advanced intelligence.

Keywords

Multi-Agent Systems (MAS), Intelligent Network Management, 6G Networks, Quality of Service (QoS), Reinforcement Learning Agents, Dynamic Routing

Document Type

Dissertation

Language

English

Degree Name

Computer Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Michael Devetsikiotis

Second Committee Member

Sisay Tadesse Arzo

Third Committee Member

Ali Bidram

Fourth Committee Member

Riccardo Bassoli

Comments

Proactive Management, Speed Optimized LSTM, Next-Generation Networks, Agent-Based Design, Micro-Service Architectures, Python Agent Development Environment (PADE), Deep Learning Algorithms, Machine Learning, Radio Resource Management, Autonomous Network Systems,

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