Han, Zhu2020-06-04May 20202020-05May 2020Portions of this document appear in: R. A. Banez, H. Gao, L. Li, C. Yang, Z. Han, and H. V. Poor, “Belief and Opinion Evolution in Social Networks Based on a Multi-Population Mean Field Game Approach,” IEEE International Conference on Communications, Dublin, Ireland, Jun. 2020. And in: Banez, Reginald A., Lixin Li, Chungang Yang, Lingyang Song, and Zhu Han. "A Mean-Field-Type Game Approach to Computation Offloading in Mobile Edge Computing Networks." In ICC 2019-2019 IEEE International Conference on Communications (ICC), pp. 1-6. IEEE, 2019. And in: Banez, Reginald A., Haitao Xu, Nguyen H. Tran, Ju Bin Song, Choong Seon Hong, and Zhu Han. "Network Virtualization Resource Allocation and Economics Based on Prey–Predator Food Chain Model." IEEE Transactions on Communications 66, no. 10 (2018): 4738-4752.https://hdl.handle.net/10657/6698According to the latest Cisco Annual Internet Report, the current communication network infrastructures are approaching their limits caused by the increasing data traffic, more frequent network usage, and rising number of connected devices. In order to overcome these limitations, enabling technologies such as ultra-dense networks, multi-access edge networks, and massive antenna arrays are proposed as part of the future generation of communication networks. However, in order to analyze, model, and simulate these technologies, an appropriate mathematical framework that can handle large number of interacting entities is necessary. Hence, the application of mean field games (MFGs) to future communication networks is proposed in this dissertation. The first work of this dissertation deals with the modeling of user behavior through belief and opinion evolution in social networks, which is essential in improving the services provided by a network provider. A multiple-population MFG approach is applied to depict the behavior of social network users in a multiple-group setting. Theoretical and experimental simulations using a social evolution dataset suggest the effectiveness of the MFG approach in estimating and predicting the distribution of belief and opinion in social networks. The second work of this dissertation investigates an effective and efficient method for computation offloading in multi-access edge computing networks (MECN). A mean-field-type game (MFTG) approach is utilized to design non-cooperative and cooperative computation offloading algorithms to decrease latency and energy consumption. The results indicate that the proposed MFTG-based algorithms can optimize energy consumption and latency associated with computation offloading. Then, the third work of this dissertation presents a proposed dynamic hierarchical framework for resource allocation in network virtualization. Lastly, this dissertation is concluded with a summary of important results and remarks. Furthermore, future works integrating MFG in unmanned aerial vehicle (UAV) networks, network virtualization, and Internet-of-Things (IoT) are proposed.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Mean field gamesmean-field-type gamesmean field games with several populationsdynamic hierarchical gameprey-predator food chain modelsocial networksbelief and opinion evolutioncomputation offloadingmulti-access edge computing networksmobile edge computing networksnetwork virtualization2020-06-04Thesisborn digital