Author ORCID Identifier
0000-0001-6753-4216
Document Type
Dissertation
Date of Award
5-31-2024
Degree Name
Doctor of Philosophy in Computer Engineering - (Ph.D.)
Department
Electrical and Computer Engineering
First Advisor
Nirwan Ansari
Second Advisor
Abdallah Khreishah
Third Advisor
Roberto Rojas-Cessa
Fourth Advisor
MengChu Zhou
Fifth Advisor
Cristian Borcea
Abstract
The advent of next-generation wireless networks ushers in a new era of potential, harnessing cutting-edge technologies like mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing as pivotal drivers of transformation. Within this landscape, an innovative approach is proposed by introducing a NOMA-enabled network slicing technique within MEC networks. This approach aims to achieve multiple objectives: meeting stringent quality of service requirements, minimizing service latency, and enhancing spectral efficiency. By seamlessly integrating NOMA with network slicing in edge computing environments, significant reductions in overall latency are achieved, alongside ensuring optimal resource allocation for NOMA users. To address these challenges, a mixed-integer nonlinear programming (MINLP) problem is formulated and tackled, utilizing heuristic algorithms to simultaneously minimize latency and energy consumption for tasks offloaded to the MEC server.
Delving deeper into the nuances of NOMA, particularly in dense traffic scenarios, a Hybrid Multiple Access (HYMA) scheme tailored for such environments is proposed. This scheme strategically prioritizes NOMA to leverage its advantages in spectral and energy efficiency. Leveraging the capabilities of network slicing and a partial task offloading scheme, the optimization of energy consumption within the MEC network is achieved through solving a MINLP problem. Expanding the horizon further, a novel unmanned aerial vehicle (UAV)-enabled MEC framework is introduced specifically designed for massive Internet of things (IoT) networks. This framework incorporates numerology schemes with the overarching goal of enhancing battery life of IoT devices and spectral efficiency of the network. Utilizing a multi-objective optimization problem (MOOP), the aim is to maximize uplink spectral efficiency while concurrently minimizing energy consumption for IoT devices. Validation of this approach is conducted through sequential sub-problems, heuristic algorithms, and extensive simulation results.
In the context of a multi-tier heterogeneous network (HetNet) architecture, NOMA plays a pivotal role as a catalyst for federated edge learning (FEL). A three-tier model for NOMA-enabled HetNet is proposed for FEL, comprising a top tier hosting a global model aggregator, intermediate tier edge servers, and a bottom tier consisting of user equipment. This holistic approach minimizes total energy consumption for federated learning, facilitated through the formulation of a non-linear programming problem and the introduction of two sequential algorithms aimed at optimizing energy consumption within the federated learning context. Through the integration of these cutting-edge technologies, our proposed approaches effectively tackle the complexities of wireless networks, prioritizing essential factors like latency, spectral efficiency, and energy consumption across diverse scenarios. Thoroughly validated through comprehensive simulations, these solutions not only deliver immediate advantages but also lay the groundwork for the development of highly efficient next-generation wireless networks.
Recommended Citation
Hossain, Mohammad Arif, "Network slicing and noma enabled mobile edge computing for next-generation networks" (2024). Dissertations. 1756.
https://digitalcommons.njit.edu/dissertations/1756
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Electronics Commons
