Designing data-aided demand-driven user-centric architecture for 6G and beyond networks

Kasi, Shahrukh Khan

Designing data-aided demand-driven user-centric architecture for 6G and beyond networks

dc.contributor.advisor	Imran, Ali
dc.contributor.author	Kasi, Shahrukh Khan
dc.contributor.committeeMember	Sluss, James
dc.contributor.committeeMember	Cheng, Samuel
dc.contributor.committeeMember	Ford, Timothy
dc.date.accessioned	2023-07-21T16:37:36Z
dc.date.available	2023-07-21T16:37:36Z
dc.date.issued	2023-08-04
dc.date.manuscript	2023-06-13
dc.description.abstract	Despite advancements in capacity-enhancing technologies like massive MIMO (multiple input, multiple output) and intelligent reflective surfaces, network densification remains crucial for significant capacity gains in future networks such as 6G. However, network densification increases interference and power consumption. Traditional cellular architectures struggle to minimize these without compromising service quality or capacity, which necessitates a shift to a user-centric radio access network (UC-RAN). The UC-RAN approach offers additional degrees of freedom to ease the spectral-energy efficiency interlock while improving the service quality. However, its increased degrees of freedom make its optimal design and operation more challenging. This dissertation introduces four novel approaches for UC-RAN optimal design and operation. The objectives include mitigating interference, reducing power consumption, ensuring diverse user/vertical service quality, facilitating proactive network operation, risk-aware optimization, adopting an open radio access network, and enabling universal coverage. First, we construct an analytical framework to assess the effects of incorporating Coordinated Multipoint (CoMP) technology into UC-RAN to reduce interference and power consumption. We use stochastic geometry tools to derive expressions for network-wide coverage, spectral efficiency, and energy efficiency as a function of UC-RAN Configuration and Optimization Parameters (COPs), including data base station densities and user-centric service zone sizes. While the analytical framework provides insightful performance analysis that can guide overall system design, it cannot fully capture the dynamics of a UC-RAN system to enable optimal operation. Next, we present a Deep Reinforcement Learning (DRL) based method to dynamically orchestrate the UC-RAN service zone size to satisfy varying application demands of various service verticals during its operation. We define a novel multi-objective optimization problem that fairly optimizes otherwise conflicting key performance indicators (KPIs). DRL's practical adaptation by the industry remains thwarted by the risk it poses to the safe operation of a live network. To address this challenge, we propose a digital twin-enabled approach to enrich the DRL-based optimization framework, ensuring risk-aware COP optimization. We use Open Radio Access Network standards-based simulations to show that the proposed risk-aware DRL framework can maximize system-level KPIs while maintaining safe operational requirements. Lastly, we propose a hybrid model of aerial and terrestrial UC-RAN deployment to ensure universal coverage. We assess the impact of aerial base station parameters on system-level KPIs, providing a quantitative analysis of the advantages of a hybrid over a solely terrestrial UC-RAN. We develop a robust multi-objective function solvable via our DRL-based framework to balance and optimize these KPIs in a hybrid UC-RAN. Our extensive analytical and system-level simulation results suggest that these contributions can foster the much-needed paradigm shift towards demand-driven, elastic, and user-centric architecture in emerging and future cellular networks.	en_US
dc.identifier.uri	https://shareok.org/handle/11244/337942
dc.language	en_US	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/	*
dc.subject	User-centric Architecture	en_US
dc.subject	6G and beyond networks	en_US
dc.subject	deep reinforcement learning	en_US
dc.thesis.degree	Ph.D.	en_US
dc.title	Designing data-aided demand-driven user-centric architecture for 6G and beyond networks	en_US
ou.group	Gallogly College of Engineering::School of Electrical and Computer Engineering	en_US
shareok.orcid	0000-0002-5973-5322	en_US