Radio resource management for high-speed wireless cellular networks.

Ha, Vu N. (2016). Radio resource management for high-speed wireless cellular networks. Thèse. Québec, Université du Québec, Institut national de la recherche scientifique, Doctorat en télécommunications, 264 p.

Prévisualisation

PDF Télécharger (3MB) | Prévisualisation

Résumé

The fifth-generation (5G) wireless cellular system, which would be deployed by 2020, is expected to deliver significantly higher capacity and better network performance compared to those of the current fourth-generation (4G) system. Specifically, it is predicted that tens of billions of wireless devices will be connected to the wireless network over next few years, which results in an exponential explosion of mobile data traffic. Therefore, more advanced wireless architecture, as well as radical and innovative access technologies, must be proposed to meet this urgent increasing growth of mobile data and connectivity requirements in the coming years. Toward this end, two important wireless cellular architectures, namely wireless heterogeneous networks (HetNets) based on the dense deployment of small cells and the cloud radio access networks (C-RANs) have been proposed and actively studied by both academic and industry communities. Besides enabling a lot of advantages in increasing network coverage as well as end-to-end system throughput, these two novel network architectures have also raised some novel technical challenges and opened exciting research areas for further research. Motivated by the aforementioned technical challenges, the general objective of this Ph.D. research is to develop efficient radio resource allocation and interference management algorithms for the future high-speed wireless cellular networks. In particular, we have developed adaptive resource management techniques that can effectively control both critical interferences in wireless HetNets and designed innovative access techniques for the C-RAN that efficiently exploit the radio, cloud computation resources, and fronthaul capacity. Our research has resulted in four major research contributions, which are presented in four corresponding main chapters of this dissertation. First, we consider the joint base station association and power control design for singlecarrier- based HetNets, which is presented in Chapter 5. In particular, we have developed a generalized BSA and PC algorithm and proved its convergence if the underlying power update function satisfies the so-called two-sided-scalable property. In addition, we have proposed an hybrid power control adaptation algorithm that effectively adjusts key design parameters to maintain the SINR requirements of all users whenever possible while enhancing the system throughput. Second, we study fair resource allocation design with subcarrier assignment and power control for OFDMA-based HetNets, which is presented in Chapter 6. Specifically, we have presented a resource allocation formulation for the two-tier macrocell-femtocell network that aims to maximize the total minimum rate of all femtocell subject to QoS protection constraints for macrocell users. We have proposed a low-complexity distributed joint subchannel and power allocation algorithm where FBSs can make subchannel allocations for femto user equipments in the distributed manner. Third, we design the joint cooperative transmission protocol for the downlink C-RAN, which is covered in Chapter 7. The considered system captures the fact that fronthaul links connecting remote radio heads with cloud processing center have limited capacity, which is translated into the new fronthaul capacity constraint involving a non-convex and discontinuous function. Then, we propose two low-complexity algorithms, so-called pricing-based algorithm, and iterative linear-relaxed algorithm, to duel with this difficult problem. Finally, we consider the resource allocation for virtualized uplink C-RAN in which multiple OPs are assumed to share the C-RAN infrastructure and resources to serve their users under the limited fronthaul capacity and cloud computation. The research outcomes of this study are presented in Chapter 8. In particular, we model the design into the upper-level and lowerlevel problems. The upper-level problem focuses on slicing the fronthaul capacity and cloud computing resources for all OPs. Then, the lower-level maximizes the operator’s sum rate by optimizing users’ transmission rates and quantization bit allocation for the compressed I/Q baseband signals. Then, a two-stage algorithmic framework is proposed to solve these problems. We have developed various efficient resource allocation algorithms for reducing the transmission power and increasing the end-to-end network throughput for both HetNets and CRANs. Furthermore, extensive numerical results are presented to gain further insights and to evaluate the performance of our resource allocation designs. The numerical results confirm that our proposed protocols can achieve efficient spectrum utilization, power saving, and significant performance gains compared to existing and fast greedy designs.

Type de document:	Thèse Thèse
Directeur de mémoire/thèse:	Le, Long Bao
Mots-clés libres:	Télécommunications
Centre:	Centre Énergie Matériaux Télécommunications
Date de dépôt:	04 mars 2019 16:00
Dernière modification:	04 mars 2019 16:00
URI:	https://espace.inrs.ca/id/eprint/7869

Gestion Actions (Identification requise)

Modifier la notice