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.
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.
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