Resource Allocation for Cloud Radio Access Networks

Handle URI:
http://hdl.handle.net/10754/608606
Title:
Resource Allocation for Cloud Radio Access Networks
Authors:
Dhifallah, Oussama ( 0000-0002-8961-3553 )
Abstract:
Cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio system. In CRANs, joint signal processing is performed at multiple cloud computing centers (clouds) that are connected to several base stations (BSs) via high capacity backhaul links. As a result, large-scale interference management and network power consumption reduction can be effectively achieved. Unlike recent works on CRANs which consider a single cloud processing and treat inter-cloud interference as background noise, the first part of this thesis focuses on the more practical scenario of the downlink of a multi-cloud radio access network where BSs are connected to each cloud through wireline backhaul links. Assume that each cloud serves a set of pre-known single-antenna mobile users (MUs). This part focuses on minimizing the network total power consumption subject to practical constraints. The problems are solved using sophisticated techniques from optimization theory (e.g. Dual Decomposition-based algorithm and the alternating direction method of multipliers (ADMM)-based algorithm). One highlight of this part is that the proposed solutions can be implemented in a distributed fashion by allowing a reasonable information exchange between the coupled clouds. Additionally, feasible solutions of the considered optimization problems can be estimated locally at each iteration. Simulation results show that the proposed distributed algorithms converge to the centralized algorithms in a reasonable number of iterations. To further account of the backhaul congestion due to densification in CRANs, the second part of this thesis considers the downlink of a cache-enabled CRAN where each BS is equipped with a local-cache with limited size used to store the popular files without the need for backhauling. Further, each cache-enabled BS is connected to the cloud via limited capacity backhaul link and can serve a set of pre-known single antenna MUs. This part assumes that only imperfect channel state information (CSI) is available at the cloud. This part focuses on jointly minimizing the network total power consumption as well as backhaul cost. It then suggests solving this optimization problem using the majorization-minimization (MM) approach. Simulation results show that the proposed algorithm converges in a reasonable number of iterations.
Advisors:
Al-Naffouri, Tareq
Committee Member:
Dahrouj, Hayssam ( 0000-0002-3075-321X ) ; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 ) ; Shihada, Basem ( 0000-0003-4434-4334 ) ; Ghanem, Bernard ( 0000-0002-5534-587X )
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program
Program:
Electrical Engineering
Issue Date:
Apr-2016
Type:
Thesis
Appears in Collections:
Theses; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorAl-Naffouri, Tareqen
dc.contributor.authorDhifallah, Oussamaen
dc.date.accessioned2016-05-08T13:01:40Zen
dc.date.available2016-05-08T13:01:40Zen
dc.date.issued2016-04en
dc.identifier.urihttp://hdl.handle.net/10754/608606en
dc.description.abstractCloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio system. In CRANs, joint signal processing is performed at multiple cloud computing centers (clouds) that are connected to several base stations (BSs) via high capacity backhaul links. As a result, large-scale interference management and network power consumption reduction can be effectively achieved. Unlike recent works on CRANs which consider a single cloud processing and treat inter-cloud interference as background noise, the first part of this thesis focuses on the more practical scenario of the downlink of a multi-cloud radio access network where BSs are connected to each cloud through wireline backhaul links. Assume that each cloud serves a set of pre-known single-antenna mobile users (MUs). This part focuses on minimizing the network total power consumption subject to practical constraints. The problems are solved using sophisticated techniques from optimization theory (e.g. Dual Decomposition-based algorithm and the alternating direction method of multipliers (ADMM)-based algorithm). One highlight of this part is that the proposed solutions can be implemented in a distributed fashion by allowing a reasonable information exchange between the coupled clouds. Additionally, feasible solutions of the considered optimization problems can be estimated locally at each iteration. Simulation results show that the proposed distributed algorithms converge to the centralized algorithms in a reasonable number of iterations. To further account of the backhaul congestion due to densification in CRANs, the second part of this thesis considers the downlink of a cache-enabled CRAN where each BS is equipped with a local-cache with limited size used to store the popular files without the need for backhauling. Further, each cache-enabled BS is connected to the cloud via limited capacity backhaul link and can serve a set of pre-known single antenna MUs. This part assumes that only imperfect channel state information (CSI) is available at the cloud. This part focuses on jointly minimizing the network total power consumption as well as backhaul cost. It then suggests solving this optimization problem using the majorization-minimization (MM) approach. Simulation results show that the proposed algorithm converges in a reasonable number of iterations.en
dc.language.isoenen
dc.subjectCloud Radio Accessen
dc.subjectDistributed optimizationen
dc.subjectCache-enable networken
dc.titleResource Allocation for Cloud Radio Access Networksen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberDahrouj, Hayssamen
dc.contributor.committeememberAlouini, Mohamed-Slimen
dc.contributor.committeememberShihada, Basemen
dc.contributor.committeememberGhanem, Bernarden
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.nameMaster of Scienceen
dc.person.id132130en
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