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Efstathios Katranaras

Researcher at University of Surrey

Publications -  27
Citations -  217

Efstathios Katranaras is an academic researcher from University of Surrey. The author has contributed to research in topics: Fading & Telecommunications link. The author has an hindex of 8, co-authored 27 publications receiving 201 citations. Previous affiliations of Efstathios Katranaras include Sequans.

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Journal ArticleDOI

Energy Efficiency Benefits of RAN-as-a-Service Concept for a Cloud-Based 5G Mobile Network Infrastructure

TL;DR: A vision of the advantages of the RANaaS is given, its benefits in terms of energy efficiency are presented, and a consistent system-level power model is proposed as a reference for assessing innovative functionalities toward 5G systems.
Journal ArticleDOI

Uplink capacity of a variable density cellular system with multicell processing

TL;DR: This work investigates the information theoretic capacity of the uplink of a cellular system, and finds the capacity when the multiple antennas used for each Base Station are omnidirectional and uncorrelated (power gain on top of degrees of freedom gain).
Journal ArticleDOI

Dynamic femtocell resource allocation for managing inter-tier interference in downlink of heterogeneous networks

TL;DR: This study investigates the downlink resource allocation problem in orthogonal frequency division multiple access heterogeneous networks consisting of macrocells and femtocells sharing the same frequency band and proposed dynamic resource allocation scheme provides performance close to the optimal solution, while introducing much lower complexity.
Proceedings ArticleDOI

Sum Rate of Linear Cellular Systems with Clustered Joint Processing

TL;DR: The sum rate of the uplink of a linear network of cells when clustered coordinated processing is adopted among the base stations in a generalised fading environment is derived.
Journal ArticleDOI

Performance Analysis and Optimal Cooperative Cluster Size for Randomly Distributed Small Cells Under Cloud RAN

TL;DR: Simulation results show that the proposed analytical framework can be utilized to accurately evaluate the performance of practical cloud-based small cell networks employing clustered cooperation, using the homogenous Poisson point processing model.