Tradeoff between User Quality-Of-Experience and Service Provider Profit in 5G Cloud Radio Access Network

Mahbuba Afrin, Md. Abdur Razzaque, Iffat Anjum, Mohammad Mehedi Hassan and Atif Alamri
Additional contact information
Mahbuba Afrin: Department of Computer Science and Engineering, University of Dhaka, Dhaka 1000, Bangladesh
Md. Abdur Razzaque: Department of Computer Science and Engineering, University of Dhaka, Dhaka 1000, Bangladesh
Iffat Anjum: Department of Computer Science and Engineering, University of Dhaka, Dhaka 1000, Bangladesh
Mohammad Mehedi Hassan: College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Atif Alamri: College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

Sustainability, 2017, vol. 9, issue 11, 1-19

Abstract: In recent years, the Cloud Radio Access Network (CRAN) has become a promising solution for increasing network capacity in terms of high data rates and low latencies for fifth-generation (5G) cellular networks. In CRAN, the traditional base stations (BSs) are decoupled into remote radio heads (RRHs) and base band units (BBUs) that are respectively responsible for radio and baseband functionalities. The RRHs are geographically proximated whereas the the BBUs are pooled in a centralized cloud named BBU pool. This virtualized architecture facilitates the system to offer high computation and communication loads from the impetuous rise of mobile devices and applications. Heterogeneous service requests from the devices to different RRHs are now sent to the BBUs to process centrally. Meeting the baseband processing of heterogeneous requests while keeping their Quality-of-Service (QoS) requirements with the limited computational resources as well as enhancing service provider profit is a challenging multi-constraint problem. In this work, a multi-objective non-linear programming solution to the Quality-of-Experience (QoE) and Profit-aware Resource Allocation problem is developed which makes a trade-off in between the two. Two computationally viable scheduling algorithms, named First Fit Satisfaction and First Fit Profit algorithms, are developed to focus on maximization of user QoE and profit, respectively, while keeping the minimum requirement level for the other one. The simulation environment is built on a relevant simulation toolkit. The experimental results demonstrate that the proposed system outperforms state-of-the-art works well across the requests QoS, average waiting time, user QoE, and service provider profit.

Keywords: 5G; cloud radio access network; computing resource allocation; quality-of-experience; profit maximization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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