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.
Highlights
The concept of next-generation cellular networks such as fifth-generation (5G) is becoming popular, since they can help to accommodate the indomitable increase of data traffic currently being experienced by the mobile network operators (MNOs) [1,2,3]
The remote radio heads (RRHs) are distributed over a wide geographic region located at each cell site, the base band units (BBUs) are pooled and moved into a centralized cloud named the BBU pool, where the RRHs and BBUs are connected via fronthaul link
The computational resource allocation problem for the incoming requests from RRHs to BBUs is formulated as a multi-objective non-linear programming (MONLP) optimization problem, focusing on maximization of end-user Quality-of-Experience (QoE) as well as service provider profit
Summary
The concept of next-generation cellular networks such as fifth-generation (5G) is becoming popular, since they can help to accommodate the indomitable increase of data traffic currently being experienced by the mobile network operators (MNOs) [1,2,3]. We have developed a resource allocation scheme for mapping heterogeneous requests from RRHs to BBUs so that user QoE and service providers’ profit is maximized under optimal resource utilization. A renewable energy-based user association and power allocation is proposed in [10] They have addressed the QoS requirement in terms of achievable rate, and completely disregard user QoE and service provider profit. Meeting up the QoS requirements of heterogeneous requests with the limited computational resources as well as enhancing service provider profit should be the prerequisite of a working CRAN environment. Computational resource allocation problem for incoming requests is formulated as multi-objective non-linear programming optimization problem focusing on maximization of end-user QoE as well as service provider profit.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have