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.
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