## 2 Related Works  
CRAN technology is one suitable candidate for 5G systems; the advantages and challenges for various candidate architectures and their performance capacities are reviewed in [1]. Provisioning and allocation methods of virtual base stations (VBSs) in the base band unit (BBU) are proposed in [5]. The advantages, challenges, and future directions of CRAN technology are also studied in this survey paper. However, in [12], using integer linear programming (ILP) cells are optimally assigned to different BBU pools. It focuses on minimizing the capital expenditure (CAPEX) of CRAN deployment, although the operational expenditure (OPEX) is not taken into account. In [13], the Virtual Radio Access Networks (VRAN) Placement and Assignment Problem (VRAN-PAP) is formulated as a binary integer linear program (BILP) where the center of attention is only to minimize the server and front haul link setup cost rather than considering other cost issues.  
Cooperative interference mitigation and handover management issues are addressed in [14], which did not analyze user QoS requirement and resource allocation schemes. The authors of [8] address the RRH to BBU assignment problem as a bin-packing problem which requires a significant amount of computational complexity, where the end-user QoE is not traced. In [6], even though QoS of requests are considered, service provider profit is not studied. In [15], the authors introduce a clustering mechanism to maximize user satisfaction and enhance network energy efficiency. Another noteworthy study [10] proposes a user association, and power allocation in mmWave-based ultra-dense bands is considered with load balancing and energy efficiency. Nevertheless, in both of the mentioned contributions, service provider profit in terms of executing a request on the cloud server is not discussed.  
To exploit next-generation CRAN operations, a main challenging issue is how to properly control system resources. To address this, one article [9] employs the news-vendor game model for resource allocation. Here, the two-stage game-based resource management approach can practically adapt current system conditions. However, an optimal tradeoff between service provider profit and customer satisfaction are not concentrated here as well as in the state-of-the-art works.  
In our proposed work, a QoE and Profit-Aware optimal Resource Allocation scheme in a Cloud Radio Access Network has been developed. Here, focusing on maximization of user QoE and service provider profit, the computational resource allocation problem for the incoming requests is formulated as a multi-objective non-linear programming optimization problem. While allocating resources, the amount of data to be processed, the maximum allowable service delay per request, and received signal strength of the connected device of the user are also taken into account for identifying the priority. In this work, a tradeoff is made between profit and customer satisfaction for service provisioning in 5G CRAN architecture. For efficient resource utilization and enhancement of system performance, the scheduling interval of the system is dynamically determined according to the request arrival rate and service rate. To the best of our knowledge, this work is the first to address the problem of maximizing user QoE as well as service provider profit under optimal resource utilization.  
A comparative study among the state-of-the-art works focusing on different aspects of CRAN and our proposed Quality-of-Experience and Profit-aware Resource Allocation scheme or QEPRA system is given in Table 1. In the literature, several mechanisms [12; 13; 16] basically focus on infrastructure development of CRAN architecture. Another noteworthy paper [6] addresses QoS-aware scheduling and based on the service provider profit [9] allocates resources for incoming requests. However, our proposed system makes a tradeoff between user QoE as well as service provider profit while utilizing resources efficiently.