## 1 Introduction  
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 leading MNOs are highly motivated to incorporate the virtualization concept of cloud computing in their networks, giving birth to the Cloud Radio Access Network (CRAN) [4]. In CRAN, the traditional base station (BS) is split into the radio frequency (RF) unit, referred to as the remote radio head (RRH) and the baseband processing unit (BBU) that provides the computational resources [4; 5]. Although the RRHs are distributed over a wide geographic region located at each cell site, the BBUs are now pooled and moved into a centralized cloud named the BBU pool, where the RRHs and BBUs are connected via fronthaul link. The virtualization of Radio Access Networks (RANs) is acknowledged as one of the important use cases of network function virtualization (NFV). It is considered to be the best candidate solution for supporting the next generation mobile communication network (5G) [1; 6], which embodies real-time cloud-computing, collaborative radio and centralized baseband processing features in cellular networks. By virtualization, the computing resources in the BBU pool can be dynamically shared among all the cells in the network. However, due to the heterogeneity of the incoming service requests from the RRHs to BBU pool, mapping an appropriate computational resource in the BBU pool to a particular request is still an important research challenge for the next-generation CRAN systems. Since in the CRAN architecture, mobile operators are dependent on cloud service providers for computational resources, service providers' profit must also be taken into account. The maximization of user Quality-of-Experience (QoE) in the competitive market must also be addressed. In this paper, 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. Figure 1 shows the CRAN service architecture and its components. Resource allocation and the RRH-BBU mapping problem in 5G CRAN has been addressed in a number of research works in the literature [6; 7; 8; 9]. However, focusing on the Quality-of-Service (QoS), [PERSON] et al. [6] developed a dynamic RRH-BBU mapping algorithm in CRAN architecture where the service provider's profit is not a focus of attention. By employing the news-vendor game model, a resource allocation problem with a bargaining solution is investigated in [9] with the ability to reconfigure its resources with varying traffic conditions. This scheme requires additional time for resource reconfiguration, which can cause violation of QoS requirements. 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. Motivated by the aforementioned discussions, we have developed a QoE and Profit-Aware optimal Resource Allocation scheme in a 5G Cloud Radio Access Network. The novelty of this work lies in formulating a multi-constraint resource allocation problem so as to meet the heterogeneous user QoS  
Figure 1: Fifth-generation (5G) Cloud Radio Access Network (CRAN) service architecture. BBU: baseband processing unit; RRH: remote radio head. requirements as well as to provide high profit to the operator. At first, weighted priority is calculated for each arrived task from RRHs to BBU pool so that the service requests can be scheduled according to their priorities and requirements.