## 2 Related Works  
The radio resource management and allocation methods in 5G networks with SDN/NFV-based functions were presented and studied in many research papers [8; 9; 11]. Several research efforts have introduced and analyzed the radio resource control and QoS provisions for 5G network slices [10; 12; 13; 14; 15; 16]. The resource allocation strategies and challenges for network slicing were introduced and discussed in some recent works [8; 9; 11]. A survey of principles and a model for network slicing resource management were discussed in [8]. In [9], the problem of sharing 5G resources between different network slices in a fair manner was studied. In [11], network slicing in virtualized wireless networks was studied. Furthermore, a resource allocation algorithm was proposed for orthogonal frequency division multiple access (OFDMA) virtual RAN that can enhance the spectral efficiency of eMBB and uRLLC slices and improve uRLLC reliability. In [12], three scheduling schemes for SDN-based network slicing were proposed to enhance the joint resource management problem with different QoS requirements. In [13], in order to achieve resource fairness and high utilization in the 5G network, packet delay modeling was proposed for the traffic flow through the chain of virtual network functions (VNFs). In [14], a 5G network slicing framework was presented in order to enable efficient isolation between the coexistence of IoT and eMBB slices. In [10], a radio resource allocation method for network slicing is discussed. Specifically, a dynamic network resource allocation model for various 5G slices across different 5G network domains was suggested. As there are different requirements for each 5G slice in C-RAN, the proposed resource allocation aims to increase the level of satisfaction for each traffic slice in C-RAN and in all 5G network segments. In [15], the authors defined a new slicing architecture for the 5G network, as they considered it the main task to make 5G easier to understand. In addition, they presented a model for managing the constraints due to spectrum sharing by different access networks. In [16], the end-to-end behavior of slice isolation is examined, especially from a security perspective. Isolations were classified as traffic, bandwidth, processing, or storage. A set of challenges for the recent trends in 5G slice isolation was discussed. The authors argued that isolation is the most important property of network slicing. The authors of [6; 17] provided a model that can work with different types of data. From this point on, different data types require a traffic scheduling technique, as this would make the QoS in 5G mobile networks easier to manage. The techniques they have suggested are priority queuing, first-in-first-out (FIFO), and weighted fair queuing. In [17], a queue model was assumed to interact with two packet types (real-time and non-real-time). Each packet type has its own transmission time, during which a scheduler allocates the packet to a resource unit (BBU). The queue model assigns a higher priority to real-time packets, and the higher priority will pass to the queue model via frequency feedback for packet transmission. In [18], an efficient and secure service-oriented authentication framework that could support both network slicing and fog computing for the 5G-enabled Internet of Things network is proposed. In [19], a dynamic network slicing framework is presented in which a regional orchestrator is introduced to coordinate workload distribution among local fog nodes. In this node, the number of resources allocated to each slice could be dynamically adjusted according to service requests and energy availability. In [20], the authors proposed a network slicing architecture that utilizes spectrum resources in both licensed and unlicensed bands. In [21], an optimization framework is proposed that could jointly allocate resources for slices in terms of both network bandwidth and cloud processing power at a fine-grained level.