# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q2. What Is the Importance of Resource Allocation in 5G?
According to [106], the authors presented a novel cross-layer infrastructure for resource allocation and downlink scheduling that was able to exemplify the characteristics of two of the most favorable waveform technologies with respect to 5G networks--namely, orthogonal frequency division multiple (OFDM) and filterbank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM). Unlike the comparative analysis discussed in previous literature, the publication of these two abovementioned technologies were highly centered on PHY metrics. The presented work reveals the extraction of user-relevant metrics such as average delay, throughput, service coverage, and Jain's fairness index in a multi-class traffic environment. In [107], the authors emphasized relay selection and downlink resource allocation, where a user is coupled through a multi-hop relay to a base station while considering various relay stations for the purpose of selecting one that is in his range. To tackle the supplementary issues presented by multi-hop relay nodes, they proposed a scheme for dynamic resource allocation and the choice of relay. A mathematical analysis was portrayed to illustrate the validity of the presented scheme. The authors of [108] presented a small-complexity technique based on subgroups that attained maximum performance. The outcome was appropriate for employment in hands-on systems, such as Satellite Long-Term Evolution (S-LTE), as the computational cost did not depend on the number of available resources and the multicast group size. The effectiveness of the presented technique was examined through simulations performed in various multicast group and radio transmission environments. In [109], the authors presented an end-to-end slicing that acted as in the capacity of providing a computing and communication resources system over the entire 2-tier architecture of edge computing having multi-access. This system was deployed by utilizing open-source tools. It was observed that the presented framework significantly incorporated the resources of 5G, which were either communication or computing among slices and guaranteed that the implemented slices' resources were more efficient in achieving the tenant's latency requirements. Furthermore, the experiments revealed that mMTC and URLLC services needed a supplementary 70% of the required communication and computing resources delivered by the RAN and edge to comply with their stricter latency requirements. As per [110], the authors introduced a unique resource allocation scheme that was named a threshold-controlled access (TCA) protocol, in which an uplink resource allocation scheme through which the device itself made decisions to allocate blocks of resources depending on the relevant application's power profile and battery status ultimately led to attaining a favorable QoS metric. At first, the presented TCA scheme chose multiple carriers for the purpose of allocating resources to a specific node for improving the life of MTC devices having reduced consuming power. Subsequently, the well-organized solution was deployed by pursuing a threshold value. The specific value was considered to select via plotting the QoS metric. The threshold improved the subcarrier selection for reduced-power devices such as small e-health sensors. In [111], the authors presented a communication framework to subtenant the implementation of a CPIoTS having a central controller. Using this framework, various actuators and sensors could create communication links in full duplex mode along with the main controller. To deal with the available band signal data, the problem of resource allocation was considered as non-convex mixed integer programming problem, focusing to enhance the CPIoTS total energy efficiency. By proposing the alteration, they divided the issue of resource allocation among channel and power allocations. Furthermore, the authors considered an energy-efficient allocation of power scheme depending on game theory and [PERSON]'s algorithm.