# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q2. What Is the Importance of Resource Allocation in 5G?
In [144], resource allocation for multi-users in a 5G massive-MIMO (mMIMO) was executed through a deep neural network (DNN). In the first phase, the unbiased functions were enhanced through the Multi-objective Sine Cosine algorithm (MOSCA). The unbiased functions that were observed through the optimization method were energy efficiency (EE), power consumption, signal-to-interference-and-noise ratio (SINR), and dataset. In next phase, these unbiased functions were assigned to a neural network for the allocation of resources. The DNN recognized the level of requirement for all users. Depending on this level status, resources were allocated to every user by maintaining EE and high throughput. Moreover, the fairness level of the neural network-based resource allocation process was also recognized. The researchers of [145] proposed a scheme for categorizing resource allocation into two main parts named as resource allocation and medium access. The medium access influences the transmission nature of the wireless signal and MTC devices' wait time to allocate priorities utilizing capillary band in an integral way. Meanwhile, in resource allocation, SNR, whole induced transmission-awaiting, and transmission delay MTC devices were considered to allocate resources in the cellular band. The reflection of two-staged dynamic priorities in the proposed scheme brought significant performance enhancement in outage and success probabilities. The researchers in [146] proposed a scheme to minimize interference for 5G cellular users (Sus) that was focused on interference threshold, minimal transmission rate, available power, and quality of service (QoS). At first, the mandatory least transmission power by the V2X users (VUs) was assigned as the initial power value. Next, the Hungarian algorithm was performed to acquire a suitable subchannel. Finally, an approach for optimization was presented to the power allocation. The findings revealed by simulation illustrated that the presented method confirmed the smallest transmission rate of VUs, and it enhanced the CUs' channel capacity while ensuring the QoS of the CUs. The authors in [147] addressed the dynamic latency-aware resource allocation problem in multi-tenant 5G slice networks, a multi-tier heterogeneous environment, for efficient radio resource management. The problem was expressed as a higher utility optimization problem. The optimization problem was altered, and a classified decomposition method was implemented to decrease the complexities while solving the optimization problem. Additionally, the authors proposed a genetic algorithm (GA) intelligent latency-aware resource allocation scheme (GI-LARE). Authors compared GI-LARE with static slicing (SS) resource allocation, the bound-based scheme and spatial branch, and an optimal resource allocation algorithm. The results revealed that GI-LARE outperformed the other mentioned schemes. As per [148], the authors proposed a centralized low-complexity packet scheduling scheme to provide URLLC QoS services. Progressive 5G NR system-level results were discussed to evaluate the effectiveness of the presented scheme. It was observed that unified architecture improved URLLC latency. Compared with effective point selection and scattered scheduling for dynamic spectral, the presented scheme attained a 99% and 90% reduction in latency for URLLC, respectively. The authors of [149] developed an arrangement technique inside the IoT traffic to offer end-to-end QoS in an NB-IoT network. They established a process for handling a smart queue based on the IoT traffic arranging processes. Through the many simulations, they verified that the developed method guaranteed high E2E QoS of the present traffic. This was accomplished through decreasing an average E2E communication delay of the real-time messages. The authors in [150] obtained their outcome in two phases.