# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q2. What Is the Importance of Resource Allocation in 5G?
By altering the feasible domain and variables, the main challenge was converted into the standard convex optimization form which could be addressed by the Lagrange duality method. The authors [138] proposed hybrid MC-NOMA systems based on a joint resource allocation scheme that was relevant to a generalized scenario having an identical subcarrier for various users that could be multiplexed. For each user, they investigated the fewest number of requirements that existed simultaneously in the system and that could provide significant influence on the OMA and NOMA selection. The hybrid MC-NOMA mode considerably beat both OMA and NOMA related to the EE-SE tradeoff, and it also displayed excessive potential to progress the tradeoff between user system efficiency and fairness. The researchers in [139] investigated energy efficient resource allocation in a 5G challenge having soft frequency reuse (SFR). The power allocation and RB assignment were optimized jointly under the umbrella of SFR. The Stackelberg game model was presented to acquire the maximum EE in the 5G network under inter-cell interference (ICI), and to limit the ICI, the interference pricing factors were utilized along with the authors provision of the NE point's existence. Due to non-convex object function, they used the Lagrange duality method of decomposition to achieve the ideal outcome for the power allocation challenge. By using several iterations, they achieved the maximum energy-efficient resource allocation for the 5G network. The authors of [140] aimed to support the maximum number of those who could access the system simultaneously; the authors proposed a virtual code resource allocation (VCRA) method that extended the code-expanded approach. Furthermore, they introduced the virtual resource allocation method to ensure energy-priority in the access technique. The main purpose was to elaborate the various access levels that meaningly split a cluster of access codewords, appropriately maintained to ensure maximum capability for every access level. The authors of [141] proposed matching slice architecture for resource allocation based on the idea of a self-organizing network. The running architecture initially shaped the processes and functions of the matching independent management of the resource. Based on the multidimensional statistics, an efficient deep learning model named LSTM (long short-term memory) was utilized to build the dynamic multicast service traffic model in space-time, which facilitated the base for more network resource allocation. By relying on the obtained results and satisfactory conditions of users changing requirements, the corresponding model was developed to minimize the RRHS energy usage and to maintain QoS as constraints. In [142], the authors proposed an innovative deep strengthening learning-based intellectual Time Division Duplex (TDD) configuration system to dynamically allocate radio online resources. They deployed a deep neural network to obtain the characteristics of complex network information, and the dynamic Q-value iteration-based strengthening learning along with an experience replay memory mechanism was presented to adaptively change the TDD Up/Down-link ratio based on estimated rewards. They obtained significant network performance enhancement with respect to both packet loss rate and network throughput. As per [143], the authors employed DRL to develop an optimal resource allocation and computation offloading scheme for reducing the energy consumption of the system. Initially, they discussed a multi-user end-edge-cloud composed system in which all base stations and devices had computation proficiencies. In the next step, they investigated the joint resource allocation and computation offloading problem as a Markov Decision Process (MDP) and presented a new DRL scheme to reduce the system's consumption of energy. The results obtained by using a practical dataset illustrated that the presented scheme provided excellent performance to achieve the required goal.