Radio Resource Scheduling in 5G Networks Based on Adaptive Golden Eagle Optimization Enabled Deep Q-Net

Radio Resource Scheduling in 5G Networks Based on Adaptive Golden Eagle Optimization Enabled Deep Q-Net

연구 분야: Networking

학회: SN Computer Science

The growth of fifth-generation (5G) broadband wireless systems presents several issues in terms of network resource allocation. In a collaborative network of mobile devices, the users, and devices are struggled with precious resources. Consequently, it emphasizes the importance of fair and effective resource allocation for the optimum functioning of the networks. Hence, this research presents the Adaptive Golden Eagle optimization based Deep Q Net (Adaptive GEO_DQN) in radio resource scheduling of 5G networks. Moreover, the 5G network is made up of base stations (BS) and user equipment (UEs). The radio resource scheduler at the BS is active in every slot. The BS can collect the data from the UE, like channel feedback data, buffer, hybrid automatic repeat request (HARQs), and allocation log. The resource blocks (RBs) from the current resource blocks group (RBG) have been scheduled by UEs in the current slot. Furthermore, the DQN is used in the UE scheduling, and the Adaptive GEO is utilized in the training of DQN. In addition, the efficacy of the system is validated with respect to the throughput and fairness metrics with the outcomes of 0.921 Mbps, and 0.902 are attained.