FL-ORA: Optimized and Decentralized Resource Allocation Scheme for D2D Communication

This article presents an optimized and decentralized resource allocation approach aimed at maximizing the system throughput and energy efficiency of device-to-device (D2D) communication. The proposed scheme modifies the meta-heuristic whale optimization algorithm (WOA) by blending the differential evolution (DE) technique in the WOA’s exploration phase to offer intelligence and reduce the computational overburden. The hybrid WOA (DE+WOA) serves as a physical layer access control that efficiently finds the optimal cellular users (CUs) and D2D users (DUs) based on their channel conditions. The proposed access control acts as a restrictive filter, where only optimal CU-DUs can participate in resource allocation tasks. Furthermore, a dataset has been prepared using the optimal CUs-DUs channel conditions from the hybrid WOA to serve as input for the federated learning (FL)-based resource allocation. We utilized statistical tests (e.g., Spearman’s test) to analyze the generated dataset’s non-independent and identically distributed (non-IID) characteristics, thus providing generalization in the AI training. Allowing only the optimal CUs and DUs (from hybrid WOA) in the FL-based resource allocation substantially reduces the computational cost of AI training and improves energy efficiency. In the FL-based resource allocation, we used a sequential convolutional neural network (CNN) trained on the aforementioned dataset to provide proactive resource allocation decisions. Furthermore, we used momentum-based weight aggregation in the FL to reduce the computational burden on the central server. The proposed scheme is assessed by utilizing different standard metrics, such as training accuracy (98.93%), training time, overall system throughput (35.62 Mbps), energy efficiency (96.42 bits/joule), and resource fairness.