An Opportunistic Energy Harvesting Scheme for Tactile NOMA-Based D2D Users Using Federated Learning

The Internet of Things (IoT) has emerged as an innovative paradigm that interconnects a diverse array of devices and systems to enable uninterrupted communication and data transmission. Within this extensive architecture, device-to-device (D2D) communication is critical for facilitating direct interactions among linked devices. This aspect of IoT surpasses traditional modalities of engagement between humans and machines. It cultivates an interactive ecosystem wherein devices autonomously collaborate, share data, and perform designated functions. D2D communication offers a multitude of benefits, such as enabling short-range interactions, minimizing latency, enhancing scalability, and optimizing energy efficiency. Moreover, to promote widespread connectivity and exceptionally dependable low-latency performance within the fifth generation (5G) network, the adoption of non-orthogonal multiple access (NOMA) merits further exploration. In this regard, the impact of federated learning on NOMA-based D2D group users (DGUs) within wireless-powered communication frameworks is examined. Initially, the D2D transmitters (DDTs) harvest energy from the radio frequency signals emitted by the base station. Subsequently, the DDTs employ NOMA to establish communication with the D2D receivers (DDRs) by utilizing the energy they have accumulated. A stochastic optimization problem is formulated to enhance energy efficiency (EE) and minimize delay. This formulation incorporates both stochastic traffic arrivals and the time-varying conditions of the communication channel. By applying the Markov decision process, the non-convex optimization problem is transformed into a mathematical model that encapsulates decision-making scenarios. Furthermore, federated learning is implemented to achieve the objectives and accelerate the dissemination of local training data across the DGUs. Empirical results illustrate that the proposed methodology achieves performance metrics that are 8.47% and 66.10% superior to those of distributed and centralized schemes, respectively.