Resource allocation in downlink SWIPT-based cooperative NOMA systems

Abstract

This paper considers a downlink multi-carrier cooperative non-orthogonal multiple access (NOMA) transmission, where no direct link exists between the far user and the base station (BS), and the communication between them only relies on the assist of the near user. Firstly, the BS sends a superimposed signal of the far and the near user to the near user, and then the near user adopts simultaneous wireless information and power transfer (SWIPT) to split the received superimposed signal into two portions for energy harvesting and information decoding respectively. Afterwards, the near user forwards the signal of the far user by utilizing the harvested energy. A minimum data is required to ensure the quality of service (QoS) of the far user. We jointly optimize power allocation, subcarrier allocation, time allocation, the power allocation (PA) coefficient and the power splitting (PS) ratio to maximize the number of data bits received at the near user under the energy causality constraint, the minimum data constraint and the transmission power constraint. The block-coordinate descent method and the Lagrange duality method are used to obtain a suboptimal solution of this optimization problem. In the final simulation results, the superiority of the proposed NOMA scheme is confirmed compared with the benchmark NOMA schemes and the orthogonal multiple access (OMA) scheme.

Centralized Machine Learning Versus Federated Averaging: A Comparison using MNIST Dataset

“…However, many important data types are rigorously shared using centralized storage, including hospital data (patient information), government data, bank data, and more. Therefore, federated learning has attracted attention in fields that must prioritize the preservation of privacy such as those involving the 5G network and the Internet of Things (IoT) and in the communication and networking fields, in particular, to optimize resource allocation in communication rounds [4]. Further, federated learning has attracted increasing attention in research fields because its algorithm can be trained without the data owners pushing their data to the cloud server.…”