Changchuan Yin

TL;DR: In this article, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality of experience (QoE) of wireless devices in a cloud radio access network is studied.

TL;DR: In this paper, a joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm.

TL;DR: This paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks and overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems.

TL;DR: Simulation results show that the proposed joint federated learning and communication framework can improve the identification accuracy by up to 1.4%, 3.5% and 4.1%, respectively, compared to an optimal user selection algorithm with random resource allocation and a wireless optimization algorithm that minimizes the sum packet error rates of all users while being agnostic to the FL parameters.

TL;DR: In this article, the authors provide a comprehensive tutorial on the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications.