Liang Guo

TL;DR: A distributed learning-based AMC (DistAMC) method is proposed, which relies on the cooperation of multiple edge devices and model averaging (MA) algorithm, which has two advantages: the higher training efficiency and the lower computing overhead, which are very consistent with the characteristics of edge devices.

TL;DR: This paper proposes an adaptive deep learning aided digital predistortion model by optimizing a deep regression neural network and makes the linearization architecture more adaptive by using multiple sub-DPD modules and an ensemble predicting process.

TL;DR: A grouping-based conflict detection algorithm based on the preprocessed ADS-B data set is proposed, and the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds.

TL;DR: A deep learning based robust AMC (RAMC) method to effectively suppress the Doppler shift and achieve classification performance and simulation results show that the proposed CRFN-CSS method achieves the best performance and brings better performance than either CNN or SRU in 100 Hz Dopplers shift.

TL;DR: In this article, a federated learning-based user activity analysis (FedeUAA) method was proposed for reducing the risk of data leakage in mobile wireless networks, which has no requirement to upload data to cloud server, while it directly trains the DL models in local devices, and only needs to upload the knowledge (model weight or model gradient) rather than data.