Zhangfeng Ma

TL;DR: A three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed for air-to-air (A2A) channels in UAV communication scenarios and the 3D Markov mobility model is used to characterize the movements of the UAV in both horizontal and vertical directions.

TL;DR: A three-dimensional (3D) wideband non-stationary geometry-based stochastic model (GBSM) is proposed for UAV multiple-input multiple-output (MIMO) channels and it is found that, even for a low range of UAV rotations, channel correlations are significantly affected, and the time correlation gradually increases with the pitch angle.

TL;DR: A geometric three-dimensional non-stationary channel model operating at millimeter-wave (mmWave) band is proposed for wideband UAV multiple-input multiple-output (MIMO) communications based on a multiple-layer cylinder reference model, where both stationary and moving clusters around transmitter (Tx) and receiver (Rx) are considered.

TL;DR: In this paper, a machine-learning-based real-time AOA recognition approach is proposed, which includes off-line training and on-line estimation processes, and an estimation model is obtained by using the support vector machine (SVM) based on a large number of actual measurement data.

TL;DR: Based on the measured data, a cluster-based dynamic V2V channel model is proposed for OLOS scenarios and the influences of vehicle obstructions on path loss, delay and angle dispersion are intuitively embodied as changes in the statistical distribution of MPCs clusters.