Mi Yang

TL;DR: It is pointed out that a widely-used assumption, that wireless channels can be considered to be sparse, has pitfalls and a sparse channel estimator cannot guarantee stable estimation accuracy even in channels with a high degree of sparsity, and considerable performance degradation will occur if a channel changes to non-sparse.

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 cluster-based three-dimensional (3D) channel model is proposed in this paper, which is based on the measurements conducted at 5.9 GHz in urban and suburban scenarios and found that both the azimuth spread and the elevation spread follow the lognormal distribution.

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.