Haoran Qi

TL;DR: A blind joint sub-Nyquist sensing scheme is proposed by utilizing the surround IoT devices to jointly sample the spectrum based on the multicoset sampling theory and it is shown that the adaptive number of coset samplers could be adopted without causing the degradation of the detection performance and the number ofcoset sampler could be further reduced with the assists from geo-location database even when the obtained information is partially correct.

TL;DR: This work proposes and implements Artificial Intelligence (AI)-based Abnormality Detection techniques at the physical (PHY)-layer in CR enabled by learning Generative Models and shows that both of the proposed methods are capable of detecting abnormal signals in the spectrum and pave the road towards Self-Aware radio.

TL;DR: A deep neural network is learned and then used to predict a user's moving direction with up to 80 percent prediction accuracy in mmWave communication without the support of traditional channel estimation.

TL;DR: A statistical model of channel energy for CSS is postulated and a practical threshold adaption scheme is proposed aiming to maintain constant false alarm rates in channel energy detection to investigate the channel energy statistics of recovered spectrum.

TL;DR: A novel spectrum sparsity estimation scheme directly from sub-Nyquist measurements is proposed, with which the computational effort of greedy pursuit algorithms can be saved and recovery performance improved and the detection performance and complexity of the proposed CSS scheme show striking superiority against multiple benchmarking schemes.