Channel covariance matrix (CCM) is one critical parameter for designing the
communications systems. In this paper, a novel framework of the deep learning
(DL) based CCM estimation is proposed that exploits the perception of the
transmission environment without any channel sample or the pilot signals.
Specifically, as CCM is affected by the user's movement, we design a deep
neural network (DNN) to predict CCM from user location and user speed, and the
corresponding estimation method is named as ULCCME. A location denoising method
is further developed to reduce the positioning error and improve the robustness
of ULCCME. For cases when user location information is not available, we
propose an interesting way that uses the environmental 3D images to predict the
CCM, and the corresponding estimation method is named as SICCME. Simulation
results show that both the proposed methods are effective and will benefit the
subsequent channel estimation.Comment: 30 pages, 18 figure