5,532 research outputs found
Multi-Channel Deep Networks for Block-Based Image Compressive Sensing
Incorporating deep neural networks in image compressive sensing (CS) receives
intensive attentions recently. As deep network approaches learn the inverse
mapping directly from the CS measurements, a number of models have to be
trained, each of which corresponds to a sampling rate. This may potentially
degrade the performance of image CS, especially when multiple sampling rates
are assigned to different blocks within an image. In this paper, we develop a
multi-channel deep network for block-based image CS with performance
significantly exceeding the current state-of-the-art methods. The significant
performance improvement of the model is attributed to block-based sampling
rates allocation and model-level removal of blocking artifacts. Specifically,
the image blocks with a variety of sampling rates can be reconstructed in a
single model by exploiting inter-block correlation. At the same time, the
initially reconstructed blocks are reassembled into a full image to remove
blocking artifacts within the network by unrolling a hand-designed block-based
CS algorithm. Experimental results demonstrate that the proposed method
outperforms the state-of-the-art CS methods by a large margin in terms of
objective metrics, PSNR, SSIM, and subjective visual quality.Comment: 12 pages, 8 figure
Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems
Enabling highly-mobile millimeter wave (mmWave) systems is challenging
because of the huge training overhead associated with acquiring the channel
knowledge or designing the narrow beams. Current mmWave beam training and
channel estimation techniques do not normally make use of the prior beam
training or channel estimation observations. Intuitively, though, the channel
matrices are functions of the various elements of the environment. Learning
these functions can dramatically reduce the training overhead needed to obtain
the channel knowledge. In this paper, a novel solution that exploits machine
learning tools, namely conditional generative adversarial networks (GAN), is
developed to learn these functions between the environment and the channel
covariance matrices. More specifically, the proposed machine learning model
treats the covariance matrices as 2D images and learns the mapping function
relating the uplink received pilots, which act as RF signatures of the
environment, and these images. Simulation results show that the developed
strategy efficiently predicts the covariance matrices of the large-dimensional
mmWave channels with negligible training overhead.Comment: to appear in Asilomar Conference on Signals, Systems, and Computers,
Oct. 201
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