5 research outputs found
FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems
Channel estimation is of crucial importance in massive multiple-input
multiple-output (m-MIMO) visible light communication (VLC) systems. In order to
tackle this problem, a fast and flexible denoising convolutional neural network
(FFDNet)-based channel estimation scheme for m-MIMO VLC systems was proposed.
The channel matrix of the m-MIMO VLC channel is identified as a two-dimensional
natural image since the channel has the characteristic of sparsity. A deep
learning-enabled image denoising network FFDNet is exploited to learn from a
large number of training data and to estimate the m-MIMO VLC channel.
Simulation results demonstrate that our proposed channel estimation based on
the FFDNet significantly outperforms the benchmark scheme based on minimum mean
square error.Comment: This paper will be published in IEEE WC
Deep Learning for Spectrum Sensing in Cognitive Radio
The detection of primary user signals is essential for optimum utilization of a spectrum by secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have the problem of missed detection/false alarm, which hampers the proper utilization of spectrum. Spectrum sensing through deep learning minimizes the margin of error in the detection of the free spectrum. This research provides an insight into using a deep neural network for spectrum sensing. A deep learning based model, “DLSenseNet”, is proposed, which exploits structural information of received modulated signals for spectrum sensing. The experiments were performed using RadioML2016.10b dataset and the outcome was studied. It was found that “DLSenseNet” provides better spectrum detection than other sensing models