Estimation of Signal Parameters using Deep Convolutional Neural Networks

This paper introduces a Deep Learning approach for signal parameter estimation in the context of wireless channel modeling. Our work is capable of multidimensional parameter estimation from a signal containing an unknown number of paths. The signal parameters are estimated relative to a predefined grid, providing quasi grid-free, hence, more accurate estimates than previous grid-limited approaches. It requires no prior knowledge of the number of paths, giving it an advantage in terms of complexity compared to existing solutions. Along with the description, we provide an initial performance analysis and a comparison with State-of-the-Art techniques and discuss future research directions.