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We propose a novel deep neural network, coined DeepFPC-$\ell_2$, for solvingthe 1-bit compressed sensing problem. The network is designed by unfolding theiterations of the fixed-point continuation (FPC) algorithm with one-sided$\ell_2$-norm (FPC-$\ell_2$). The DeepFPC-$\ell_2$ method shows higher signalreconstruction accuracy and convergence speed than the traditional FPC-$\ell_2$algorithm. Furthermore, we compare its robustness to noise with the previouslyproposed DeepFPC network---which stemmed from unfolding the FPC-$\ell_1$algorithm---for different signal to noise ratio (SNR) and sign-flipped ratio(flip ratio) scenarios. We show that the proposed network has better noiseimmunity than the previous DeepFPC method. This result indicates that therobustness of a deep-unfolded neural network is related with that of thealgorithm it stems from.

A Robust Deep Unfolded Network for Sparse Signal Recovery from Noisy Binary Measurements