  We propose a novel deep neural network, coined DeepFPC-$\ell_2$, for solving
the 1-bit compressed sensing problem. The network is designed by unfolding the
iterations of the fixed-point continuation (FPC) algorithm with one-sided
$\ell_2$-norm (FPC-$\ell_2$). The DeepFPC-$\ell_2$ method shows higher signal
reconstruction accuracy and convergence speed than the traditional FPC-$\ell_2$
algorithm. Furthermore, we compare its robustness to noise with the previously
proposed 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 noise
immunity than the previous DeepFPC method. This result indicates that the
robustness of a deep-unfolded neural network is related with that of the
algorithm it stems from.
