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We present DeepFPC, a novel deep neural network designed by unfolding theiterations of the fixed-point continuation algorithm with one-sided l1-norm(FPC-l1), which has been proposed for solving the 1-bit compressed sensingproblem. The network architecture resembles that of deep residual learning andincorporates prior knowledge about the signal structure (i.e., sparsity),thereby offering interpretability by design. Once DeepFPC is properly trained,a sparse signal can be recovered fast and accurately from quantizedmeasurements. The proposed model is evaluated in the task ofdirection-of-arrival (DOA) estimation and is shown to outperformstate-of-the-art algorithms, namely, the iterative FPC-l1 algorithm and the1-bit MUSIC method.

DeepFPC: Deep Unfolding of a Fixed-Point Continuation Algorithm for Sparse Signal Recovery from Quantized Measurements