Efficient Model-Free Learning to Overcome Hardware Nonidealities in Analog-to-Information Converters

Abstract

This paper considers compressed sensing (CS) in the context of RF spectrum sensing and presents an efficient approach for learning hardware nonidealities in an analog-to-information converter (A2IC). The proposed methodology is based on the learned iterative shrinkage-thresholding algorithm (LISTA), which enables co-optimization of the hardware and the reconstruction algorithm and leads to a model-free recovery approach that is optimally tuned for the unique computational constraints and hardware nonidealities present in the RF frontend. To achieve this, we devise a training protocol that employs a dataset and neural network of minimal sizes. We demonstrate the effectiveness of our methodology on simulated data from a model of a well-established CS A2IC in the presence of linear impairments and noise. The recovery process extrapolates from training on 1-sparse signals to recovering the support of signals whose sparsity runs up to the theoretical optimum for l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -based algorithms across a range of typical operating SNRs.