  Direction of arrival (DOA) estimation employing low-resolution
analog-to-digital convertors (ADCs) has emerged as a challenging and intriguing
problem, particularly with the rise in popularity of large-scale arrays. The
substantial quantization distortion complicates the extraction of signal and
noise subspaces from the quantized data. To address this issue, this paper
introduces a novel approach that leverages the Transformer model to aid the
subspace estimation. In this model, multiple snapshots are processed in
parallel, enabling the capture of global correlations that span them. The
learned subspace empowers us to construct the MUSIC spectrum and perform
gridless DOA estimation using a neural network-based peak finder. Additionally,
the acquired subspace encodes the vital information of model order, allowing us
to determine the exact number of sources. These integrated components form a
unified algorithmic framework referred to as TransMUSIC. Numerical results
demonstrate the superiority of the TransMUSIC algorithm, even when dealing with
one-bit quantized data. The results highlight the potential of
Transformer-based techniques in DOA estimation.
