Automatic modulation recognition of sub-Nyquist spectrum sensing is essential to demodulate and process the signals in a spectrum- and energy-efficient Internet of Things system. Motivated by the recent advances of deep learning, a promising direction is to automatically predict the modulation based on data-driven representation instead of hand-crafted features. Specifically, our solution SHELL (Simple solution witH ELegant detaiLs) provides a simple yet effective approach, which is capable of achieving high modulation recognition accuracy without complex network structure.