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The massive MIMO approach presents an exciting prospect for the upcoming generation of wireless transmission systems. However, the adoption of actual massive MIMO scenarios is hindered by high hardware expenses and increased energy usage, particularly as the quantity of RF modules expands. To address this issue and make massive MIMO more commercially viable, the design of 1-bit analog-to-digital converters (ADCs) has been considered as a solution. Various deep learning (DL) techniques for channel estimation (CE) with 1-bit ADCs have been developed in the literature. Nonetheless, most of these methods demonstrate limited performance in CE regarding pilot lengths and noise levels. In this paper, an efficient DL model known as bi-directional long short-term memory (BiLSTM) is proposed. This model enhances CE performance with limited pilot signals by training on long input sequence data …