  With the increasing development of Internet of Things (IoT), the upcoming
sixth-generation (6G) wireless network is required to support grant-free random
access of a massive number of sporadic traffic devices. In particular, at the
beginning of each time slot, the base station (BS) performs joint activity
detection and channel estimation (JADCE) based on the received pilot sequences
sent from active devices. Due to the deployment of a large-scale antenna array
and the existence of a massive number of IoT devices, conventional JADCE
approaches usually have high computational complexity and need long pilot
sequences. To solve these challenges, this paper proposes a novel deep learning
framework for JADCE in 6G wireless networks, which contains a dimension
reduction module, a deep learning network module, an active device detection
module, and a channel estimation module. Then, prior-feature learning followed
by an adaptive-tuning strategy is proposed, where an inner network composed of
the Expectation-maximization (EM) and back-propagation is introduced to jointly
tune the precision and learn the distribution parameters of the device state
matrix. Finally, by designing the inner layer-by-layer and outer layer-by-layer
training method, a feature-aided adaptive-tuning deep learning network is
built. Both theoretical analysis and simulation results confirm that the
proposed deep learning framework has low computational complexity and needs
short pilot sequences in practical scenarios.
