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With the increasing development of Internet of Things (IoT), the upcomingsixth-generation (6G) wireless network is required to support grant-free randomaccess of a massive number of sporadic traffic devices. In particular, at thebeginning of each time slot, the base station (BS) performs joint activitydetection and channel estimation (JADCE) based on the received pilot sequencessent from active devices. Due to the deployment of a large-scale antenna arrayand the existence of a massive number of IoT devices, conventional JADCEapproaches usually have high computational complexity and need long pilotsequences. To solve these challenges, this paper proposes a novel deep learningframework for JADCE in 6G wireless networks, which contains a dimensionreduction module, a deep learning network module, an active device detectionmodule, and a channel estimation module. Then, prior-feature learning followedby an adaptive-tuning strategy is proposed, where an inner network composed ofthe Expectation-maximization (EM) and back-propagation is introduced to jointlytune the precision and learn the distribution parameters of the device statematrix. Finally, by designing the inner layer-by-layer and outer layer-by-layertraining method, a feature-aided adaptive-tuning deep learning network isbuilt. Both theoretical analysis and simulation results confirm that theproposed deep learning framework has low computational complexity and needsshort pilot sequences in practical scenarios.

Feature-Aided Adaptive-Tuning Deep Learning for Massive Device Detection