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

TL;DR

This paper introduces a deep learning framework for joint activity detection and channel estimation in 6G IoT networks, reducing complexity and pilot length requirements through feature learning and adaptive tuning.

Contribution

It proposes a novel deep learning architecture with adaptive tuning for efficient JADCE, combining EM and back-propagation for improved performance in massive IoT device detection.

Findings

Low computational complexity demonstrated

Requires shorter pilot sequences

Effective in massive device scenarios

Abstract

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.