This letter provides a deep learning framework for massive grant-free random access in 6G cellular internet of things (IoT) networks. A model-driven deep learning algorithm for joint activity detection and channel estimation is proposed based on the principle of approximate massage passing (AMP). This algorithm only needs to learn four key parameters, but not the whole algorithm architecture. More importantly, it does not require the prior information about active probabilities and channel variance, and can significantly improve the performance with a finite number of training data. Simulation results validate the effectiveness of the proposed deep learning algorithm.

中文翻译：

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用于联合活动检测和信道估计的模型驱动深度学习算法

这封信为 6G 蜂窝物联网 (IoT) 网络中的大规模免授权随机访问提供了一个深度学习框架。基于近似按摩传递（AMP）原理，提出了一种用于关节活动检测和信道估计的模型驱动的深度学习算法。该算法只需要学习四个关键参数，而不是整个算法架构。更重要的是，它不需要关于活动概率和信道方差的先验信息，并且可以在有限数量的训练数据下显着提高性能。仿真结果验证了所提出的深度学习算法的有效性。