Deep Learning Aided Grant-Free NOMA Toward Reliable Low-Latency Access in Tactile Internet of Things

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 5; pp. 2995 - 3005
• Main Authors: Ye, Neng, Li, Xiangming, Yu, Hanxiao, Wang, Aihua, Liu, Wenjia, Hou, Xiaolin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activation; Artificial neural networks; Computer simulation; Deep learning; Deep learning (DL); fifth generation (5G); grant-free; Informatics; Internet of Things; Neural networks; NOMA; Nonorthogonal multiple access; nonorthogonal multiple access (NOMA); Optimization; Reliability; Spreading; Tactile; tactile Internet of Things (IoT); Training; variational autoencoding
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2895086

Tactile Internet of Things (IoT) requires ultraresponsive and ultrareliable connections for massive IoT devices. As a promising enabler of tactile IoT, grant-free nonorthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and nonorthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. Hence, we formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we resort to deep learning by parameterizing the intractable variational function with a specially designed deep neural network, which incorporates random user activation and symbol spreading. The network is trained according to a novel multiloss function where a confidence penalty based on the user activation probability is considered. The spreading signatures are automatically generated while training, which matches the highly automatic applications in tactile IoT. The significant reliability gain of our scheme is validated by simulations.