Tactile Internet of Things (IoT) requires ultra responsive and ultra-reliable connections for massive IoT devices.As a promising enabler of Tactile IoT, grant-free non-orthogonalmultiple access (NOMA) exploits the joint benefit of grant-free access and non-orthogonal transmissions to achieve low latencymassive access. However, it suffers from the reduced reliabilitycaused by random interference. Hence, we formulate a variationaloptimization 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 in corporates random user activation and symbol spreading. The network is trained according to a novel multi-loss function wherea 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.
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