Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks

@article{Zou2021JointUA,
  title={Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks},
  author={Yixuan Zou and Zhijin Qin and Yuanwei Liu},
  journal={ICC 2021 - IEEE International Conference on Communications},
  year={2021},
  pages={1-6}
}
Grant-free non-orthogonal multiple access (NOMA) is considered as one of the supporting technology for massive connectivity for future networks. In the grant-free NOMA systems with a massive number of users, user activity detection is of great importance. Existing multi-user detection (MUD) techniques rely on complicated update steps which may cause latency in signal detection. In this paper, we propose a generative neural network-based MUD (GenMUD) framework to utilize low-complexity neural… 

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