Finite-Alphabet Signature Design for Grant-Free NOMA using Quantized Deep Learning

Grant-free Non-Orthogonal Multiple Access (NOMA) techniques are able to reduce the signaling overhead and the transmission latency in multi-user communications system. However, most of the existing code-domain grant-free NOMA schemes reuse the spreading signatures designed for the grant-based scenarios. Considering the sparsity and randomness nature of user activities in the uplink transmissions, we propose a deep learning-based signature design, where the non-equal user activation probabilities are exploited to optimize the code-domain NOMA signature. In addition, the conventional grant-free NOMA signatures are not specifically designed over finite Galois field, which hinders the implementation of the encoder/decoder using practical hardware. To address these challenges, we utilize the quantized deep learning framework for the NOMA signature training, which jointly optimizes the sequence generation and the quantization. The numerical results reveal that the obtained signatures outperform the conventional ones especially when the users has unequal activation probabilities.