Deep Learning-based Codebook Design for Code-domain Non-Orthogonal Multiple Access Approaching Single-User Bit Error Rate Performance

A general form of codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered equivalent to a constellation design for multi-user multi-dimensional modulation (MU-MDM). Motivated by recent works on deep learning (DL)-based design of MDM, we propose new architectures for autoencoder and the underlying training methodology that aim at joint optimization of resource mapping and a constellation design with bit-to-symbol mapping, hopefully approaching a bit error rate (BER) performance of the equivalent single-user MDM (SU-MDM) model. The novelty and contribution of the paper lies in the proposed architectures of autoencoder and the underlying training framework for optimum codeword design for CD-NOMA, which without knowing other-user symbols, approaches the performance of SU-MDM with the same spectral efficiency. It includes the trainable architectures of DL-based designs for SU-MDM and MU-MDM, which can be equivalently compared to each other. They are implemented to demonstrate that the proposed design for MU-MDM can achieve the BER performance of DL-based single-user codebook design within 0.3 dB in the additive white Gaussian noise channel, thus serving as the best existing design that can be realized with its low-complex decoder.