A Neural-Network-Assisted Chaos-NOMA Scheme

We propose a non-orthogonal multiple access (NOMA) scheme based on three-dimensional chaotic attractors, which we denote chaos-NOMA. In this system, the chaotic signals transmitted by each user are generated from the state variables of the attractor. We employ a principal component analysis algorithm for dimensionality reduction and obtain an orthonormal basis and a signal constellation for the chaotic signals. The dynamical properties of these signals result in the transmission of time-varying waveforms, modeled as an intrinsic noise. Depending on factors such as the number of users and the power difference of the transmitted signals, this intrinsic noise can lead to a performance curve exhibiting an error floor. We propose a neural network architecture coupled to the demodulator to mitigate the impact of the intrinsic noise.