An Enhanced Multi-User Differential Chaos Shift Keying System With Intelligent Sparse Code Multiple Access

In this paper, we propose a deep learning-based sparse code multiple access multi-user multi-carrier differential chaos shift keying (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DL-SCMA-MU-MC-DCSK</i>) system, for the sake of improving the spectrum efficiency (SE) and bit-to-error (BER) performance. In the proposed system, the transmitted symbols of each user are mapped to complex codewords which are randomly generated from a complex normal distribution, and the codewords overlap on sub-carriers in a non-orthogonal way for the sparsity. Subsequently, the real and imaginary parts of the resultant complex codewords are modulated by the chaotic signal and its Hilbert-transform version, respectively. At the receiver, after correlation demodulation, a deep learning-based decorder consisting of deep neural network (DNN) is adopted to recover the transmitted data. We also compare the SE, energy efficiency (EE), and complexity with benchmark systems. Simulation results demonstrate the superiority of the proposed system in terms of bit-error-rate (BER) performance. Therefore, the proposed DL-SCMA-MU-MC-DCSK system represents a remarkable solution for low-power and cost-effective short-range wireless communication.