Joint Subcarrier and Power Allocation in MU OFDM DCSK Systems with Noise Reduction

This paper investigates the joint number of subcarriers and power optimization in Multi user Orthogonal Frequency Division Multiplexing based Differential Chaotic Shift Keying systems with noise reduction. We first find a closed-form expression for the optimal number of references in MU OFDM DCSK systems, while existing works only consider the numerical simulations. Moreover, this paper is the first to consider jointly subcarrier and power allocation in the MU OFDM DCSK system by defining a fractional problem. The BER expression of the MU OFDM DCSK system as an objective function is nonconvex and complex, which makes the analytical solution so difficult. We make it tractable by converting the fractional problem into a subtractive form, which leads to a near optimal solution. Simulation results show that our proposed approach provides better BER performance than the existing plans.

Deep Chaos Synchronization

In this study, we address the problem of chaotic synchronization over a noisy channel by introducing a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN). Conventional Deep Learning (DL) based communication strategies are extremely powerful but training on large data sets is usually a difficult and time-consuming procedure. To tackle this challenge, DCS does not require prior information or large data sets. In addition, we provide a novel Recurrent Neural Network (RNN)-based chaotic synchronization system for comparative analysis. The results show that the proposed DCS architecture is competitive with RNN-based synchronization in terms of robustness against noise, convergence, and training. Hence, with these features, the DCS scheme will open the door for a new class of modulator schemes and meet the robustness against noise, convergence, and training requirements of the Ultra Reliable Low Latency Communications (URLLC) and Industrial Internet of Things (IIoT).