An Intelligent Detector for Carrier Index Differential Chaos Shift Keying System

Inspired by the orthogonal time frequency space modulation, carrier index differential chaos shift keying (CI-DCSK) system has been proposed and attracted more and more attention because of its high-efficiency and low-complexity advantages. In this paper, a deep learning based intelligent detector for CI-DCSK system, referred to as DL-CI-DCSK detector, is proposed to realize more reliable transmission. The proposed detector inherits the advantages of neural network and traditional energy detection. The proposed DL-CI-DCSK detector first recovers the index bits using a neural network and then using the index bits to demodulate the modulation bits. The designed network structure mainly exploits the characteristics of long short-term memory unit and multiple fully connected layers to extract and integrate the correlation and features of the modulated signals. Simulation results show that the proposed intelligent detector can achieve better bit-error-rate (BER) performance than conventional detectors over multipath Rayleigh fading channels.