An Intelligent Detection Based on Deep Learning for Multilevel Code Shifted Differential Chaos Shift Keying System With M-ary Modulation

Abstract

Multilevel code shifted M-ary differential chaos shift keying (MCS-MDCSK) system provides higher data rate chaotic information transmission by applying multilevel code shifting aided M-ary modulation. However, the real-valued chaotic sequences induce interferences to signals while higher-order modulation shortens the Euclidean distance between adjacent symbols, thereby leading to performance degradation. To improve the bit-error rate (BER) performances, we propose an intelligent detector to achieve the joint demodulation and de-spreading at the receiver. In this design, we construct the recursive long short-term memory (LSTM) unit to extract features from the correlated chaotic modulated signals. Then we concatenate the LSTM unit with multiple full connection layers (FCLs) and compose the deep neural network (DNN) to recover the information. Owing to the serial concatenated LSTM-aided DNN, the intelligent detector can learn the joint chaotic modulation and spreading pattern, and achieve the joint demodulation and de-spreading. Consequently, larger performance gain can be attained and the reliability performances will be improved. Simulation results validate the proposed design. Moreover, for practical systems undergoing multiplicative fading, the intelligent MCS-MDCSK detector exhibits better BER performances than the benchmark systems.