A Neural Network-Aided Detection Scheme for Index-Modulation DCSK System
Yi Fang, Dongyang Peng, Huan Ma, Guojun Han, Yonghui Li
Abstract
The accuracy of index-bit detection greatly affects the overall bit-error-rate (BER) performance of index modulation aided differential chaos shift keying (IM-DCSK). To improve the BER performance of index bits, a novel neural network (NN)-aided IM-DCSK detection scheme, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NN-IM-DCSK</i> detection scheme, is proposed in this article. The proposed scheme can be applied to various IM-DCSK systems, such as carrier index DCSK, pulse position modulation DCSK, and code index modulation DCSK systems. The NN-IM-DCSK detection scheme uses a two-layer long short-term memory unit and multiple fully connected layers to extract the features and the correlation of IM-DCSK signals. By estimating the index bits from the configured NN framework, the scheme can make correct correlation to retrieve modulated bits. Therefore, the NN-IM-DCSK detection scheme that benefits from the advantage of both the NN and traditional energy detection can improve transmission reliability. The complexity of the NN-IM-DCSK system is analyzed. Simulation results show that the proposed detection scheme can achieve better BER performance than the traditional detection scheme, such as the energy detection scheme, in IM-DCSK systems over multipath Rayleigh fading channels.