Intelligent and Reliable Deep Learning LSTM Neural Networks-Based OFDM-DCSK Demodulation Design
Intelligent and Reliable Deep Learning LSTM Neural Networks-Based OFDM-DCSK Demodulation Design
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DOI：
10.1109/tvt.2020.3022043
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发表时间：
2020-12-01
影响因子：
6.8
通讯作者：
Wu, Zhiqiang
中科院分区：
文献类型：
--
作者：
Zhang, Lin;Zhang, Haotian;Wu, Zhiqiang
混沌通信利用混沌系统的不规则特性，在保密、抗干扰等方面得到了广泛的应用。然而，施加在信息上的实值混沌序列会对用户数据产生干扰，从而导致可靠性性能下降。为了解决这个问题，在本文中，我们提出利用深度神经网络（DNN）的智能和特征提取能力来学习传输模式以解调接收到的信号。在我们的设计中，我们提出为正交频分复用辅助差分混沌键控（OFDM-DCSK）系统构建长短期记忆（LSTM）单元辅助的智能DNN深度学习（DL）解调器。经过学习，并在训练阶段提取的信息承载混沌传输的特征，接收信号可以有效地恢复，并可靠地在部署阶段。由于递归LSTM辅助DL设计，可以利用信息承载混沌调制信号之间的相关性来提高可靠性性能。仿真结果表明，在加性白色高斯噪声（AWGN）信道和衰落信道下，与基准系统相比，智能OFDM-DCSK系统能够获得更可靠的性能。
Chaos communications have widely been applied to provide secure, and anti-jamming transmissions by exploiting the irregular chaotic behavior. However, the real-valued chaotic sequences imposed on the information induce interferences to the user data, thereby leading to reliability performance degradations. To address this issue, in this paper, we propose to utilize the intelligent, and feature extraction capability of the deep neural network (DNN) to learn the transmission patterns to demodulate the received signals. In our design, we propose to construct the long short-term memory (LSTM) unit-aided intelligent DNN-based deep learning (DL) demodulator for orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) systems. After learning, and extracting features of information-bearing chaotic transmissions at the training stage, the received signals can be recovered efficiently, and reliably at the deployment stage. Thanks to the recursive LSTM-aided DL design, correlations between information-bearing chaotic modulated signals can be exploited to enhance reliability performances. Simulation results demonstrate with the proposed DL demodulation design, the intelligent OFDM-DCSK system can achieve more reliable performances over additive white Gaussian noise (AWGN) channel, and fading channels compared with benchmark systems.