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- Title
融合压缩采样与深度神经网络的直接序列扩频参数估计.
- Authors
刘 锋; 张 爽; 黄渝昂
- Abstract
The high sampling rates caused by the wideband characteristics of the direct sequence spread spectrum (DSSS) signals increase the difficulty of the parameter estimations. Regarding to the problems and challenges within the existing techniques, a method based on the combination of the compressive sampling (CS) and deep neural network (DNN) is proposed to estimate the parameters of the DSSS signals. On the one hand, the CS can obtain the information on the parameters effectively with low sampling rate, by exploiting the redundancy within signals; on the other hand, the DNN can extract the data features effectively and accurately. By the combined training of the compressive sampling and the parameter estimation network, the effective cooperation of the two parts and the accurate estimation of the DSSS estimations with low sampling rates are achieved. The simulations prove that the proposed method outperforms the conventional method in terms of estimation capability in low signal-to-noise ratio.
- Subjects
ARTIFICIAL neural networks; PARAMETER estimation; HAND signals; SIGNAL-to-noise ratio; CHANNEL estimation
- Publication
Telecommunication Engineering, 2022, Vol 62, Issue 9, p1248
- ISSN
1001-893X
- Publication type
Article
- DOI
10.3969/j.issn.1001-893x.2022.09.007