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Clipping Noise Estimation Based on Deep Complex Neural Network with Sparsity Constraint
- Source :
- VTC Spring
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Clipping noise estimation and cancellation are essential in orthogonal frequency division multiplexing (OFDM) systems when clipping is performed to reduce the peak-to-average power ratio (PAPR). Motivated by the richer representational capacity of complex numbers and the fact that communication is a complex-valued problem, a novel clipping noise estimation scheme based on deep complex neural network is proposed in this paper. Specifically, the clipping noise is determined by a deep complex network, namely clipping noise estimation network (CNE-Net), such that the mean square error (MSE) and the sparsity of the estimated clipping noise are jointly optimized. Besides, an ordering based zero-forcing scheme is utilized to further ensure the sparsity of the estimated clipping noise. Simulation results show that the proposed CNE-Net shows comparable performance with the conventional decision-aided reconstruction (DAR) scheme and can achieve better performance than the one-iteration DAR scheme when the clipping noise is not sparse enough. In summary, the CNE-Net has a good capability to estimate the clipping noise from noise-affected features.
- Subjects :
- Clipping (audio)
Mean squared error
Clipping noise
Artificial neural network
Orthogonal frequency-division multiplexing
Computer science
020206 networking & telecommunications
02 engineering and technology
Constraint (information theory)
Clipping (photography)
Computer Science::Sound
Computer Science::Multimedia
0202 electrical engineering, electronic engineering, information engineering
Complex number
Algorithm
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)
- Accession number :
- edsair.doi...........f5270134a93947900f31fa197e998ce9