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Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks. (arXiv:1810.07181v1 [eess.SP])
来源于:arXiv
Recent explorations of Deep Learning in the physical layer (PHY) of wireless
communication have shown the capabilities of Deep Neuron Networks in tasks like
channel coding, modulation, and parametric estimation. However, it is unclear
if Deep Neuron Networks could also learn the advanced waveforms of current and
next-generation wireless networks, and potentially create new ones. In this
paper, a Deep Complex Convolutional Network (DCCN) without explicit Discrete
Fourier Transform (DFT) is developed as an Orthogonal Frequency-Division
Multiplexing (OFDM) receiver. Compared to existing deep neuron network
receivers composed of fully-connected layers followed by non-linear
activations, the developed DCCN not only contains convolutional layers but is
also almost (and could be fully) linear. Moreover, the developed DCCN not only
learns to convert OFDM waveform with Quadrature Amplitude Modulation (QAM) into
bits under noisy and Rayleigh channels, but also outperforms expert OFDM
receiver ba 查看全文>>