Jae-Hyun Ro1, Won-Seok Lee1, Min-Goo Kang2, Dae-Ki Hong3, Hyoung-Kyu Song1, *
CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 181-191, 2020, DOI:10.32604/cmc.2020.09998
Abstract In this paper, the supervised Deep Neural Network (DNN) based signal detection
is analyzed for combating with nonlinear distortions efficiently and improving error
performances in clipping based Orthogonal Frequency Division Multiplexing (OFDM)
ssystem. One of the main disadvantages for the OFDM is the high Peak to Average Power
Ratio (PAPR). The clipping is a simple method for the PAPR reduction. However, an effect
of the clipping is nonlinear distortion, and estimations for transmitting symbols are difficult
despite a Maximum Likelihood (ML) detection at the receiver. The DNN based online
signal detection uses the offline learning model where all weights and… More >
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