Abstract
Orthogonal frequency division multiplexing (OFDM) is extensively applied in the downlink of narrowband Internet of Things (NB‐IoT). However, the high peak‐to‐average power ratio (PAPR) of OFDM systems leads to a decrease in transmitter efficiency. Therefore, the researchers proposed the artificial neural network (ANN) based PAPR reduction schemes. However, these schemes have the disadvantages of high complexity or cannot overcome the defects of traditional schemes. In this paper, a novel PAPR reduction scheme based on neural networks (NNs) is proposed for OFDM systems. This scheme establishes a PAPR reduction module based on NN, which is trained using the low PAPR data obtained by the simplified clipping and filtering (SCF) method. To overcome the defect of poor BER performance of the SCF scheme, a recovery module is introduced at the receiver, to recover the distorted signal. To realize the improvement of BER performance and the reduction of PAPR simultaneously, the two modules are jointly trained based on multiobjective optimization. Experimental results based on a 100 MHz OFDM signal show that this scheme can reduce PAPR by 4.5 dB. Meanwhile, the BER of this scheme can be reduced to 0.001 times that of the SCF scheme.
Highlights
Internet of Things (IoT) technologies are getting more and more attention, with the connection of a large number of devices [1,2,3]
The wireless communication channel in the simulation system is modeled as an additive white Gaussian noise (AWGN) channel, and the signal-to-noise ratio (SNR) is determined as 20 dB during the training process of the model
To verify the superiority of the proposed scheme, the proposed scheme is compared with the iterative CF (ICF) scheme, simplified clipping and filtering (SCF) scheme, and neural networks (NNs)-based SCF scheme in terms of complementary cumulative distribution function (CCDF) performance and bit error rate (BER) performance
Summary
Received 24 February 2021; Revised 10 March 2021; Accepted March 2021; Published April 2021. The researchers proposed the artificial neural network (ANN) based PAPR reduction schemes. These schemes have the disadvantages of high complexity or cannot overcome the defects of traditional schemes. A novel PAPR reduction scheme based on neural networks (NNs) is proposed for OFDM systems. This scheme establishes a PAPR reduction module based on NN, which is trained using the low PAPR data obtained by the simplified clipping and filtering (SCF) method. To overcome the defect of poor BER performance of the SCF scheme, a recovery module is introduced at the receiver, to recover the distorted signal. The BER of this scheme can be reduced to 0.001 times that of the SCF scheme
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