Abstract
Peak-to-Average Power Ratio (PAPR) reduction is one of the main key factors in orthogonal frequency division multiplexing (OFDM) systems. From the various existing PAPR reduction techniques, Neural Network (NN) has been one of the efficient and powerful techniques in reducing the PAPR due to its good generalization properties with flexible modeling and learning capabilities. In this paper, we propose a new method that uses NNs trained on the active constellation extension (ACE) signals to reduce the PAPR of OFDM signals. It employs a receiver NN unit, at the OFDM receiver side for achieving significant bit error rate (BER) improvement with low computational complexity. To reduce the time complexity SFBC (Space frequency block coding) block is used in NN and multiple input and multiple output (MIMO) technique is introduced.
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