Min-Hoe Kim, Woong-Sup Lee and Professor Dong-Ho Cho’s paper was published in IEEE Communications Letters

Title: A novel PAPR reduction scheme for OFDM system based on deep learning

Authors: Min-Hoe Kim, Woong-Sup Lee and Dong-Ho Cho

High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, known as PAPR reducing network (PRNet), based on the autoencoder architecture of deep learning. In the PRNet, the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique, such that both the bit error rate (BER) and the PAPR of the OFDM system are jointly minimized. We used simulations to show that the proposed scheme outperforms conventional schemes in terms of BER and PAPR.

Figure 1. Structure of proposed PRNet with DNN encoder and DNN decoder.

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