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.
|Number of pages||4|
|Journal||IEEE Communications Letters|
|Publication status||Published - 2018 Mar|
Bibliographical noteFunding Information:
Manuscript received November 22, 2017; revised December 19, 2017; accepted December 20, 2017. Date of publication December 27, 2017; date of current version March 8, 2018. This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2014-0-00282, Development of 5G Mobile Communication Technologies for Hyper-connected smart services). The associate editor coordinating the review of this paper and approving it for publication was B. Smida. (Corresponding author: Woongsup Lee.) M. Kim and D.-H. Co are with the School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea (e-mail: email@example.com; firstname.lastname@example.org).
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All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Computer Science Applications
- Electrical and Electronic Engineering