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Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering.
Zhuo, Xinran; Pan, Jianxiong; Wang, Huwei; Li, Xiangming; Ye, Neng.
Afiliação
  • Zhuo X; School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Pan J; Science and Technology on Communication Networks Laboratory, Shijiazhuang 050081, China.
  • Wang H; School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
  • Li X; School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
  • Ye N; School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel) ; 22(13)2022 Jun 29.
Article em En | MEDLINE | ID: mdl-35808399
ABSTRACT
High frequency wireless communication aims to provide ultra high-speed transmissions for various application scenarios. The waveform design for high frequency communication is challenging due to the requirements for high spectrum efficiency, as well as good hardware compatibility. With high flexibility and low peak-to-average power ratio (PAPR), discrete Fourier transformation spreading-based orthogonal frequency division multiplexing (DFT-s-OFDM) can be a promising candidate waveform. To further enhance the spectral efficiency, we integrate faster-than-Nyquist (FTN) signaling in DFT-s-OFDM, and find that the PAPR performance can also be improved. While FTN can introduce increased inter-symbol interference (ISI), in this paper, we deploy an isotropic orthogonal transform algorithm (IOTA) filter for FTN-enhanced DFT-s-OFDM, where the compact time-frequency structure of the IOTA filter can significantly reduce the ISI. Simulation results show that the proposed waveform is capable of achieving good performance in PAPR, bit error rate (BER) and throughput, simultaneously, with 3.5 dB gain in PAPR and 50% gain in throughput.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article