Designing Low-PAPR Waveform for OFDM-Based RadCom Systems

Huang, Yixuan; Hu, Su; Ma, Shiyong; Liu, Zilong; Xiao, Ming

doi:10.1109/twc.2022.3153606

Cited by 42 publications

(12 citation statements)

References 32 publications

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“…因此, 部分学者对如何降低 OFDM 信号的 PAPR 进行了深入研究 [35∼37] . 例如, Huang 等 [35] 提出了一种新颖的降低 OFDM 信号 PAPR 的波形设计算法, 该算法通过优化与通信段互补的子载波来实现. Varshney 等 [36] 在积分周期自相关旁瓣电平为零的约束条件下, 将基于 OFDM 信号的波形设计问题转化为 PAPR 最小化问题.…”

Section: 一体化波形设计unclassified

“…将 OTFS 通信信号与 FMCW 雷达信号进行正交复用, 不仅可以实现较低的 PAPR, 同时还可以获得高数据速率和精确的感知特性 [65] . 基于 OFDM 的一体化系统, 通过优化与通信频带互补的子载波, 可以获得具有低 PAPR 的新一体化波形 [35] . Yang 等 [66] 在总功率、PAPR 以及感知精度克拉美罗界 (Cramer-Rao bound, CRB) 的约束条件下, 通过优化多用户干扰来设计一体化新波形.…”

Section: 一体化波形设计unclassified

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太赫兹通信感知一体化技术综述

Chen,

Liu,

Lingxiang

et al. 2024

Sci. Sin.-Inf.

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IMT [15] ; 美国联邦通信委员会 (Federal Communications Commission, FCC) 开放 0.1∼3 THz 实验频段, 支持 0.95∼3 THz 高带宽应用及成像和传感创新 [16] . 由于具有百 GHz 以上的频谱资源、超短的波长, 太赫兹频段有潜力提供 Tbps (Terabits per second) 无线传输速率和毫米级的精确成像能力, 在工业自动化、智慧医疗、智能交通、手势识别、无接触人机交互和人体安全扫描等领域具有广泛的潜在应用价值.

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Section: 一体化波形设计unclassified

太赫兹通信感知一体化技术综述

Chen,

Liu,

Lingxiang

et al. 2024

Sci. Sin.-Inf.

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“…Similarly, in [37], the transmit codes to allocate the subcarrier power and the receive filters were jointly designed to obtain the desired waveform. Huang et al [38] proposed a peak-toaverage power ratio (PAPR) reduction scheme by optimizing the over those subcarriers that are complementary to the communication bands.…”

Section: A Ofdmmentioning

confidence: 99%

Integrated Sensing and Communication Waveform Design: A Survey

Zhou

Zhang

Chen

et al. 2022

IEEE Open J. Commun. Soc.

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Integrated sensing and communication (ISAC) has been widely recognized as a key technology in future sixth-generation (6G) wireless networks, especially for emerging applications and scenarios demanding both high-performance sensing and communication functionalities. The ISAC technique has the potential of enabling sensing and communication functionalities in a single hardware platform, even sharing the same waveform, thus, achieving the reduced cost of hardware implementation and the efficient use of spectrum resources. In this paper, we commence with the discussion on sensing and communication at the current stage from the view of motivations, applications, and challenges. Then, we provide a comprehensive survey of the state-of-the-art approaches to the ISAC technique from the waveform design perspective. To be specific, we classify the waveform design methods into three categories, namely, communicationcentric waveform design, sensing-centric waveform design, and joint waveform optimization and design. In addition, potential research directions and challenges for future ISAC waveform design are outlined.INDEX TERMS 6G, integrated sensing and communication, communication-centric waveform design, sensing-centric waveform design, joint waveform optimization and design.

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“…[10] use clipping method to minimise the PAPR; the authors in Ref. [11] carrie out optimising subcarrier allocation for communication bands to reduce PAPR; the authors in Ref. [12] adopt Golay block coding to reduce PAPR.…”

Section: Introductionmentioning

confidence: 99%

Radar and communication integrated systems design based on deep neural networks

Liu

Cao

Mao

et al. 2023

IET Radar Sonar & Navi

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Due to the spectrum congestion in the future 6G networks and the dramatic increasing requirement for the equipment that can detect and communicate simultaneously, the design of bi‐static radar and communication integrated (RCI) system is receiving more and more attention. Orthogonal frequency division multiplexing (OFDM) is a commonly used waveform in bi‐static RCI system. There are two gaps in current research that need to be filled. First, high peak‐to‐average ratio (PAPR) of OFDM signal is less considered in the conventional bi‐static RCI system due to the difficulty in recovering in the receiver. Second, the target echo estimation depends on the reconstruction of the direct‐path communication (DPC) signal. To overcome these limitations, we firstly propose a deep learning (DL)‐based transceiver design for the bi‐static RCI systems. Specifically, we design a neural network (NN)‐based waveform designer (WDNN) to reduce the PAPR of the transmit waveform, an NN‐based signal separator (SSN) to directly separate the target echo and the DPC signal, and an NN‐based symbol recovery (SRN) to demodulate the symbols. By using the training data and channel realisations, these three networks are jointly trained offline to minimise the PAPR of the transmit waveform, the separation error of the target echo and DPC signals, and the bit error rate. Then, in the online deployment phase, the trained WDNN is used to modify the transmitted waveform, and the trained SSN and SRN are placed at the receiver to separate signal and demodulate symbol. Simulation results show that compared to the conventional method, the proposed scheme not only reduces the PAPR but also improves the estimation of target parameters and communication symbol.

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Designing Low-PAPR Waveform for OFDM-Based RadCom Systems

Cited by 42 publications

References 32 publications

太赫兹通信感知一体化技术综述

太赫兹通信感知一体化技术综述

Integrated Sensing and Communication Waveform Design: A Survey

Radar and communication integrated systems design based on deep neural networks

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