Peak to average power ratio reduction techniques based on chirp selection for single and multi-user orthogonal chirp division multiplexing system

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Vincent Savaux
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Abstract

This article deals with peak to average power (PAPR) reduction in a single and multi-user orthogonal chirp division multiplexing (OCDM) context. Two methods for PAPR reduction based on the selection of the frequency variation (up or down) of the chirps are first presented in a single user system. The first technique consists in considering two OCDM signals generated with up and down chirps, respectively, and selecting the one offering lowest PAPR. The second PAPR reduction method is based on usual clipping, and in that case the chirp selection aims to reduce the clipping noise. An adapted receiver is presented, based on the maximum likelihood estimation of the frequency variation (up or down) of the chirp. Then, a general procedure for multi-user OCDM transmission is introduced, where a sub-band of the available bandwidth is dedicated to each user, whose frequency of the chirps varies within this sub-band. Next, the PAPR reduction techniques are generalised to this multi-user OCDM system. Moreover, a performance analysis of the first PAPR reduction method is developed, and it is shown through simulations that theoretical and numerical results match for both Nyquist rate and oversampled signals. It is also shown that the chirp selection reduces the clipping noise, and improves the bit error rate performance compared with clipping only.

Abstract Image

单用户和多用户正交啁啾分复用系统中基于啁啾选择的峰值平均功率比降低技术
本文讨论了单用户和多用户正交啁啾分复用(OCDM)环境下的峰值平均功率(PAPR)降低问题。首先在单用户系统中提出了两种基于选择啁啾频率变化(向上或向下)的PAPR降低方法。第一种技术包括考虑分别由向上和向下啁啾产生的两个OCDM信号,并选择提供最低PAPR的信号。第二种减少PAPR的方法是基于通常的裁剪,在这种情况下,啁啾选择的目的是减少裁剪噪声。基于对啁啾频率变化(向上或向下)的最大似然估计,提出了一种自适应接收机。然后,介绍了多用户OCDM传输的一般流程,其中可用带宽的一个子频带专用于每个用户,其啁啾频率在该子频带内变化。接下来,将PAPR降低技术推广到这个多用户OCDM系统。此外,对第一种PAPR减小方法进行了性能分析,并通过仿真证明了理论和数值结果对奈奎斯特速率和过采样信号都是匹配的。啁啾选择降低了剪切噪声,与单纯的剪切相比,提高了误码率性能。
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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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