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Low-Complexity Integrated Super-Resolution Sensing and Communication with Signal Decimation and Ambiguity Removal

ZTE Communications 2024年第3期22卷 48-55页

作者： DAI Qianglong ZHOU Zhiwen XIAO Zhiqiang ZENG Yong YANG Fei CHEN Yan National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China Wireless Technology Lab. 2012 LabShanghai Huawei Technolo-gies Co.LtdShanghai 201206China

Integrated sensing and communication(ISAC)is one of the main usage scenarios for 6G wireless *** most efficiently utilize the limited wireless resources,integrated super-resolution sensing and communication(ISSAC)has been recently proposed to significantly improve sensing performance with super-resolution algorithms for ISAC systems,such as the Multiple Signal Classification(MUSIC)***,traditional super-resolution sensing algorithms suffer from prohibitive computational complexity of orthogonal-frequency division multiplexing(OFDM)systems due to the large dimensions of the signals in the subcarrier and symbol *** address such issues,we propose a novel two-stage approach to reduce the computational complexity for super-resolution range estimation *** key idea of the proposed scheme is to first uniformly decimate signals in the subcarrier domain so that the computational complexity is significantly reduced without missing any target in the range ***,the decimation operation may result in range ambiguity due to pseudo peaks,which is addressed by the second stage where the total collocated subcarrier data are used to verify the detected *** with traditional MUSIC algorithms,the proposed scheme reduces computational complexity by two orders of magnitude,while maintaining the range resolution and *** results verify the effectiveness of the proposed scheme.

关键词： ISSAC sparse decimation range ambiguity two-stage approach

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two-stage piecewise linear model for investigating doseresponse relationship in meta-analysis:methodology,examples,and applicationShort title:two-stage piecewise linear model for dose-response meta-analysis

Two-stage piecewise linear model for investigating doserespo...

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2017年中国卫生统计学学术年会

作者： Chang Xu Lehana Thabane Chao Zhang Tong-Zu Liu Ling Li Ke Deng Xin Sun Chinese Evidence-Based Medicine Center and Chinese Cochrane Center West China HospitalSichuan University Department of Clinical Epidemiology and Biostatistics McMaster University Center for Evidence-Based Medicine and Clinical Research Taihe HospitalHubei University of Medicine Department of Urology Zhongnan Hospital of Wuhan UniversityWuhan University

Background:Dose-response meta-analysis（DRMA）is widely employed to establishing the potential dose-response relationship between continuous exposures and disease ***,no method is readily available for exploring the relation between a discrete exposure and a binary or continuous ***:We proposed a two-stage piecewise linear（PL）DRMA model as a solution to this *** used sleep（a continuous exposure）and parity（a discrete exposure）data to illustrate how to apply PL model in DRMA using the Stata code *** also empirically compared the slopes of PL model with restricted cubic spline（RCS）model by sleep ***:PL DRMA model fitted well in our two *** sleep duration,our meta-analysis showed that the relative risk（RR）of all-cause mortality was 1.02（95%CI:1.01,1.04）for every 1-hour reduction of sleep duration among people who slept less than 7 hours,and 1.15（95%CI:1.06,1.25）for every 1-hour increase of sleep duration among people who slept more than 7 *** parity and risk of rheumatoid arthritis:among women with 3 or less births,the RR was 0.88（95%CI:0.77,1.00）for every 1-birth increment;otherwise（3 or more births）,the RR was 1.10（95%CI:0.99,1.23）for every 1-birth *** slopes of PL model fitted well to RCS curve based on sleep ***:Piecewise linear function is a simple and valid method for DRMA and can be used for discrete *** also represents a superior model to linear model in DRMA and may be an alternative model to non-linear model.

关键词： piecewise linear function dose-response meta-analysis discrete exposure two-stage approach

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