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时频二维重叠复用系统
引用本文:王竞, 李道本. 时频二维重叠复用系统[J]. 电子与信息学报, 2008, 30(5): 1176-1179. doi: 10.3724/SP.J.1146.2007.00541
作者姓名:王竞  李道本
作者单位:北京邮电大学信息工程学院,北京,100876;北京邮电大学信息工程学院,北京,100876
摘    要:基于时域重叠复用和频域重叠复用的思想,该文提出了时频二维重叠复用系统OvHDM系统。研究了OvHDM系统的最佳检测、频谱效率、复杂度、性能以及峰均比。OvHDM系统模型是向量卷积编码约束模型。为了检测OvHDM信号,提出了向量空间上的最大似然序列检测算法,MU-MLSD。计算机仿真证明:频谱效率为 时,OvHDM较64QAM具有4.5dB的功率增益,较OvTDM也有1.5dB的功率增益;同时,OvHDM的峰均比较128个子载波的OFDM有2dB的增益,较1024个子载波的OFDM有2.6dB的增益。

关 键 词:时频二维重叠复用   时域重叠复用   频域重叠复用   多用户序列检测
文章编号:1009-5896(2008)05-1176-04
收稿时间:2007-04-09
修稿时间:2007-04-09

Overlapping Multiplexing in Both Time and Frequency Domain
Wang Jing, Li Dao-ben . Overlapping Multiplexing in Both Time and Frequency Domain[J]. Journal of Electronics & Information Technology, 2008, 30(5): 1176-1179. doi: 10.3724/SP.J.1146.2007.00541
Authors:Wang Jing  Li Dao-ben
Affiliation:School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Abstract:In the paper, a novel overlapping multiplexing technology named OvHDM (Overlapped Hybrid Division Multiplexing) is proposed. In OvHDM, the overlapping multiplexing is developed in both time and frequency domain. The optimum detection, spectrum efficiency, detection complexity, performance and Peak-to-Average Power Ratio (PAPR) of OvHDM are studied in detail. The signal model of OvHDM is the convolutional constraint model between vectors. In order to implement optimum detection, a novel algorithm named MU-MLSD (Multi-User Maximum-Likelihood Sequence Detection) is proposed. The simulation results prove that the performance of OvHDM is better 4.5dB than that of 64QAM and better 1.5dB than that of OvTDM (Overlapped Time Division Multiplexing) when the spectrum efficiency is 6bit/s/Hz. Meanwhile, the PAPR of OvHDM is better 2dB than that of 128-subcarrier OFDM and better 2.6dB than that of 1024-subcarrier OFDM.
Keywords:Overlapped Hybrid Division Multiplexing (OvHDM)  Overlapped Time Division Multiplexing (OvTDM)  Overlapped Frequency Division Multiplexing (OvFDM)  Multi-User Maximum-Likelihood Sequence Detection (MU-MLSD)
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