A Novel SNR Estimation Technique Associated with Hybrid ARQ

A Novel SNR Estimation Technique Associated with Hybrid ARQ
复制标题

一种与混合ARQ相关的新型SNR估计技术

DOI:
10.1587/transfun.e92.a.2895
复制
发表时间:
2009-11
影响因子:
0.5
通讯作者:
Fan, Pingzhi
Fan, Pingzhi
中科院分区:
计算机科学4区
文献类型:
--
作者:
Chen, Qingchun;Fan, Pingzhi

文献摘要

参考文献

相似文献

在AWGN信道下,通过使用多个重复信号副本来构造混合ARQ系统中的累积观测噪声信号序列(AONSS)或差分观测噪声信号序列(DONSS),提出了一种新的数据辅助最大似然(DA ML)信噪比估计和盲ML信噪比估计方法。结果表明,传统的DA ML估计是一个特殊的情况下,新的DA ML估计,和建议的DA ML和建议的盲ML SNR估计技术可以提供令人满意的SNR估计,而不会引入显着的额外复杂性,现有的混合ARQ方案。基于AONSS,得到了广义确定性和随机Cramer-Rao下界(GCRLB),其中包括传统Cramer-Rao下界(CRLB)的特殊情况.最后,通过数值分析和仿真结果验证了所提出的基于AONSS和DONSS的信噪比估计技术的适用性。
By using multiple repeated signal replicas to formulate the accumulative observed noisy signal sequence (AONSS) or the differential observed noisy signal sequence (DONSS) in the hybrid ARQ system, a novel data-aided maximum likelihood (DA ML) SNR estimation and a blind ML SNR estimation technique are proposed for the AWGN channel. It is revealed that the conventional DA ML estimate is a special case of the novel DA ML estimate, and both the proposed DA ML and the proposed blind ML SNR estimation techniques can offer satisfactory SNR estimation without introducing significant additional complexity to the existing hybrid ARQ scheme. Based on the AONSS, both the generalized deterministic and the random Cramer-Rao lower bounds (GCRLBs), which include the traditional Cramer-Rao lower bounds (CRLBs) as special cases, are also derived. Finally, the applicability of the proposed SNR estimation techniques based on the AONSS and the DONSS are validated through numerical analysis and simulation results.
DOI: 10.1109/26.58745
发表时间: 1990-08
期刊: IEEE Trans. Commun.
影响因子: --
作者:
S. Kallel
通讯作者: S. Kallel
DOI: 10.1109/7.102709
发表时间: 1990-09
影响因子: 4.4
作者:
B. Shah;S. Hinedi
通讯作者: B. Shah;S. Hinedi
DOI: 10.1109/tcom.1968.1089851
发表时间: 1968-06
影响因子: 8.3
作者:
R. Gagliardi;C. Thomas
通讯作者: R. Gagliardi;C. Thomas
DOI: 10.1049/el:20001432
发表时间: 2000-11
影响因子: 1.1
作者:
Soon-Young Kim;J. Chang;M. Lee
通讯作者: Soon-Young Kim;J. Chang;M. Lee