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Soft decision metrics for turbo-coded FH M-FSK ad hoc packet radio networks

机译:涡轮编码的FH M-FSK ad hoc分组无线网络的软判决指标

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This paper addresses turbo-coded non-coherent FH M-FSK ad hoc networks with a Poisson distribution of interferers where multiple access interference can be modeled as symmetric /spl alpha/-stable (SaS) noise and /spl alpha/ is inversely proportional to the path loss exponent. The Bayesian Gaussian metric does not perform well in non-Gaussian (/spl alpha//spl ne/2) noise environments and therefore an optimum metric for Cauchy (/spl alpha/=1) noise and a generalized likelihood ratio (GLR) Gaussian metric requiring less side information (amplitude, dispersion) are presented. The robustness of the metrics is evaluated in different SoS noise environments and for mismatched values of the interference dispersion and channel amplitude in an interference-dominated network with no fading or independent Rayleigh fading. Both the Cauchy and GLR Gaussian metric exhibit significant performance gain over the Bayesian Gaussian metric, while the GLR Gaussian metric does so without the knowledge of the dispersion or amplitude. The Cauchy metric is more sensitive to the knowledge of the amplitude than the dispersion, but generally maintains better performance than the GLR Gaussian metric for a wide range of mismatched values of these parameters. Additionally, in an environment consisting of non-negligible Gaussian thermal noise along with multiple access interference, increasing the thermal noise level degrades the performance of the GLR Gaussian and Cauchy metric while for the observed levels both maintain better performance than the Bayesian Gaussian metric.
机译:本文研究了具有Poisson分布的干扰源的Turbo编码非相干FH M-FSK ad hoc网络,其中多路访问干扰可以建模为对称/ spl alpha / -stable(SaS)噪声,而/ spl alpha /与/ spl alpha /成反比。路径损耗指数。贝叶斯高斯度量在非高斯(/ spl alpha // spl ne / 2)噪声环境中表现不佳,因此是柯西(/ spl alpha / = 1)噪声和广义似然比(GLR)高斯的最佳度量提出了需要较少辅助信息(幅度,色散)的度量。在没有衰落或独立瑞利衰落的干扰占主导地位的网络中,评估指标的鲁棒性在不同的SoS噪声环境中,以及干扰色散和信道幅度的失配值。柯西(Cauchy)和GLR高斯(Gaussian)度量均比贝叶斯高斯(Bayesian Gaussian)度量表现出显着的性能增益,而GLR高斯(Gaussian)度量却在不了解色散或振幅的情况下实现了这一目的。柯西度量比色散对振幅的知识更敏感,但对于这些参数的广泛失配值,通常保持比GLR高斯度量更好的性能。此外,在由不可忽略的高斯热噪声和多路访问干扰组成的环境中,增加热噪声级别会降低GLR高斯和柯西度量的性能,而对于观察到的级别,它们都保持比贝叶斯高斯度量更好的性能。

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