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

机译:Turbo Coded FH M-FSK Ad Hoc数据包无线网络软判决度量

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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:本文地址涡轮编码的非相干FH M-FSK ad Hoc网络,具有干扰泊的泊松分布,其中多个访问干扰可以作为对称/ SPL alpha / -stable(SAS)噪声和/ SPL alpha /成反比路径损失指数。贝叶斯高斯度量标准在非高斯(/ SPL alpha // SPL NE / 2)噪声环境中并不良好地表现良好,因此Cauchy(/ SPL alpha / = 1)噪声的最佳度量和广义似然比(GLR)高斯提出了需要较少信息(幅度,色散)的度量。在不同的SOS噪声环境中评估度量的鲁棒性,以及干扰主导网络中干扰色散和信道幅度的不匹配值,其具有没有衰落或独立的瑞利衰落。 Cauchy和GLR高斯度量均在贝叶斯高斯公制上表现出显着的性能增益,而GLR高斯度量标准则没有观察分散或幅度。 Cauchy度量对幅度的知识比色散更敏感,但通常比这些参数的各种不匹配值的GLR高斯度量保持更好的性能。另外,在由不可忽略不可忽略的高斯热噪声以及多次访问干扰组成的环境中,增加热噪声水平降低GLR高斯和Cauchy公制的性能,而对于观察到的水平,它们都会比贝叶斯高斯公制保持更好的性能。

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