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Closed-form CRLBs for SNR estimation from turbo-coded square-QAM-modulated signals

机译:从Turbo编码的方形QAM调制信号进行SNR估计的闭式CRLB

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In this contribution, we derive for the first time the closed-form expressions for the Cramér-Rao lower bounds (CRLBs) of the signal-to-noise ratio (SNR) estimates from turbo-coded square-QAM transmissions. By exploiting the structure of the Gray mapping, we are able to factorize the likelihood function thereby linearizing all the derivation steps for the FIM elements. The analytical CRLBs coincide exactly with their empirical counterparts validating thereby our new analytical expressions. Numerical results suggest that the CLRBs for code-aided (CA) SNR estimates range between the CRLBs for non-data-aided (NDA) SNR estimates and those for data-aided (DA) ones, thereby highlighting the effect of the coding gain. At sufficiently high SNR levels, the three CRLBs coincide. The derived bounds are also valid for LDPC-coded systems and they can be evaluated in the same way when the latter are decoded using the turbo principal.
机译:在此贡献中,我们首次从涡轮编码平方QAM传输中得出信噪比(SNR)估计的Cramér-Rao下界(CRLB)的闭式表达式。通过利用格雷映射的结构,我们能够分解似然函数,从而线性化FIM元素的所有推导步骤。分析性CRLB与它们的经验对等完全吻合,从而验证了我们的新分析表达式。数值结果表明,代码辅助(CA)SNR估计的CLRB在非数据辅助(NDA)SNR估计的CRLB和数据辅助(DA)的CRLB之间,从而突出了编码增益的影响。在足够高的SNR级别下,三个CRLB重合。导出的边界对于LDPC编码的系统也有效,并且使用turbo原理对后者进行解码时,可以用相同的方式对其进行评估。

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