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Revisit on maximum ratio combining reception practically attained across correlated Nakagami-m branches

机译:回顾相关Nakagami-m分支实际获得的最大比率合并接收

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First- and second-order statistics that can be attained at a practical receiver via maximum-ratio combining (MRC) reception across correlated Nakagami-m branches are studied in this paper. A lump sum of the branch weight squares is employed in the conventional analysis of the MRC reception. The conventional MRC reception whose first- and second-order statistics were only derived on a noise-free condition can never be achieved in practice because the branches, which are smeared by crosscorrelation and are corrupted by noise, can only be statistically specified and they must be considered of as stochastic processes at the receiver, instead of deterministic time functions. The proposed method, in fact, considers the branch weights as stochastic processes and effectively restores diversity by the assistance from a linear transformation. Significant component selection combining (SCSC) can hence be applied not only to simplify the inner receiver but also to reduce the estimation error and the thermal noise. The MRC reception is then conducted with assistance from estimates of the decorrelated branches that are selected by the SCSC methods. The performance merit figures, i.e., first- and second-order statistics, must be redefined in the post-inner-receiving sense to provide sufficient and accurate information required for the design or the derivations of the outer receiving techniques or algorithms. The first- and second-order statistics of the MRC reception over the significant decorrelated diversity branches were verified by computer simulations.
机译:本文研究了在实际接收器上可以通过相关Nakagami-m分支上的最大比率合并(MRC)接收获得的一阶和二阶统计量。在MRC接收的常规分析中采用分支权重平方的总和。一阶和二阶统计仅在无噪声条件下得出的常规MRC接收在实践中永远无法实现,因为被互相关拖尾且被噪声破坏的分支只能通过统计方式指定,并且必须在接收器被视为随机过程,而不是确定性的时间函数。实际上,所提出的方法将分支权重视为随机过程,并借助线性变换的帮助有效地恢复了多样性。因此,重要的分量选择组合(SCSC)不仅可以简化内部接收器,而且可以减小估计误差和热噪声。然后在通过SCSC方法选择的去相关分支的估计的帮助下进行MRC接收。必须从内部接收后的意义上重新定义性能指标,即一阶和二阶统计,以提供设计或外部接收技术或算法的派生所需的足够和准确的信息。通过计算机仿真验证了重要去相关分集分支上的MRC接收的一阶和二阶统计量。

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