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A Diversity Analysis for Distributed Interference Alignment Using the Max-SINR Algorithm

机译:基于Max-SINR算法的分布式干扰对准的分集分析

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摘要

Gomadam recently proposed two distributed interference alignment algorithms, namely the zero-forcing and the maximal signal to interference plus noise ratio (max-SINR) algorithms. Both of them only require local channel state information and no symbol extension is needed. Then, Ning showed that when only one stream of information symbols is sent by each user, interference alignment may achieve receive diversity using the max-SINR algorithm. This result was, however, derived only based on an assumption. In this paper, using a different approach, we prove that interference alignment using the max-SINR algorithm indeed achieves receive diversity without the assumption used by Ning The result in this paper not only completes the proof of the result by Ning , but also generalizes it by allowing more than one stream of information symbols to be sent by each user.
机译:戈马丹(Gomadam)最近提出了两种分布式干扰对准算法,即迫零和最大信噪比(max-SINR)算法。它们都只需要本地信道状态信息,并且不需要符号扩展。然后,Ning展示了当每个用户仅发送一个信息符号流时,干扰对齐可以使用max-SINR算法实现接收分集。但是,此结果仅基于假设得出。在本文中,我们使用另一种方法证明了使用max-SINR算法的干扰对准确实可以实现接收分集,而无需使用Ning的假设。本文的结果不仅完成了Ning对结果的证明,而且对其进行了概括。通过允许每个用户发送一个以上的信息符号流。

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