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Maximum entropy quantization for link-state adaptation in two-way relaying

机译:双向中继链路状态适应的最大熵量化

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In this paper, we investigate how to adapt a composite decode-forward (DF) two-way relaying scheme to fading links. We develop a maximum entropy (ME) quantization algorithm to feed back the link magnitudes such that the transmitters are able to perform link adaptation. The ME algorithm outperforms that of Lloyd-Max in mid and high range SNR, and performs comparably at low SNR. With just one bit of ME quantization feedback, rate performance of the composite DF scheme is better than that of long-term channel state information (CSI). With three quantization bits, performance is close to that of perfect CSI. We further evaluate the sensitivity of the ME quantizer to errors in the estimated channel distribution. The developed ME quantization algorithm can also be applied in any compression scenario with known data distribution, including real-time applications because of the simplicity of the quantizer.
机译:在本文中,我们研究了如何调整复合解码(DF)双向中继方案到衰落链路。我们开发了最大熵(ME)量化算法来回馈链路幅度,使得发射器能够执行链路自适应。 ME算法优于MID和高范围SNR中的LLOYD-MAX的胜过,并且在低SNR处进行相对执行。只有ME的一点量化反馈,复合DF方案的速率性能优于长期信道状态信息(CSI)。具有三个量化位,性能接近完美的CSI。我们进一步评估了ME量化器对估计信道分布中误差的敏感性。发布的ME量化算法也可以应用于具有已知数据分布的任何压缩方案,包括实时应用程序,因为量化器的简单性。

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