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arXiv 1010.2787cs.ITmath.IT
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基于模拟信道状态反馈的干扰对齐

Interference Alignment with Analog Channel State Feedback

Omar El Ayach, Robert W. Heath

摘要:

干扰对齐是一种针对干扰信道的复用增益最优传输策略。虽然使用IA实现的和速率比以前认为可能的要高得多,但这种改进通常是以在发射机处需要网络信道状态信息为代价的。这可以通过显式反馈来实现,这是一种灵活但可能代价高昂的方法,会产生大量开销。在本文中,我们提出了模拟反馈作为替代有限反馈或互惠为基础的对齐。我们发现,完整的复用增益观察到完美的信道知识是由模拟反馈保存,并在总速率的平均损失是有界的一个常数时,信噪比是可比的前向和反馈信道。当信噪比不完全对称时,实现了复用增益的一小部分。我们考虑了训练和反馈的开销,并使用这个框架来优化系统的有效吞吐量。我们目前的模拟结果,以证明模拟反馈IA的性能,验证我们的理论分析,并扩展我们的结论,最佳的培训和反馈长度。

英文摘要:

Interference alignment (IA) is a multiplexing gain optimal transmission strategy for the interference channel. While the achieved sum rate with IA is much higher than previously thought possible, the improvement often comes at the cost of requiring network channel state information at the transmitters. This can be achieved by explicit feedback, a flexible yet potentially costly approach that incurs large overhead. In this paper we propose analog feedback as an alternative to limited feedback or reciprocity based alignment. We show that the full multiplexing gain observed with perfect channel knowledge is preserved by analog feedback and that the mean loss in sum rate is bounded by a constant when signal-to-noise ratio is comparable in both forward and feedback channels. When signal-to-noise ratios are not quite symmetric, a fraction of the multiplexing gain is achieved. We consider the overhead of training and feedback and use this framework to optimize the system's effective throughput. We present simulation results to demonstrate the performance of IA with analog feedback, verify our theoretical analysis, and extend our conclusions on optimal training and feedback length.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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