arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1807.05660cs.ITmath.IT
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MmWave通信中的高精度自适应波束训练算法

A High-Accuracy Adaptive Beam Training Algorithm for MmWave Communication

Zihan Tang, Jun Wang, Jintao Wang, Jian Song

摘要:

在毫米波通信中,波束训练是实现波束对准的一种有效方法。传统的波束训练方法在预先设计的波束训练码本中为每个波束平均分配训练资源。这种方法的性能远不能令人满意,因为不同的波束具有不同的波束形成增益,并且因此某些波束相对于其他波束更难与最佳波束区分开。本文提出了一种新的波束训练算法,该算法自适应地为每个波束分配训练资源。具体地说,该算法将更多的训练符号分配给波束赋形增益相对较高的波束,而使用较少的资源来区分波束赋形增益相对较低的波束。通过理论分析和数值仿真,我们表明,在实际情况下,所提出的自适应算法的波束训练精度渐近优于传统的方法。此外,模拟还表明,这种相对性能行为在非渐近制度。

英文摘要:

In millimeter wave communications, beam training is an effective way to achieve beam alignment. Traditional beam training method allocates training resources equally to each beam in the pre-designed beam training codebook. The performance of this method is far from satisfactory, because different beams have different beamforming gain, and thus some beams are relatively more difficult to be distinguished from the optimal beam than the others. In this paper, we pro- pose a new beam training algorithm which adaptively allocates training resources to each beam. Specifically, the proposed algorithm allocates more training symbols to the beams with relatively higher beamforming gain, while uses less resources to distinguish the beams with relatively lower beamforming gain. Through theoretical analysis and numerical simulations, we show that in practical situations the proposed adaptive algorithm asymptotically outperforms the traditional method in terms of beam training accuracy. Moreover, simulations also show that this relative performance behavior holds in non-asymptotic regime.

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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