多径存在下的多用户波束对准

Nariman Torkzaban, M. Khojastepour, J. Baras
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引用次数: 1

摘要

为了克服毫米波通信中的高路径损耗和强阴影,需要采用具有高波束形成增益的窄波束的有效波束形成方案。毫米波通道由几个空间簇组成,每个簇与一个出发角(AoD)相关联。窄波束必须与通道aod对齐,以增加波束形成增益。这是通过称为光束对准(BA)的过程实现的。文献中的大多数BA方案都考虑了具有单一主导路径的信道,而在实践中,信道具有几个具有不同aod的可解析路径,因此,这种BA方案可能无法在多路径存在的情况下正常工作,或者至少不能利用这种多路径来实现多样性或增加鲁棒性。在本文中,我们提出了一种多路径存在下的高效BA方案。该方案使用一组扫描波束发送探测数据包,并在探测阶段结束时接收每个用户对所有扫描波束的反馈。我们将BA方案表述为在不同策略下最小化平均传输波束宽度的期望值。将策略定义为接收反馈集与发射波束集(TB)之间的函数。为了使可能的反馈序列数量最大化,我们证明了扫描波束集具有一种特殊的形式,即郁金香设计。因此,我们将最小化问题重写为一组线性约束和减少变量的问题,并使用一种高效的贪心算法来求解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-user Beam Alignment in Presence of Multi-path
To overcome the high pathloss and the intense shadowing in millimeterwave (mmWave) communications, effective beamforming schemes are required which incorporate narrow beams with high beamforming gains. The mm Wave channel consists of a few spatial clusters each associated with an angle of departure (AoD). The narrow beams must be aligned with the channel AoDs to increase the beamforming gain. This is achieved through a procedure called beam alignment (BA). Most of the BA schemes in the literature consider channels with a single dominant path while in practice the channel has a few resolvable paths with different AoDs, hence, such BA schemes may not work correctly in the presence of multi-path or at the least do not exploit such multi path to achieve diversity or increase robustness. In this paper, we propose an efficient BA schemes in presence of multi-path. The proposed BA scheme transmits probing packets using a set of scanning beams and receives the feedback for all the scanning beams at the end of probing phase from each user. We formulate the BA scheme as minimizing the expected value of the average transmission beamwidth under different policies. The policy is defined as a function from the set of received feedback to the set of transmission beams (TB). In order to maximize the number of possible feedback sequences, we prove that the set of scanning beams (SB) has an special form, namely, Tulip Design. Consequently, we rewrite the minimization problem with a set of linear constraints and reduced number of variables which is solved by using an efficient greedy algorithm.
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