Iterative Eigenvalue Decomposition and Multipath-Grouping Tx/Rx Joint Beamformings for Millimeter-Wave Communications

Iterative Eigenvalue Decomposition and Multipath-Grouping Tx/Rx Joint Beamformings for Millimeter-Wave Communications
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用于毫米波通信的迭代特征值分解和多路径分组 Tx/Rx 联合波束成形

DOI:
10.1109/twc.2014.2370637
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发表时间:
2015-03
影响因子:
10.4
通讯作者:
Ning Ge
Ning Ge
中科院分区:
计算机科学1区
文献类型:
--
作者:
Zhenyu Xiao;Xiang-Gen Xia;Depeng Jin;Ning Ge

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我们研究了毫米波通信(MMWC)中的Tx/Rx联合波束形成。由于多径分量(mpc)具有不同的转向角和独立的衰落,波束形成的目标是在这种情况下实现阵列增益和分集增益。提出了一种基于迭代特征值分解(EVD)的次优波束形成方案,在发送端和接收端均可获得全信道状态信息的情况下,通过迭代特征值分解(EVD)找到Tx/Rx处的天线权重向量(awv)。为了使该方案在MMWC中切实可行,提出了相应的训练方法,以避免信道估计和迭代EVD计算。在快速衰落场景下,由于训练频繁,训练方法可能会耗费大量时间;提出了一种利用MMWC准静态转向角的波束形成方案,通过多径分组(MPG)降低系统开销,提高系统可靠性。该方案首先对多模组进行分组,然后对分组多模组的多个转向角同时进行波束形成,从而同时获得阵列增益和分集增益。性能比较结果表明,与现有方案相比,采用训练方法的迭代EVD方案在降低开销和复杂度的情况下获得了相同的性能,而MPG方案在复杂度近似相等的情况下获得了更好的性能。
We investigate Tx/Rx joint beamforming in millimeter-wave communications (MMWC). As the multipath components (MPCs) have different steering angles and independent fadings, beamforming aims at achieving array gain and diversity gain in this scenario. A sub-optimal beamforming scheme is proposed to find the antenna weight vectors (AWVs) at Tx/Rx via iterative eigenvalue decomposition (EVD), provided that full channel state information (CSI) is available at both the transmitter and receiver. To make this scheme practically feasible in MMWC, a corresponding training approach is suggested to avoid the channel estimation and iterative EVD computation. As in fast fading scenario, the training approach may be time-consuming due to frequent training; another beamforming scheme, which exploits the quasi-static steering angles in MMWC, is proposed to reduce the overhead and increase the system reliability by multipath grouping (MPG). The scheme first groups the MPCs and then concurrently beamforms toward multiple steering angles of the grouped MPCs, so that both array gain and diversity gain are achieved. Performance comparisons show that, compared with the corresponding state-of-the-art schemes, the iterative EVD scheme with the training approach achieves the same performance with a reduced overhead and complexity, whereas the MPG scheme achieves better performance with approximately equivalent complexity.
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