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arXiv 2210.03345cs.ITeess.SPmath.IT

面向超大规模Massive MIMO的基于空间啁啾码本的分层波束训练

Spatial-chirp Codebook-based Hierarchical Beam Training for Extremely Large-Scale Massive MIMO

Xu Shi, Jintao Wang, Zhi Sun, Jian Song

AI总结:

针对近场XL-MIMO中角度与距离联合波束训练开销大的问题,提出基于空间啁啾码本的分层训练及流形优化增强方案,显著降低开销并提升波束赋形性能。

摘要:

超大规模多输入多输出(XL-MIMO)有望在毫米波(mmWave)和太赫兹(THz)频谱中提供超高数据速率。然而,由大孔径阵列引起的球面波前无线传输给信道状态信息(CSI)获取和波束赋形带来了巨大挑战。XL-MIMO波束赋形必须同时考虑物理角度和传输距离这两个独立参数,从而导致严重的开销消耗和波束赋形性能下降。为解决这一问题,我们利用近场信道特性,针对近场XL-MIMO系统提出了两种低开销分层波束训练方案。首先,我们将近场信道投影到空间-角度域和斜率-截距域,以捕获详细表示;随后指出XL-MIMO分层波束训练的三个关键准则。其次,提出了一种新型空间啁啾波束辅助码本及相应的分层更新策略。第三,鉴于空间啁啾波束的覆盖不完善和重叠问题,我们进一步通过流形优化和交替最小化设计了增强型分层训练码本。文中还给出了理论分析和数值仿真,以验证其在波束赋形和训练开销方面的优越性能。

英文摘要:

Extremely large-scale multiple-input multiple-output (XL-MIMO) promises to provide ultrahigh data rates in millimeter-wave (mmWave) and Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition and beamforming. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose two low-overhead hierarchical beam training schemes for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Then we point out three critical criteria for XL-MIMO hierarchical beam training. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Thirdly, given the imperfect coverage and overlapping of spatial-chirp beams, we further design an enhanced hierarchical training codebook via manifold optimization and alternative minimization. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.

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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