.png){kind=icon}
返回
Spatial-Chirp Codebook-Based Hierarchical Beam Training for Extremely Large-Scale Massive MIMO
.png){kind=icon}
OA
AI
DOI：10.1109/TWC.2023.3303229.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Training
Array signal processing
Wireless communication
Antennas
Millimeter wave communication
Degradation
Optimization
XL-MIMO
beamforming design
hierarchical beam training
near-field
training overhead
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
The bonding of H2 in W(CO)5(H2), a reactive intermediate stabilized and identified in liquid xenon
PREAI
分享收藏Is skin closure with cyanoacrylate glue effective for the prevention of sternal wound infections?
OAAI
OAAI分享收藏Triple-Structured Compressive Sensing-Based Channel Estimation for RIS-Aided MU-MIMO SystemsRIS辅助mu-mimo系统中基于三结构压缩感知的信道估计OAAI
OAAI分享收藏Codebook design and beam training for extremely large-scale RIS: Far-field or near-field?.png)
CHINA COMMUNICATIONSOAAI
CHINA COMMUNICATIONSIF3.1
OAAI分享收藏
查看更多内容