.png){kind=icon}
返回
Grid-Free MIMO Beam Alignment Through Site-Specific Deep Learning
.png){kind=icon}
OA
AI
DOI：10.1109/TWC.2023.3283475.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Beam alignment is a critical bottleneck in millimeter wave communication. An ideal beam alignment technique should achieve high beamforming gain with low latency, scale well to systems with higher carrier frequencies, larger antenna arrays and multiple user equipment, and not require hard-to-obtain context information. These qualities are collectively lacking in existing methods. We depart from the conventional codebook-based (CB) approach where the optimal beam is chosen from quantized codebooks and instead propose a grid-free beam alignment method that directly synthesizes the transmit and receive beams from the continuous search space using measurements from a few site-specific probing beams found via a deep learning pipeline. In realistic settings, the proposed method achieves a far superior signal-to-noise ratio (SNR)-latency trade-off compared to the CB baselines: it aligns near-optimal beams 100x faster or equivalently finds beams with 10-15 dB higher average SNR in the same number of searches, relative to an exhaustive search over a conventional codebook.
Keyword:
5G mobile communication
beam management
deep learning
millimeter wave communication
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Hierarchical Codebook-Based Multiuser Beam Training for Millimeter Wave Massive MIMO基于分层码本的毫米波大规模MIMO多用户波束训练
PREAI
分享收藏Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems深度学习在TDD大规模MIMO OFDM系统信道感知和混合预编码中的应用OAAI
OAAI分享收藏分享收藏Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination5g网络的深度强化学习: 联合波束成形、功率控制和干扰协调
OAAI
OAAI分享收藏
查看更多内容