论文标题
通过特定网站的深度学习，无网格的MIMO光束对齐
Grid-Free MIMO Beam Alignment through Site-Specific Deep Learning
论文作者
论文摘要
光束对齐是毫米波（MMWave）通信中的关键瓶颈。理想的光束对准技术应以低潜伏期的形式获得远光度（BF）增益，良好地扩展到具有较高的载波频率，较大的天线阵列和多个用户设备（UES）的系统，并且不需要难以实现的上下文信息（CI）。这些品质在现有方法中统一缺乏。我们脱离了基于代码的传统方法（CB）方法，其中从量化的代码簿中选择了最佳光束，而是提出了一种无网束（GF）光束对齐方法，该方法通过从连续的探测梁中通过几个站点特定的探测梁从连续的搜索空间中直接合成传输（TX）和接收（RX）光束，该测量值通过一些深度学习（DL）的管道（dl）。在现实的环境中，所提出的方法的信噪比（SNR）的差额比CB基线相比要取消了差异要高得多：相对于相同数量的搜索，相对于常规搜索而言，它使近乎最佳的光束比对齐近距离梁更快地或等效地找到了梁的平均SNR。
Beam alignment is a critical bottleneck in millimeter wave (mmWave) communication. An ideal beam alignment technique should achieve high beamforming (BF) gain with low latency, scale well to systems with higher carrier frequencies, larger antenna arrays and multiple user equipments (UEs), and not require hard-to-obtain context information (CI). 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 (GF) beam alignment method that directly synthesizes the transmit (Tx) and receive (Rx) beams from the continuous search space using measurements from a few site-specific probing beams that are found via a deep learning (DL) 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 better average SNR in the same number of searches, relative to an exhaustive search over a conventional codebook.
