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.

翻译：波束对齐是毫米波通信中的关键瓶颈问题。理想的波束对齐技术应能以低时延实现高波束赋形增益，良好扩展至更高载波频率、更大天线阵列及多用户设备系统，且无需获取难以获得的上下文信息。现有方法普遍缺乏这些特性。我们摒弃了从量化码本中选择最优波束的传统基于码本的方法，转而提出一种无网格波束对齐方法：该方法利用通过深度学习流水线获得的少量站点特化探测波束测量值，直接从连续搜索空间中合成发射和接收波束。在实际场景中，与基于码本的基准方法相比，所提方法实现了远超后者的信噪比-时延权衡：相对于传统码本的穷举搜索，该方法能在快100倍的速度下对齐近最优波束，或在相同搜索次数下平均获得信噪比高10-15 dB的波束。