  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.
