The challenging propagation environment, combined with the hardware limitations of mmWave systems, gives rise to the need for accurate initial access beam alignment strategies with low latency and high achievable beamforming gain. Much of the recent work in this area either focuses on one-sided beam alignment, or, joint beam alignment methods where both sides of the link perform a sequence of fixed channel probing steps. Codebook-based non-adaptive beam alignment schemes have the potential to allow multiple user equipment (UE) to perform initial access beam alignment in parallel whereas adaptive schemes are favourable in achievable beamforming gain. This work introduces a novel deep learning based joint beam alignment scheme that aims to combine the benefits of adaptive, codebook-free beam alignment at the UE side with the advantages of a codebook-sweep based scheme at the base station. The proposed end-to-end trainable scheme is compatible with current cellular standard signaling and can be readily integrated into the standard without requiring significant changes to it. Extensive simulations demonstrate superior performance of the proposed approach over purely codebook-based ones.

翻译：毫米波系统面临的挑战性传播环境与硬件限制，使得需要开发具有低时延和高可实现波束赋形增益的精准初始接入波束对齐策略。近期该领域的研究主要集中于单侧波束对齐，或链路两侧执行固定信道探测序列的联合波束对齐方法。基于码本的非自适应波束对齐方案可允许多个用户设备并行执行初始接入波束对齐，而自适应方案在可实现波束赋形增益方面更具优势。本文提出一种新颖的基于深度学习的联合波束对齐方案，旨在结合用户侧无码本自适应波束对齐与基站侧码本扫描式波束对齐的优势。所提端到端可训练方案兼容现有蜂窝标准信令，可无缝集成至标准中而无需重大修改。大量仿真证明，本方案性能显著优于纯码本方案。