Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for large antenna systems. Mobile systems like 5G use a beam management framework that joins the initial access, beamforming, CSI acquisition, and data transmission. The design of codebooks for these stages, however, is challenging due to their interrelationships, varying array sizes, and site-specific channel and user distributions. Furthermore, beam management is often focused on single-sector operations while ignoring the overarching network- and system-level optimization. In this paper, we proposed an end-to-end learned codebook design algorithm, network beamspace learning (NBL), that captures and optimizes codebooks to mitigate interference while maximizing the achievable performance with extremely large hybrid arrays. The proposed algorithm requires limited shared information yet designs codebooks that outperform traditional codebooks by over 10dB in beam alignment and achieve more than 25% improvements in network spectral efficiency.

翻译：获取准确且及时的通道状态信息（CSI）是大规模天线系统面临的基本挑战。5G等移动系统采用波束管理框架，将初始接入、波束赋形、CSI采集和数据传输相结合。然而，由于各阶段相互关联、阵列尺寸各异以及站点特定的信道和用户分布，设计适用于这些阶段的码本极具挑战性。此外，波束管理通常侧重于单扇区操作，而忽略了全局的网络级和系统级优化。本文提出了一种端到端学习的码本设计算法——网络波束空间学习（NBL），该算法通过捕获并优化码本来抑制干扰，同时在采用超大规模混合阵列时最大化可实现性能。所提出的算法仅需有限的共享信息，即可设计出在波束对准方面优于传统码本超过10dB、网络频谱效率提升超过25%的码本。