发表机构
School of Information and Electronics, Beijing Institute of Technology; National Mobile Communication Research Laboratory, School of Information Science and Engineering, Southeast University; Purple Mountain Laboratories; Department of Electrical and Computer Engineering, National University of Singapore（北京理工大学信息与电子学院; 东南大学信息科学与工程学院移动通信国家重点实验室; 紫金山实验室; 新加坡国立大学电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对6G网络中无人机下行通信的波束对齐问题，提出物理信息扩散框架BeamCKMDiff，构建连续波束感知信道知识图，实现端到端连续波束成形与主动双基站切换，在NLoS条件下提升频谱效率并消除链路中断。
AI 中文摘要
在第六代（6G）网络中，从基站（BS）到无人飞行器（UAV）的下行通信需要精确的波束对齐，以克服严重的移动通信路径损耗。然而，传统的穷举波束扫描依赖于离散码本，并消耗宝贵的空中接口资源进行在线测量，使其在高度动态的空中环境中效率低下。本文提出BeamCKMDiff，一种物理信息生成式扩散框架，旨在构建高保真度的连续波束感知信道知识图（BeamCKMs）。与现有经验方法不同，BeamCKMDiff采用扩散变换器（DiT）架构，具有双路径条件机制。它将解析视距（LoS）波束先验与环境拓扑相结合，同时通过自适应层归一化（adaLN）机制嵌入连续波束成形向量。基于这种可微分的生成映射，我们提出了一种基于CKM的端到端连续波束成形和主动双基站切换算法。通过解析地沿反向扩散过程传播梯度，所提出的框架能够在计算域中实现基于连续凸近似（SCA）的波束成形优化，有效绕过物理导频扫描。仿真结果表明，BeamCKMDiff实现了-23.96 dB的归一化均方误差（NMSE），建立了高度可靠的空间先验。与离散波束扫描和假设LoS的基线相比，所提出的基于CKM的波束成形能够适应非视距（NLoS）条件，提供精确的波束对齐和更高的频谱效率。此外，集成的双基站切换机制消除了由阻塞引起的链路中断，确保高机动性空中平台的稳健连接。
英文摘要
Downlink communications from base stations (BSs) to unmanned aerial vehicles (UAVs) in sixth-generation (6G) networks require precise beam alignment to overcome severe mobile communication path loss. However, traditional exhaustive beam sweeping relies on discrete codebooks and consumes valuable air-interface resources for online measurements, rendering it inefficient for highly dynamic aerial environments. In this paper, we propose BeamCKMDiff, a physics-informed generative diffusion framework designed to construct high-fidelity continuous beam-aware channel knowledge maps (BeamCKMs). Unlike existing empirical approaches, BeamCKMDiff employs a diffusion transformer (DiT) architecture featuring a dual-path conditioning mechanism. It integrates an analytical line-of-sight (LoS) beam prior with environmental topologies, while simultaneously embedding continuous beamforming vectors through an adaptive layer normalization (adaLN) mechanism. Building upon this differentiable generative mapping, we propose a CKM-based end-to-end continuous beamforming and proactive dual-BS handover algorithm. By analytically propagating gradients through the reverse diffusion process, the proposed framework enables successive convex approximation (SCA)-based beamforming optimization in the computational domain, effectively bypassing physical pilot scanning. Simulation results demonstrate that BeamCKMDiff achieves a normalized mean square error (NMSE) of --23.96 dB, establishing a highly reliable spatial prior. Compared to discrete beam sweeping and LoS-assumed baselines, the proposed CKM-based beamforming adapts to non-LoS (NLoS) conditions, providing accurate beam alignment and higher spectral efficiency. Furthermore, the integrated dual-BS handover mechanism eliminates blockage-induced link outages, ensuring robust connectivity for high-mobility aerial platforms.
