基于耦合高斯混合模型的轻量波束索引图
Lightweight Beam Index Map Using Coupled Gaussian Mixture Models
AI总结：
该研究针对MIMO系统波束对齐问题，提出基于耦合高斯混合模型的轻量机器学习方法，构建波束索引图，在降低复杂度开销的同时，性能优于聚类指纹法且接近穷举搜索，适用于资源受限设备。
AI中文摘要：
本文从以移动终端（MT）为中心的去中心化视角，解决多输入多输出（MIMO）系统中的波束对齐问题。我们提出一种轻量机器学习方法，利用位置信息执行波束选择，无需依赖穷举搜索或强基站协调。具体而言，我们采用耦合高斯混合模型（GMM）对MT位置与信道观测值的联合分布进行建模，从而构建出将空间位置与码本条目直接关联的波束索引图（BIM）。为兼顾实际硬件约束，我们引入了一种优化流程，使学习到的统计模型适配固定码本。该方法计算效率高，适用于资源受限设备的部署。在DeepMIMO和QuaDRiGa数据集上的仿真结果表明，所提方法的性能优于基于聚类的指纹方法，与穷举搜索相比具有竞争力，同时大幅降低了复杂度和开销。
英文摘要：
This paper addresses the beam alignment problem in MIMO systems from a decentralized, mobile terminal (MT)-centric perspective. We propose a lightweight machine learning approach that leverages position information to perform beam selection without relying on exhaustive search or strong base station coordination. Specifically, we model the joint distribution of MT positions and channel observations using a coupled Gaussian mixture model (GMM), enabling the construction of a beam index map (BIM) that directly associates spatial locations with codebook entries. To account for practical hardware constraints, we introduce a refinement procedure that adapts the learned statistical model to fixed codebooks. The resulting method is computationally efficient and suitable for deployment on resource-constrained devices. Simulation results on the DeepMIMO and QuaDRiGa datasets demonstrate that the proposed approach outperforms clustering-based fingerprinting methods and achieves competitive performance compared to exhaustive search, while significantly reducing complexity and overhead.
