Future intelligent indoor wireless environments require fast and reliable beam alignment to sustain high-throughput links under mobility and blockage. Exhaustive beam training achieves optimal performance but is prohibitively costly. In indoor settings, dense scatterers and transceiver hardware imperfections introduce multipath and sidelobe leakage, producing measurable power across multiple angles and reducing the effectiveness of outdoor-oriented alignment algorithms. This paper presents a Refined Bayesian Optimization (R-BO) framework that exploits the inherent structure of mmWave transceiver patterns, where received power gradually increases as the transmit and receive beams converge toward the optimum. R-BO integrates a Gaussian Process (GP) surrogate with a Matern kernel and an Expected Improvement (EI) acquisition function, followed by a localized refinement around the predicted optimum. The GP hyperparameters are re-optimized online to adapt to irregular variations in the measured angular power field caused by reflections and sidelobe leakage. Experiments across 43 receiver positions in an indoor laboratory demonstrate 97.7% beam-alignment accuracy within 10 degrees, less than 0.3 dB average loss, and an 88% reduction in probing overhead compared to exhaustive search. These results establish R-BO as an efficient and adaptive beam-alignment solution for real-time intelligent indoor wireless environments.

翻译：未来的智能室内无线环境需要快速可靠的波束对准，以在移动和遮挡条件下维持高吞吐量链路。穷举式波束训练可实现最优性能，但其成本过高。在室内环境中，密集散射体和收发器硬件缺陷会引入多径和旁瓣泄漏，导致在多个角度上产生可测量的功率，从而降低了面向室外场景的对准算法的有效性。本文提出了一种精细化贝叶斯优化框架，该框架利用毫米波收发器波束图的固有结构，即当发射和接收波束向最优方向收敛时，接收功率会逐渐增加。R-BO 集成了采用 Matern 核函数的高斯过程代理模型和期望提升采集函数，并在预测的最优点周围进行局部精细化。高斯过程的超参数通过在线重新优化，以适应由反射和旁瓣泄漏引起的测量角度功率场的不规则变化。在室内实验室中对 43 个接收器位置进行的实验表明，该方法在 10 度范围内实现了 97.7% 的波束对准精度，平均损耗小于 0.3 dB，并且与穷举搜索相比，探测开销减少了 88%。这些结果确立了 R-BO 作为一种高效且自适应的波束对准解决方案，适用于实时智能室内无线环境。