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[2512.00036] Refined Bayesian Optimization for Efficient Beam Alignment in Intelligent Indoor Wireless Environments
Summary
This article presents a refined Bayesian optimization framework for efficient beam alignment in intelligent indoor wireless environments, achieving high accuracy with reduced overhead.
Why It Matters
As wireless communication demands increase, efficient beam alignment is crucial for maintaining high-throughput links in indoor environments. This research addresses the challenges posed by mobility and physical obstructions, offering a novel solution that enhances performance while minimizing resource usage.
Key Takeaways
- Introduces a refined Bayesian optimization (R-BO) framework for beam alignment.
- Achieves 97.7% beam-alignment accuracy within 10 degrees.
- Reduces probing overhead by 88% compared to exhaustive search methods.
- Utilizes Gaussian Process surrogate modeling for adaptive optimization.
- Demonstrates effectiveness in real-time indoor wireless environments.
Computer Science > Networking and Internet Architecture arXiv:2512.00036 (cs) [Submitted on 12 Nov 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Refined Bayesian Optimization for Efficient Beam Alignment in Intelligent Indoor Wireless Environments Authors:Parth Ashokbhai Shiroya, Amod Ashtekar, Swarnagowri Shashidhar, Mohammed E. Eltayeb View a PDF of the paper titled Refined Bayesian Optimization for Efficient Beam Alignment in Intelligent Indoor Wireless Environments, by Parth Ashokbhai Shiroya and 3 other authors View PDF HTML (experimental) Abstract: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 hyperp...
