Adaptive Beamforming Optimization for Ultra-Wideband Bluetooth Mesh Networks Utilizing Reinforcement Learning and Multi-Objective Genetic Algorithms

**Abstract:** Ultra-Wideband (UWB) Bluetooth Mesh networks offer significant advantages in indoor positioning and localized control applications, but suffer from challenges related to interference mitigation and energy efficiency, particularly in dense deployments. This paper introduces a novel adaptive beamforming optimization framework leveraging Reinforcement Learning (RL) combined with multi-objective Genetic Algorithms (MOGA) specifically tailored for UWB Bluetooth Mesh networks. The framework dynamically adjusts beamforming weights to maximize signal strength while minimizing interference and energy consumption, resulting in demonstrably improved network performance and energy efficiency compared to traditional static or reactive beamforming approaches. This solution is readily implementable with current Bluetooth Mesh protocols and hardware, facilitating rapid adoption within existing and emerging IoT ecosystems.