<ul><li>Radio-based localization in dynamic environments, such as urban and vehicular settings, requires systems that can efficiently adapt to varying signal conditions and environmental changes.</li><li>This work presents an adaptive localization framework that combines shallow attention-based models with a router/switching mechanism based on a single-layer perceptron (SLP).</li><li>The framework utilizes three low-complexity localization models optimized for different scenarios, allowing seamless adaptation to diverse deployment conditions.</li><li>Real-world vehicle localization data collected from a massive MIMO base station (BS) validates the framework's ability to maintain high localization accuracy.</li></ul>

Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization

Discover more