Indoor–Outdoor User Detection in Cellular Networks: A Kalman Filtering and Likelihood Ratio Testing Approach

Reliable indoor–outdoor user detection is essential for optimizing resource allocation and network performance in cellular networks. While Artificial Intelligence (AI)-based techniques have gained attention, statistical signal processing remains a robust and interpretable approach. This paper presents a lightweight and practical detection framework that leverages signal quality metrics, specifically the signal-to-interference-plus-noise ratio (SINR). To enhance detection accuracy, a Kalman filter is employed to mitigate signal fluctuations while preserving consistency. Furthermore, a likelihood-based hypothesis test is introduced to improve indoor user classification. Simulation results show that the proposed detector achieves a recall of up to 99% and F1-scores of around 98% while maintaining prescribed false-alarm targets. Compared with simple SINR thresholding, the Kalman-regularized generalized likelihood ratio test (GLRT) provides substantially improved robustness under stringent false-alarm constraints while retaining constant-time inference complexity.