Indoor–Outdoor User Detection in Cellular Networks: A Kalman Filtering and Likelihood Ratio Testing Approach
Year: 2026
Type: article
Abstract: Reliable indoor-outdoor user detection is essential for optimizing resource allocation and network performance in cellular networks. While AI-based techniques have gained attention, statistical signal processing remains a robust and interpretable approach. This paper presents a novel 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 statistical consistency. Furthermore, a likelihood-based hypothesis test is introduced to improve indoor user classification. Simulation results validate the proposed approach, demonstrating high detection accuracy and low false alarm rates. The proposed method provides a practical and computationally efficient solution for next-generation network optimization.
Source: Electronics
Authors: Hamidreza Khaleghi, Thierry Lucidarme
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7135092913