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  • 27
    Nov

    DEEPIS-IPS: OPTIMIZING WI-FI INDOOR POSITIONING SYSTEM WITH DEEP LEARNING AND INTELLIGENT SURFACES

    Date: November 27, 2025
    Time: 04:00 PM
    Location: EE Reading Room, Maxwell’s Wing, Second Floor, SBASSE, LUMS

    Supervisor: Dr Naveed ul Hassan
    Student(s): Aisha Javed

    Indoor positioning remains challenging due to the limitation of Global Navigation Satellite Systems (GNSS) in indoor environments. Wi-Fi based Indoor Positioning Systems (IPS) presents a practical alternative that leverages the widespread availability of Wi-Fi indoors. However, multipath effects, interference, and minimal control over wireless signals often limit their accuracy. Achieving high accuracy in these systems typically requires Wi-Fi fingerprinting, which involves creating a comprehensive database of Wi-Fi signals and matching real-time data to estimate locations. IPS systems rely on increasing the number of Access Points (APs) to enhance dataset dimensionality, which is costly and challenging to scale. Additionally, acquiring and maintaining this fingerprinting database is labor-intensive, often requiring extensive RF measurements, crowdsourced data, or complex RF propagation models.

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