Network as Sensor - Aravinda Prabhu,Nisha Prabhu; Köhn,Miranda (2025)
The thesis introduces a concept of leveraging advanced 5G, or even future 6G networks, as powerful sensing systems. By utilizing communication network infrastructure, it explores the potential of deploying transmitter and receiver nodes at different locations, thus enabling the detection of Non-Line-of-Sight (NLOS), passive, dynamic objects transforming the network into dual-purpose communication and sensing platforms. This thesis presents a novel approach to human fall detection in an indoor environment and cyclist tracking in an outdoor environment, using advanced signal processing techniques. For robust fall detection, key scenarios like walking, sit-to-stand transitions, falling, and static postures are crucial indicators that the network must accurately identify to ensure its effectiveness and reliability in real-world applications. Utilizing MATLAB for simulations, the research integrates the beam steering capability of a custom-designed patch microstrip antenna array operating at a chosen set of carrier frequencies, the ray tracing technique and machine learning algorithms. The antenna arrays, including both transmit and receive units, are designed to optimize detection capabilities of passive objects. Two optimally chosen waveforms are passed through a simulated propagation environment, including multi-path effects, and are subsequently received and processed. Channels and noise are modeled using Ray Tracing Channels (RTC) and Additive White Gaussian Noise (AWGN). The machine learning process involves segmenting the signal data, assigning labels based on predefined categories, and training a Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) using Doppler signatures to ensure robust performance. The network architecture includes layers designed for feature extraction and classification, optimized using the Adam optimizer. The results demonstrate the efficacy of the proposed system in accurately detecting falls, with high classification accuracy confirmed by confusion matrix analysis and Cumulative Distribution Function (CDF) plot for positioning error. This work contributes to the field of health monitoring by providing a reliable and efficient method for human fall detection.