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AREA CLASSIFICATION FOR RF SIGNALS: A GRAPH NEURAL NETWORKS APPROACH
PhD Thesis Proposal Defence Title: "AREA CLASSIFICATION FOR RF SIGNALS: A GRAPH NEURAL NETWORKS APPROACH" by Mr. Weipeng ZHUO Abstract: We study the problem of area classification for radio frequency (RF) signals, where ambient RF signals, sampled by mobile Internet of Things (IoT) devices of unknown location, are to be identified by the areas they are collected. The solutions of the problem are applied in geofencing (for in-out area detection) and floor identification (for indoor localization) tasks. We consider the general but challenging scenario that the RF signals may exhibit significant spatial and temporal heterogeneities. By modelling the heterogeneous RF signals as a bipartite graph, we develop novel efficient graph neural network (GNN) approaches for the tasks. In geofencing, the ambient signals of a geofencing area is first collected for offline training. In online process, the system would detect in real time whether the signals, as collected and reported by a wearable attached to a user, are outside the area or not. We propose GEM, a geofencing approach based on network embedding for offline training and online detection. GEM efficiently processes the RF signals using a novel GNN called BiSAGE, and captures the multi-modal signal distribution within the area based on an enhanced histogram-based classification model. Extensive experimental results show that GEM outperforms state-of-the-art algorithms significantly (by up to 34% in F-score). For floor identification, we first consider the case where RF signals weakly spill over across floors (e.g., office buildings). A large number of signals are crowdsourced from users, and only a small number of them are labelled with their floors. We propose GRAFICS, a graph embedding-based floor identification system for the unlabelled signals. GRAFICS integrates a novel graph embedding algorithm E-LINE to capture the signal relationship and to efficiently classify the signals into different floors. It achieves higher than 96% in F-score with much fewer labels per floor as compared with other advanced. For floor identification with more substantial signal spillover (e.g., shopping malls), we propose FIS-ONE, a novel floor identification system that requires only one labelled signal record. FIS-ONE groups RF signals into different clusters using our proposed RF-GNN, and then indexes the clusters with floor numbers given the one labelled sample using a travelling salesman problem formulation. Our extensive experiments demonstrates that FIS-ONE outperforms other state-of-the-art algorithms substantially. Date: Friday, 1 September 2023 Time: 3:15pm - 5:15pm Venue: Room 5510 lifts 25/26 Committee Members: Prof. Gary Chan (Supervisor) Prof. Andrew Horner (Chairperson) Prof. Pedro Sander Prof. Pan Hui (EMIA) **** ALL are Welcome ****