Digital network twin (DNT) allows network operators to test their network management policy before their actual deployment in real-world networks. Achieving DNT, however, can be challenging and compute-intensive if every detail needs to be replicated exactly. In this work, we propose a new compute-efficient approach to realize DNT by augmenting existing network simulators. First, we build a real-world testbed by using OpenAirInterface and replicate its settings with the NS-3 simulator. Second, we observe the non-trivial distributional discrepancy between the simulator and the real-world testbed. Third, we use deep learning techniques to bridge the sim-to-real discrepancy under different network states. The experimental results show our method can reduce up to 91% sim-to-real discrepancy.

In this poster paper, we propose and demonstrate an architectural framework for service Management and Orchestration (M&O) in Sixth-Generation (6G) communication systems. This architecture was designed by the Hexa-X project, which is a European flagship project dedicated to developing a vision and technological enablers for 6G. To provide a comprehensive and high-level description, we consider three views: (i) Functional View; (ii) Structural View; and (iii) Deployment View. We first discuss 6G service M&O before delving deeper into each view.

Recently reconfigurable intelligent surface~(RIS) has been proposed as a promising technique to enhance the capacity of wireless networks by reshaping the electromagnetic characteristics of the environment. However, given numerous RIS elements, it is non-trivial to design an efficient beamforming scheme especially for the real-time mobile applications that require fast response to varying environments. In this paper, aiming to maximize the sum rate of a multi-user system via the RIS-enabled beamforming design, a meta-critic network is proposed to recognize the environment change and automatically perform the self-updating of the learning model. We also develop a stochastic Explore and Reload procedure to alleviate the high-dimensional action space issue. Simulation results demonstrate that the proposed scheme can converge to a higher sum rate more rapidly compared to the state-of-the-art methods in dynamic settings. The robustness of our proposed scheme against different RIS sizes is also verified.

Virtual Reality is thought to be the prototype of the next-generation Internet, consisting of more I/O devices and interactive methods than traditional mobile systems. Hence VR developers need to inform users what data is collected and shared, which is generally conveyed by privacy policies. Existing research has examined the consistency between the VR app's privacy policy and its corresponding actual behaviors. However, few studies paid attention to the data minimization principle, i.e., whether a privacy policy claims to collect no more data than it practically needs to implement the app's functionalities. In this poster, we targeted a mainstream VR platform and analyzed the data minimization principle compliance of privacy policies for all 1,726 VR apps in this platform. Experiment results show that 48.1% VR apps potentially violate the data minimization principle. Moreover, the comparative experiments reveal significant differences in the distribution of data collection between VR and non-VR apps.

We present MilliPED, a system that uses a millimeter-wave device to identify pedestrians at traffic intersections and enhance road safety during inclement weather, such as low visibility and heavy rain, when vision cameras are ineffective. We evaluate it with 3000 millimeter-wave reflection samples of pedestrian crossing traffic intersections and show that accurate pedestrian detection is feasible with millimeter-wave devices.

In the context of control systems over a communication network, status updates can be discarded based on their content to unload the network and prevent network congestion. In this work, we propose a transport layer scheme that not only considers the current system state, but also its significance w.r.t already transmitted updates, including those that are not yet acknowledged. The benefit of admitting a packet to be sent is compared to its transmission cost to obtain the value of update (VoU). Using Zolertia Re-Mote sensors, we show that the consideration of VoU allows improving the control performance by at least \(70\%\).

In this paper, we present a new approach for detecting wireless link layer anomalies in large-scale IoT networks based on graph neural networks (GNN). We propose a method that transforms time series data into graphs with Markov Transition Field representation. The transformed data is then used to train a new GNN architecture that can successfully distinguish between 4 different link-layer anomalies and outperforms state-of-the-art shallow and deep learning methods.

In this paper, we introduce Orbiting TCP (OrbTCP), a novel multipath data transport protocol for Low Earth Orbit (LEO) satellite networks. OrbTCP utilises in-network telemetry (INT) to obtain per-hop congestion information for each of its active subflows running on edge-disjoint paths. As a result, OrbTCP (1) enables network operation with low buffer capacity and low latency for end-hosts; (2) maximises application throughput and network utilisation; and (3) swiftly reacts to network hotspots due to bursty traffic or path reconfiguration. In this paper, we present early results showcasing the limitations of state-of-the-art data transport in LEO satellite networks, motivate the need for a novel data transport protocol and offer initial evidence that OrbTCP could overcome the identified limitations.

This paper proposes an Attention-enhanced ResNet based Wi-Fi Gesture Recognition (ARES-WiGR), in which it first extracts Doppler Frequency Shift (DFS) vector parameters from Channel State Information (CSI) via conjugate matrix multiplication and antenna pair selection. Then, the DFS spectrogram is obtained by Short Time Fourier Transform (STFT), and the DFS spectrogram features are used as the input of the proposed neural network model. Moreover, with the attention mechanism, ARES-WiGR is able to automatically recognize the important information and achieve better recognition effect. The performance is examined in real environment, which shows that the proposed ARES-WiGR effectively extracts gesture features and improves recognition accuracy to about 96%.

WiFi sensing systems have gained enormous interest in extensive areas, including vital sign monitoring. By using deep neural networks (DNNs), WiFi sensing systems can perform very well. However, the security and vulnerability of DNNs under adversarial attack would greatly influence the WiFi sensing performance. In this paper, we develop a DNN-based apnea detection system using WiFi channel state information (CSI) and then evaluate its robustness under three different attacks. The experimental results show that adversarial attacks can significantly impact the model performance, and the defense scheme (i.e. adversarial training) can improve the system robustness.