Nowadays, all the 4G/5G voice solutions are offered by the IMS (IP Multimedia Subsystem) system. They include 4G VoLTE (Voice over LTE) and 5G VoNR (Voice over New Radio), as well as a non-3GPP access solution, VoWiFi (Voice over WiFi). Since VoWiFi is implemented on mobile OS, instead of the modem with hardware security for VoLTE and VoNR, it can be a vulnerability of the IMS system and may further impair other IMS-based services. It has been exposed that due to vulnerable Vo WiFi sessions, several IMS vulnerabilities are discovered and the smartphones with IMS-based call services may suffer from a stealthy call DoS attack, where the smartphones cannot make or receive any calls and no ringtone or messages are appeared on them during the attack. In this paper, we develop a detector that can remotely and concurrently detect such DoS attack for multiple UEs (User Equipments). It consists of three major components: session hijacking, SIP fabrication, and call detection. We demonstrate its effectiveness in the operational networks of two carriers from different countries by considering three different phone models with VoLTE and VoWiFi call services.
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.
Wireless collaborative mixed reality (WCMR) has many fascinating applications in education, training, manu-facturing, and gaming. In this demo, we develop a WCMR-based firefighter training system that provides firefighters with experiences in extreme and diverse fire accidents without any safety concerns. In such a system, it is important to ensure that all firefighters can see almost the same status of the fire accident to facilitate collaborative training. This is challenging due to the heterogeneous communication delays of different network users. We propose the latency compensation algorithm that determines when the edge server should transmit the message to users based on the estimated latency of each user. Our experiment demon-strates around 55% synchronization performance improvement while guaranteeing at least 60 frames per second (FPS).
The infrastructure of mobile networks in 5G will offer various services in the form of network slices that can be deployed and implemented in a highly customizable manner. A real dynamic network with time-varying network utility has not been considered in previous works. In this paper, we examine the multi-resource allocation problem for network slicing in an online manner where the utility functions change over time. To solve the problems as mentioned above, we present Metis, a first systematic solution. Metis is an online network slice resource allocation framework that combines the time-varying property of the network utility function given the bandwidth and processing capacity constraints with the virtual network functions isolation requirements. As a result, we aim to maximize the cumulative network utility over time. Utilizing state-of-the-art concave optimization methods, we formulate the multi-resource allocation problem. To the best of our knowledge, this is the first work investigating an online method for multi-resources allocation for network slicing in a real-time network. Metis can proveably converge to the optimal solution, and the experiment results show a steady state behavior for Metis which converges in dynamic network settings.
With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years. To remedy the challenges of privacy preservation and decentralized power zones with strategic data owners, Federated Learning (FL) has contemporarily surfaced as a viable privacy-preserving alternative which enables collaborative training of attack detection models without requiring the sharing of raw data. To address some of the technical challenges associated with conventional synchronous FL, this paper proposes FeDiSa, a novel Semi-asynchronous Federated learning framework for power system faults and cyberattack Discrimination which takes into account communication latency and stragglers. Specifically, we propose a collaborative training of deep auto-encoder by Supervisory Control and Data Acquisition sub-systems which upload their local model updates to a control centre, which then perform a semi-asynchronous model aggregation for a new global model parameters based on a buffer system and a preset cut-off time. Experiments on the proposed framework using publicly available industrial control systems datasets reveal superior attack detection accuracy whilst preserving data confidentiality and minimizing the adverse effects of communication latency and stragglers. Furthermore, we see a 35% improvement in training time, thus validating the robustness of our proposed method.
The throughput of conventional and quantum network connections is an important performance metric, which is typically specified by bits per second (bps) and entangled quntum bits per second (ebps), respectively. It is measured over practical quantum network connections using specialized methods, and estimated using analytical bounds for which extensive theory has been developed. For practical connections, however, these two quantities have often been hard to correlate due to the lack of measurements and estimates derived under well-characterized common conditions. They both differ significantly from the conventional network throughput of TCP which employs buffers and loss recovery mechanisms. We describe a conventional-quantum testbed that enables the comparison of these two quantities both qualitatively and quantitatively. The bps and ebps throughput is measured over fiber connections of lengths over 75 kilometers, which show that the former decreases significantly slower with distance than the latter and in a qualitatively different way. The analytic capacity estimates of ebps are derived using approximations based on light intensity measurements, and they decrease more rapidly with distance than the measured ebps throughput. These results provide qualitative insights into the conventional transport mechanisms based on buffers, and the conditions used in deriving the analytical ebps capacity estimates.
Integrated sensing and communication (ISAC) has been an attractive solution to address the issues of spectrum shortage and hardware overhead through the coexistence of sensing and communication function. In ISAC, one of the most meaningful topics is communication-assisted sensing, namely, sensing-centered ISAC, where the sensing performance is enhanced by the improvement of communication efficiency between multiple sensing nodes. As a classic architecture of distributed sensing, federated learning (FL) based communication-assisted sensing is continuously discussed. Note that the huge number of parameters in deep neural network (DNN) applied to FL brings a heavy burden to the communication link between sensing node and central server, which even leads to the disconnecting of training clients. In this paper, a novel federated transfer learning (FTL) framework is proposed to address the above issue. More specifically, a task-general sub-model of DNN is pre-trained as feature extractor to accelerate learning and only the task-specific sub-model is aggregated to reduce the communication overhead in the framework. The performance of our proposed scheme is evaluated and the simulation results demonstrate that our algorithm outperforms the benchmark in terms of communication overhead and average test accuracy.
The orthogonal time frequency space (OTFS) symbol detector design for high mobility communication scenarios has received numerous attention lately. Current state-of-the-art OTFS detectors mainly can be divided into two categories; iterative and training-based deep neural network (DNN) detectors. Many practical iterative detectors rely on minimum-mean-square-error (MMSE) denoiser to get the initial symbol estimates. However, their computational complexity increases exponentially with the number of detected symbols. Training-based DNN detectors typically suffer from dependency on the availability of large computation resources and the fidelity of synthetic datasets for the training phase, which are both costly. In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector. DIP is a type of DNN that requires no training, which makes it beneficial in OTFS detector design. Then we propose to combine the D-DIP denoiser with the Bayesian parallel interference cancellation (BPIC) detector to perform iterative symbol detection, referred to as D-DIP-BPIC. Our simulation results show that the symbol error rate (SER) performance of the proposed D-DIP-BPIC detector outperforms practical state-of-the-art detectors by 0.5 dB and retains low computational complexity.
In this paper, we introduce Orbiting TCP (OrbTCP), a 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. 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. 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.
The5G-CLARITY project proposes a novel archi-tecture for private 5G networks that converges Wi-Fi 6, 5G NR and LiFi under a common service platform for Industry 4.0. In this demonstration, we deploy the SG-CLARITY system in a real factory setup and showcase its multi-connectivity framework, which allows to customize aggregation behavior for different devices. We demonstrate two different aggregation modes. First, a capacity aggregation mode that delivers between 200 Mbps and 600 Mbps to mobile devices throughout the factory floor. Second, a latency-sensitive aggregation mode that is used to replace Ethernet connectivity for a production line achieving end-to-end delays below 10 ms.
Distributed quantum computing is a promising solution for creating large-scale quantum computers. In such scenarios, quantum processing units (QPUs) are connected to each other via quantum and classical links. To increase the performance in such a distributed manner, and due to the fragile nature of quantum bits and their decoherence with time, the impact of classical links such as communication latency and jitter between QPUs shall be considered. Here, we propose a low-latency and time-deterministic FPGA-based network supporting execution of distributed quantum circuits. We focus on transmissions of measurement result and control messages as well as synchronization in a distributed network. We demonstrate that a message is transmitted with 361.60 ns between QPUs using optical Ethernet. Synchronization reaches 9.6 ns precision using only Ethernet frames and can reach 21 ps with an external clock. Further, a use-case example of an Inverse Quantum Fourier Transform is implemented to evaluate the impact in terms of latency for inter-QPU data transfers. Our theoretical error analysis and simulation results show that the latency added by our FPGA-controlled network has a negligible impact on the quantum algorithm performance for practical values of memory decoherence time.
This paper studies a self-sustainable reconfigurable intelligent surface (SRIS)-assisted mobile edge computing (MEC) network, where a SRIS first harvests energy from a hybrid access point (HAP), and then enhances the users' offloading performance with the harvested energy. To improve computing efficiency, a sum computation rate maximization problem is formulated. Based on the alternating optimization (AO) method, an efficient algorithm is proposed to solve the formulated non-convex problem. Simulations show that when the SRIS is deployed closer to the HAP, a higher performance gain can be achieved.
The space-air-ground integrated network (SAGIN) is dynamic and flexible, which can support transmitting data in environments lacking ground communication facilities. However, the nodes of SAGIN are heterogeneous and it is intractable to share the resources to provide multiple services. Therefore, in this paper, we consider using network function virtualization technology to handle the problem of agile resource allocation. In particular, the service function chains (SFCs) are constructed to deploy multiple virtual network functions of different tasks. To depict the dynamic model of SAGIN, we propose the reconfigurable time extension graph. Then, an optimization problem is formulated to maximize the number of completed tasks, i.e., the successful deployed SFC. It is a mixed integer linear programming problem, which is hard to solve in limited time complexity. Hence, we transform it as a many-to-one two-sided matching game problem. Then, we design a Gale-Shapley based algorithm. Finally, via abundant simulations, it is verified that the designed algorithm can effectively deploy SFCs with efficient resource utilization.
Maritime transport industry is a targeted sector for cyber security attacks as due to the inherent risks of transferring sensitive information for its smooth functioning. Digitization of maritime transport systems has led them vulnerable to various cybersecurity attacks. Vulnerable data, such as crew or passenger personal information, vessel location, route, schedule and other information can be obtained by the attackers and misused. To counter this, a novel lightweight authentication protocol has been put forward with the help of the drone technology using the 5th generation mobile network (5G) communication. The proposed scheme is analyzed to show its robustness against various security attacks, while consuming low communication and computation costs and achieving security and functionality requirements of anonymity and untraceability properties. A detailed simulation study using the network simulator (NS3) shows its impact on various network performance parameters.
Deep learning (DL) based automatic modulation classification (AMC) has gained popularity for next-generation wireless communication systems. However, these DL-based AMC models are vulnerable to adversarial examples, which can cause false predictions with high confidence, leading to unreliable and non-robust communication networks. In this paper, we propose a data mapping-based adversarial defense scheme to address this issue. This scheme uses random split, time-domain flips, and phase rotations as three methods of data mapping on the input examples, effectively mitigating the impact of adversarial perturbations on the model's output and ensuring reliable model inference. Evaluation results on the RML2016.10a dataset demonstrate that the proposed defense scheme can effectively resist various white-box attacks and improve the robustness of the AMC model without requiring fine-tuning or incremental training. This scheme therefore offers a secure solution for intelligent communication networks.
This paper investigates a digital twin (DT) and reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system under given constraints on ultra-reliable low latency communication (URLLC). In particular, we focus on the problem of total end-to-end (E2E) latency minimization for the considered system under the joint optimization of beamforming design at the RIS, power, bandwidth allocation, processing rates, and task offloading parameters using DT architecture. To tackle the formulated non-convex optimization problem, we first model it as a Markov decision process (MDP). Later, we adopt deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) algorithm to solve it effectively. We have compared the DDPG results with proximal policy optimization (PPO), modified PPO (M-PPO), and conventional alternating optimization (AO) algorithms. Simulation results depict that the proposed DT-enabled resource allocation scheme for the RIS-empowered MEC network using DDPG algorithm achieves up to 60% lower transmission delay and 20% lower energy consumption compared to the scheme without an RIS. This confirms the practical advantages of leveraging RIS technology in MEC systems. Results demonstrate that DDPG outperforms M-PPO and PPO in terms of higher reward value and better learning efficiency, while M-PPO and PPO exhibit lower execution time than DDPG and AO due to their advanced policy optimization techniques. Thus, the results validate the effectiveness of the DRL solutions over AO for dynamic resource allocation w.r.t. reduced execution time.
The coordination of robotic swarms and the remote wireless control of industrial systems are among the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that needs to be shared over the wireless medium can overload even high-capacity connections. Consequently, solving the effective communication problem by optimizing the transmission strategy to discard irrelevant information can provide a significant advantage, but is often a very complex task. In this work, we consider a prototypal system in which an observer must communicate its sensory data to an actor controlling a task (e.g., a mobile robot in a factory). We then model it as a remote Partially Observable Markov Decision Process (POMDP), considering the effect of adopting semantic and effective communication-oriented solutions on the overall system performance. We split the communication problem by considering an ensemble Vector Quantized Variational Auto encoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level, considering both the current state of the environment and the memory of past messages. We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase over traditional approaches.
In this paper, we demonstrate the design of FiND, a novel neighbor discovery protocol that accelerates BLE neighbor discovery via Wi-Fi fingerprints without any hardware modifications. The design rationale of FiND is that the two modes of Wi-Fi and BLE show complementarity in both wireless interference and discovery pattern. When abstracting the neighbor discovery problem, this demonstration provides validation for the approach of reasoning-based presence detection in the real world.
Layer-7 load balancing is an essential pillar in modern enterprise infrastructure. It is inefficient to scale software layer-7 load balancing which requires hundreds of servers to meet the large-scale service requirements of 1 Tbps throughput and 1M concurrent requests. This paper presents L7LB with a novel fast path and slow path co-design architecture running on a heterogeneous programmable server-switch. L7LB is efficient by offloading most packets' forwarding onto the Tbps bandwidth switch chip, with few CPU cores processing application connections. The preliminary prototype demonstrates the layer-7 load balancing functionality and shows that L7LB can meet the large-scale service requirements.
5G aims to support ubiquitous connectivity, ultra-Reliable Low Latency (uRLLC), and massive device communication in Next Generation networks. To achieve these objectives, the Open-Radio Access Networks (O-RAN) alliance aims to decouple the Radio Access Network (RAN) architecture and allow heterogeneity. To ensure the services' requirements, it is necessary to guarantee solutions that improve the management of the network. This work proposes an End-to-End (E2E) orchestration framework for a 5G communication infrastructure with open-source components. An overview of the implemented architecture is presented and two demonstrations are shown: how RAN and Core Network metrics are retrieved using a monitoring xAPP, and how the orchestrator enforces a policy after processing and analysing the data gathered. The results show that it is possible to deploy the proposed architecture to monitor and allocate resources efficiently in near-Real Time (near-RT) environments. The major novelty of this work is the fact that this constitutes the first E2E 5G network system using open-source tools, to the best of our knowledge. For this purpose, an interface adapter was built to interlink some of these open-source components.
