The timeliness of wide-band perception data sharing among connected automated vehicles (CAV) or unmanned aerial vehicles (UAV) is critical important to guarantee the safety of CAV fleet or UAV swarm with an enhanced environment perception ability. Due to the massive raw perception data generated by multiple on-board sensors, the millimeter wave(mmWave) communication technology shows the potential capability to support the high data rate perception data sharing among CAVs or UAVs. This paper proposes a camera sensing enabled three dimension beam tracking (CS-3DBT) algorithm to solve the fast and robust beam tracking problem in the high mobility scenario. The hardware testbed is developed and field test results verify that the proposed CS-3DBT algorithm can achieve a stable throughput of 2.8 Gbps and a smooth beam angle control with a latency of 20 ms.

The terahertz (THz) band (0.1 - 10 THz) will be instrumental in the next generation of wireless communication systems, largely due to its ability to provide 10s of gigahertz (GHz) of contiguous bandwidth. One of the main challenges facing THz communication systems is the high propagation losses experienced by THz signals. Multiple-input multiple-output (MIMO) systems have been suggested as a method to overcome the challenging propagation of THz signals. In this paper, an information bearing ultra-broadband 2x2 MIMO system above 100 GHz is built for the first time and utilized to explore the performance of transmit beamforming and maximal ratio combining in real-world setups.

Vulnerability to blockage and time-consuming beam tracking are two important issues yet to be solved in millimeter-wave (mmWave) communications systems. In this paper, we demonstrate stereo camera and LiDAR aided beam tracking and blockage prediction platforms for mmWave communications that do not cost in-band communications resources, e.g., time for pilot training and beam sweeping. In stereo camera aided platform, we perform beam tracking at a rate of 12 fps as well as frequency switching from mmWave to sub-6G right before the blockage happens. On the other side, LiDAR aided platform is mainly used to perform beam tracking under dark light environment, in which case the camera cannot accurately capture the vision information. It can be seen that the two platforms can establish the mmWave communications links with vision information only and can successfully predict the blockage.

Over the past decade, there has been fierce competitions between the singe carrier frequency domain equalization (SC-FDE) and OFDM systems. Efforts have been devoted in searching a better carrier scheme and waveform to deal with the complex channel environments. However, selecting one particular carrier paradigm to suit different channel scenarios is infeasible. In this paper, we propose a weighted fractional Fourier transform (WFRFT) based hybrid carrier scheme to integrate the existing SC-FDE and OFDM schemes under an unified physical layer architecture. By leveraging the concept of WFRFT, hybrid carrier (HC) scheme proposes a compatible process of SC-FDE and OFDM modulation. We demonstrate a running prototype based on the NI USRP and Xilinx's ZYNQ platform. The compatibility of the WFRFT-based HC scheme with conversational SC-FDE and OFDM architecture is presented. Experimental results validate the practicability and the carrier convergence capability of the proposed scheme.

Uplink non-orthogonal multiple access (UL-NOMA) is one of the promising techniques for future Wi-Fi. UL-NOMA improves spectral efficiency in a heterogeneous Wi-Fi network where the qualities of the channel between the access point and associated stations are significantly different. In UL-NOMA, various stations transmit several data streams to the access point in the same frequency and time resources. However, incorporating UL-NOMA in the Wi-Fi technology requires new channel estimation and phase compensation mechanisms as well as new frame formats that allow separate reception of multiple frames at the access point and ensure backward compatibility with existing Wi-Fi devices. This demo presents a first-ever prototype of the UL-NOMA Wi-Fi system and briefly evaluates its efficiency.

Time-sensitive networking (TSN) is utilized in industrial environments to support reliable low-latency communications. Bringing TSN features to wireless networks is getting traction recently by achieving time synchronization and traffic scheduling over wireless links. Besides these basic features, impact-less and network transparent association of prospective clients is paramount for wireless TSN. This demo presents an impactless TSN association procedure, where beacons are used to provide time synchronization and scheduling to prospective clients, where association procedure is done in reserved time slots. The presented demo is designed, implemented, and tested on top of a wireless Software Defined Radio platform using the IEEE802.11 standard. By utilizing a dashboard, this demo will demonstrate real-time control over association as well as high accuracy synchronization on client frame transmissions even with challenging scheduling configuration of 128 uS timeslots.

Wireless communication in the terahertz band (100 GHz to 10 THz) is envisioned as a critical building block of 6G wireless systems, due to the very large available channel band- width above 100 GHz. Thanks to the narrowing of the so-called terahertz technology gap, several platforms for experimental terahertz communication research have been recently developed. However, these are mostly channel sounding or physical layer testbeds that rely on off-line signal processing. In order to research and develop upper networking layers, its necessary to have a real-time platform that can process bandwidths of at least several gigahertz. In this paper, a software-defined radio platform that able to operate in real-time with up to 8 GHz of bandwidth at 130 GHz is demonstrated for the first time.

In this demonstration, we design the integration of WLAN-based RF transmission and multi-wavelength FSO transmission, achieving 20Gbps. Two wavelength beams of 1549.322nm and 1550.124nm are modulated and used for WLAN-based M-QAM OFDM signal by USRP and 10Gbps OOK signal by BERT. It is shown that when the received optical power is greater than -13dBm for multi-wavelength beams, the WDM-FSO system achieves the BER requirements of WLAN. Furthermore, for the 10Gbps OOK RF signal, the error-free condition of BER below 10e−12 is obtained at the received optical power of -13dBm. Our experiments study the feasibility of integrating WDM-FSO with WLAN-based RF systems, noticing the potential of utilizing multiple wavelengths in RoFSO transmission.

To overcome the bottleneck of sensing ability improvement in a single automated vehicle, the joint communication and sensing (JCS), as one of the potential sixth-generation wireless communication technologies, can support the efficient raw perception data sharing among connected automated vehicles in the millimeter wave frequency band. We propose a weighted mean accuracy based sensing data fusion algorithm to enhance the target positioning performance by sharing the perception data from two time division based JCS systems. Field test results prove that the proposed algorithm using two cooperative JCS systems can reduce the target positioning root mean square error by 31% compared to a single JCS system.

RF-based human pose estimation has attracted increasing interest in recent years. Compared with vision-based approaches, RF-based techniques can better protect user's privacy and are robust to the lighting and non-line-of-sight condition. However, due to complicated indoor propagation environments, most of the RF-based sensing approaches are sensitive to the deployment environment and hard to adapt to new environments. In this demo, we present a meta-learning-based approach to address the environment adaptation problem and design an environment-adaptive Radio-Frequency Identification (RFID) based 3D human pose tracking system. The system utilizes commodity RFID tags to estimate 3D human pose and leverage meta-learning algorithms to improve the environment adaptability. Experiments conducted in various environments demonstrate the high pose estimation performance and adaptability to environments.

Human activity recognition (HAR), as an essential component of many emerging smart applications, has attracted an increasing interest in recent years. Various radio-frequency (RF) sensing technologies, such as Radio-Frequency Identification (RFID), WiFi, and RF radar, have been utilized for developing non-invasive HAR systems. However, most of the RF based HAR solutions are closely designed for the specific, chosen RF technology, which incurs a significant barrier for the wide deployment of such systems. In this demo, we present a technology-agnostic approach for RF-based HAR, termed TARF, which aims to overcome such constraints and perform HAR with various RF sensing technologies.

Offloading the computation-intensive virtual reality (VR) frame rendering to the edge server is a promising approach to providing immersive VR experiences in mobile VR devices with limited computational capability and battery lifetime. However, edge-assisted VR systems require the data delivery at a high data rate, which poses challenges to the wireless communication between the edge and the device. In this demo, to reduce the communication resource consumption, we present PixSimVR, a pixel similarity-based content reuse framework for edge-assisted VR. PixSimVR analyzes the similarity of the pixels across different VR frames that correspond to different viewport poses, i.e., users' points of view in the virtual world. Based on the pixel similarity level, PixSimVR adaptively splits the VR content into the foreground and the background, reusing the background that has a higher similarity level across frames. Our demo showcases how PixSimVR reduces bandwidth requirements by adaptive VR content reuse. Demo participants will develop an intuition for the potential of exploiting the correlation between VR frames corresponding to similar viewport poses specifically, and for the promises and the challenges of edge-assisted VR as a whole. This demonstration accompanies [1].

Traditional intrusion detection systems often deal with massive alarms based on specific filtering rules, which is complex and inexplicable. In this demo, we developed a network security situation awareness (NSSA) system based on the spatio-temporal correlation of alarms. It can monitor the security situation from the temporal dimension and discover abnormal events based on the time series of alarms. Also, it can analyze alarms from the spatial dimension on the heterogeneous alarm graph and handle alarms in batches of events. With this system, system operators can filter most irrelevant alarms quickly and efficiently. The rich visualization of alarm data could also help find hidden high-risk attack behaviors.

Haptic teleoperation is typically realized through wired networking technologies (e.g., Ethernet) which guarantee performance of control loops closed over the communication medium, particularly in terms of latency, jitter, and reliability. This demonstration shows the capability of conducting haptic teleoperation over a novel low-power wireless control technology, called GALLOP, in a nuclear decommissioning use-case. It shows the viability of GALLOP for meeting latency, timeliness, and safety requirements of haptic teleoperation. Evaluation conducted as part of the demonstration reveals that GALLOP, which has been implemented over an off-the-shelf Bluetooth 5.0 chipset, can be a replacement for conventional wired TCP/IP connection, and outperforms WiFi-based wireless solution in same use-case.

The spectrum cartography plays a important role in the spectrum monitoring, management, and security. In this paper, we develop a prototype of UAV-based three-dimensional (3D) spectrum mapping system. It can autonomously fly with the optimized trajectory and capture electromagnetic data in the 3D space. By exploiting the propagation channel model and spatialtemporal correlation of raw data, we use a data-model jointly driven method to predict, complete, and merge the spectrum data. Then, the full radio map is built and displayed across multiple domains such as time, space and frequency. Users can apply the reconstructed map to detect abnormal spectral activities, locate the position of signal source, manage the radio frequency (RF) resource, and etc.

Mixed reality (MR) is a new paradigm that merges both real and virtual worlds to create new environments and visualizations. This together with the rapid growth of wireless virtual/augmented reality devices (such as smartphones and Microsoft HoloLens) spurs collaborative MR applications that provide an interactive and immersive experience for a group of people. In this demo, we develop a scalable MR-based platform for remote collaborative LEGO design. To provide the best immersive experience, the system should provide 1) high-speed and high-resolution image rendering: the rendering should achieve screen resolution of the mobile device and at least 60 frames-per-second; 2) extremely low delay guarantees: the motion-to-display latency of each user should be below 20ms; 3) synchronization: the synchronization latency should be small enough to enable the smooth collaboration; 4) scalability: the number of users should not have a significant impact on the system performance. To achieve all these goals, we introduce a central server to facilitate user synchronization via exchanging small messages. Each user reports to the server with its LEGO design progress, which is then distributed to all other users by the server; all other users render the corresponding virtual LEGO models in its own design space. We demonstrate via real-world implementations and evaluations that: 1) our system performance (e.g., synchronization delay, frame rate) does not degrade with the increase of the number of users; 2) our developed system not only yields a motion-to-display delay of 11 ms (i.e., 90 frames per second) but also achieves a screen resolution of each user's mobile device (e.g., \(2400\times1080\) pixels for Google Pixel 6).

Service function chain (SFC) allocation problems have been studied in previous works. Models with different objectives decide the allocations of functions in chains. Currently, the allocation strategies cannot be applied in Kubernetes automatically so that the performance of these models cannot be evaluated. There is a lack of existing tools which can connect the allocation models and function deployments for SFCs. We implement an open virtual network based SFC-compatible network plugin for Kubernetes. We implement two controllers for creating SFCs among existing functions and SFC deployments without existing functions which can be cooperated with allocation models. The plugin allocates the functions in chains according to the given models and connects each function in chains by setting suitable flow entries in Kubernetes. Our demonstrations validate the implementation at last.

The increasing popularity of programmable wireless networks has led to efforts by both industry as well as academia to redesign access networks based on the Open RAN concept, enabling a variety of novel use cases ranging from RAN sharing to enterprise wireless. This demonstration showcases our efforts relating to the design, development, and implementation of an O-RAN platform complete with a fully disaggregated radio access network and a near-real time network controller. Key highlights of this demo include O-RAN compliant network functions and interfaces based on purely open-source components, a new O-RAN service model to enable fine-grained control through network slicing, and novel statistics and configuration xApps supporting the proposed service model.

Network automation in the fifth generation of mobile networks (5G) requires to efficiently compute and deploy network slices. In order to guarantee physical isolation for virtual networks and no interference between different slices, it is possible to rely on hard slicing, whose principles can be implemented using Flex Ethernet (FlexE) technology. As slices are created and deleted over time, it is necessary from time to time to defragment resources and reoptimize bandwidth reservations for the remaining slices. In this demo, we present our algorithmic framework, based Column Generation, and we showcase how to efficiently defragment the network in order to reduce the Maximum Link Utilization (MLU) of current reservations with a minimum number of configuration changes.

Time-Sensitive Networking (TSN) is vital to enable time-critical deterministic communication, especially for applications with industrial-grade requirements. IEEE TSN standards are key enablers to provide deterministic and reliable operation on top of Ethernet networks. Much of the research is still done in simulated environments or using commercial TSN switches lacking flexibility in terms of hardware and software support. In this demonstration, we use an open Cloud testbed for TSN experimentation, leveraging the hardware features that support precise time synchronization, and fine-grained scheduling according to TSN standards. We demonstrate the setup and operation of a Linux-based TSN network in the testbed using our modular Centralized Network Configuration (CNC) controller prototype. With our CNC we are able to quickly initialize the TSN bridges and end nodes, as well as manage their configurations, modify schedules, and visualize overall network operation in real-time. The results show how the TSN features can be effectively used for traffic management and resource isolation.

Today's networks support a great variety of services with different bandwidth and latency requirements. To maintain high user satisfaction and efficient resource utilization, providers employ traffic shaping. One such mechanism is the Hierarchical Token Bucket (HTB), allowing for two-level flow bitrate guarantees and aggregation. In this demo, we present HTBQueue - our OMNeT++ realization of the HTB, and show how the module can be used for mimicking 5G network slicing and analyzing its effect on network services.

Along with an increased interest for connected vehicles and autonomous driving, the Cooperative Intelligent Transportation Systems (C-ITS) are being investigated and validated through the use of C-ITS messages, such as Cooperative Awareness Messages (CAMs). In this paper we demonstrate a tool to support research on CAMs, since C-ITS deploy the Cooperative Awareness Basic Service to exchange CAMs among road C-ITS entities, e.g., vehicles and roadside units (RSUs). These messages provide awareness of traffic information in the Non-line-of-sight (NLOS) of the vehicle (e.g., speed, location, heading), and are an enabler of improving safety in vehicles. Therefore, it is important that those messages are received at the receiving C-ITS vehicle with low latency. In this demo, we showcase how the size of a particular CAM that carries information about the vehicle and surrounding infrastructure affects the latency. In order to demonstrate this effect, we use two leading technologies that support the first generation V2X communication respectively ITS-G5 (IEEE 802.11p) and LTE-V2X (3GGP). We have tested our proposal in a real life C-ITS testbed, at the Smart Highway located in Antwerp, Belgium.

Payment channel networks (PCN) enable the use of cryptocurrencies in everyday life by solving the performance issues of blockchains. Nevertheless, the main implementations of payment channel networks lack the flexibility to test new proposals that can address fundamental challenges, such as efficient payment routing and maximization of the payment success rate. In this demo paper, we propose PCNsim, an open-source simulator based on OMNeT++, which fully reproduces the default behavior of a payment channel network. We build the simulator in a modular architecture that allows easy topology/workload customization and automates result visualization. The core mechanism of PCNsim implements the specifications of the Lightning Network. We evaluate our proposal with a dataset of credit card transactions in a scale-free topology and show that it successfully demonstrates the difference between two routing methods in different setups.

LoRaWAN is certainly one of the most widely used LPWAN protocols,. The LoRaWAN 1.1 specification aims to fix some serious security vulnerabilities in the 1.0 specification, however there still exist critical points to address. In this paper, we identify an attack that can affect LoRaWAN 1.0 and 1.1 networks, which hijacks the downlink path from the Network Server to an End Device. The attack exploits the deduplication procedure and the gateway selection during a downlink scheduling by the Network Server, which is in general implementation-dependent. The attack scheme has been proven to be easy to implement, not requiring physical layer-specific operations such as signal jamming, and could target many LoRaWAN devices at once. We demonstrates this attack and its effects by blocking a device under our control by receiving any downlink communication.

A key enabler for integration of machine-learning models in network management is timely access to reliable data, in terms of features, which require pervasive measurement points throughout the network infrastructure. However, excessive measurements and monitoring is associated with network overhead. The demonstrator described in this paper shows key aspects of feature selection using a novel method based on unsupervised feature selection that provides a structured approach in incorporation of network-management domain knowledge in terms of policies. The demonstrator showcases the benefits of the approach in a 5G-mmWave network scenario where the model is trained to predict round-trip time as experienced by a user.

With its mobility and flexibility, autonomous robots have received extensive attention in industrial Internet of Things (IoT). In this paper, we adopt non-orthogonal multiple access and multi-antenna technology to enhance the connectivity of sensors and the throughput of data collection through taking advantage of the power and spatial domains. For average sumrate maximization, we optimize the transmit power of sensors and the trajectories of robots jointly. To deal with uncertainty and dynamics in the industrial environment, we propose a multi-agent reinforcement learning (MARL) algorithm with experience exchange. Next, we present the visualization of robotic communication and mobility to analyze the learning behavior intuitively. From the software implementation results, we observe that the proposed MARL algorithm can effectively adjust the communication strategies of sensors and control the trajectories of robots in a fully distributed manner. The code and visualization video can be found at https://github.com/lry-bupt/Visual_MARL.

Network slicing is considered as an efficient method to satisfy the distinct requirement of diversified services by one single infrastructure in 5G network. However, owing to the cost of information gathering and processing, it's hard to swiftly allocate resources according to the changing demands of different slices. In this demo, we consider a radio access network (RAN) scenario and develop several deep reinforcement learning (DRL) algorithms which can keenly catch the varying demands of users from different slices and learn to make an intelligent decision for resource allocation. Besides, in order to implement and evaluate our algorithms efficiently, we have also implemented a platform with a modified 3GPP Release 15 base station and several on-shelf mobile terminals. Numerical analyses of the corresponding results verify the superior performance of our methods.

Electric load forecasting has emerged as a critical enabler of decision-making and scheduling for smart grids. However, most of the existing deep learning electricity prediction methods are trained offline in the cloud, which causes network congestion and long latency. Edge computing has shown great potential in training models at the network edge to ensure real-time. In this paper, we propose a dynamic combined prediction framework based on sparse anomaly perception with cloud-edge collaboration to exploit the real-time characteristic of online prediction models on edge and the strong predictive ability of offline prediction models on the cloud. The proposed framework can reasonably process abnormal data by incorporating a sparse anomaly-aware approach, thus further improving the model prediction capability. For this demo, we develop an edge computing-based microgrid platform on which we have implemented a dynamic combined prediction scheme based on sparse anomaly-aware. Experimental results verify the practicability and feasibility performance of the proposed scheme.

With the increasing demand of intelligent edge services (IES), diverse hardware vendors present their edge devices with vendor-specific inference frameworks, each requiring a distinct model parameter structure. Consequently, edge service developers have to deploy Artificial Intelligence (AI) models on these devices following the different frameworks, which significantly increases the learning cost and challenges the fragile development of IES. To simplify and accelerate the development of machine learning based edge services in the practical heterogeneous hardware systems, we present, Trueno, a cross-platform machine learning model serving framework. Trueno provides unified APIs and creates a less-code development environment for developers, so that models can easily adapt to different environments. Trueno has been used to support multiple real-world commercial AI edge systems, two of which will be demonstrated about its efficiency and flexibility in model deployment.

Employing federated learning (FL) in multi-tier computing to achieve various intelligent services is widely in demand. However, adaptive decision-making of FL tasks to improve latency performance is still mostly limited to theoretical studies of local computational optimality, and is challenging to carry out in practical systems. This paper proposes an adaptive decision-making framework (ADMF) for FL tasks with multi-layer computational participation to attain lower latency with a global optimization perspective. In this demo, a prototype framework of ADMF in multi-tier computing is demonstrated. First, the feasibility of implementing the proposed framework is provided. Then, we show the latency performance through the experimental results that validate the practicality and effectiveness of the proposed framework.

Computing Power Network (CPN) is a novel evolution of multi-access edge computing, which is expected to apply ubiquitous computing resources with intelligence and flexibility. In this paper, we implement the prototype testbed of CPN based on Kubernetes with microservice architecture, realizing key enabling technologies of CPN including computing modelling, computing awareness, computing announcement and computing offloading. We evaluate the performance of the testbed with typical Internet services with intelligent inferences, which are delay-sensitive and compute-intensive. Experimental results reveal that our CPN testbed can realize shorter response latency and better load balancing performance in comparison with traditional edge computing paradigm.

This demo presents a lightweight framework for the remote operation of human-robot interactions. As always, proper synchronization between the human (master) and the robot (controlled) is a critical issue during manipulation. In this experiment, we present an end-to-end synchronous system to establish near real-time maneuvering. Moreover, by leveraging the devices' limited yet available computational capabilities in master and controlled domains, we aim to apply edge intelligence to determine the amount of data required for mimicking the human's hand movement before wireless transmission to the controlled domain. We observe from extensive experiment results that our proposed TINGLE demonstrates a noticeable performance with fewer missing movements in the controlled domain than baselines.