The current address-centric link-layer multicast hinders the native ability of NDN dynamic multicast to fast and efficiently deliver content in wireless network with large number of end hosts. In our recent paper, we presented techniques for dynamic group MAC addresses generating and lightweight packet filtering to collaborate NDN native multicast with the current link layer. In this demo, we present a platform for evaluating content delivery over NDN multicast using these techniques. The platform does not require changes in existing 802.11 standards and we implemented it on Raspberry Pies and personal computers. The platform allows evaluation of the performance of dynamic multicast, operation overhead, and multicast feedback in various settings including network delay, multicast data transmission rate and environment interference.

The 6th generation mobile networks (6G) feature extensive connectivity, ultra-dense access points, and ubiquitous services across multi-domains. However, the 6G network is also faced with various resource conflicts and high complexity of network configuration, which makes it difficult to implement efficient and real-time network management. Intent-driven networks would provide intelligent and customized network services and realize autonomous network management. In this demo, we build an intent-driven end-to-end network orchestration platform for the 6G network, which can provide life-cycle management of network resources. The platform can translate user intents into network policies and configurations and create end-to-end network slices according to different services.

5G has evolved into a cloud-native disaggregated infrastructure, enabling the concept of modularization in its design and supporting a service-based architecture. As such the concept is not new, except that it has not been applied in the telecommunication world, which has resulted in vendor lock-in, limited innovation, and high costs. With modular design at all levels, 5G allowed the telco world to meet the IT world and outstanding innovations followed with the so-called post-5G propositions. The EU SLICES Research Infrastructure (RI) is developing an open, reproducible, distributed post-5G architecture built on top of blueprints aimed to be replicated by researchers, companies, and operators and to evolve in a collaborative manner. In this demo, we will explore the blueprints that allow building a cloud-native 5G core and a split 7.2 radio network, based on open-source software while being fully reproducible. The objective of the demo is not only to show a deployment but to motivate the research community to participate in the collaborative SLICES-RI project and to adopt a reproducible methodology supporting the full research life cycle.

In this paper, we demonstrate and introduce a novel Situational Awareness with Event-driven Network Programming Edge Network Application (EdgeApp), designed to optimize network resource utilization during vessel teleoperation in congested port areas. The demonstration is conducted on an open real-life EdgeApp 5G Standalone (SA) and beyond testbed situated at the port of Antwerp-Bruges. Through this showcase, we demonstrate how 5G and beyond services, utilizing an open 5G SA testbed, can enhance vessel teleoperation. The proposed solution dynamically adjusts network configurations, allowing for lower-quality camera feeds during vessel autonomy and higher-quality feeds when in the teleoperation zone. The practical application and benefits are exemplified through visual representations within the testbed environment.

We showcase emerging technologies capable of producing and distributing AI-generated disinformation at scale across a social media network. Our demonstration will highlight the emerging risk that AI-generated disinformation poses by allowing participants to simulate propaganda at scale within a simulated social network environment and then witness the negative consequences of scalable AI disinformation in real-time. As propagandists begin to leverage AI to generate disinformation, heightened awareness of disinformation techniques will be our best defense -- Spotting the needle of truth in a haystack of disinformation. Our demonstration will raise awareness to these emerging threats.

The lack of sufficient radio frequency (RF) data constrains the performance of AI-empowered wireless communications, networking, and sensing research. RF data collection is more difficult and costly than other data types (e.g., text or image). To this end, we propose to exploit the strength of diffusion models on latent domains to generate super-realistic data for RF sensing applications. In this demo, We present a novel lightweight AIGC framework centered on latent domains, termed Activity Class Conditional Latent Diffusion Model (RFID-ACCLDM), for easy generation of large amounts of RF data at low cost, conditioned on activity class labels. We demonstrate the high performance of RFID-ACCLDM with a human activity recognition (HAR) model as a representative downstream task.

Common Randomness (CR) can be considered a resource for future communication devices and systems that improve the transmission capacity of systems and provide robust information theoretical security. To achieve this, CR must be generated such that the reconciliation cost must be lower than the size of the key that is being generated. This can be achieved by using filters. In this demo paper, we prepare a CR generation mechanism using Software Defined Radio (SDR) and achieve this tradeoff using multi-bit quantization and Savitzky Golay filter.

The real estate industry is one which is constrained by distance (e.g., property is too far away), time (e.g., unable to attend viewings at a specific time) and physical (e.g., unable to modify the property during viewings) limitations. We present the demonstration platform which allows users to overcome these limitations using Virtual and Augmented Reality (VR/AR). Users can freely view listed properties and tour surrounding amenities in VR. In addition, they can customize the property in real time and visualize the amount of sunlight in rooms during different times of the day. To support its deployment on low-end devices, we leverage the edge and cloud render streaming infrastructure and compare the Frame Per Second (FPS) performances.

The futures of AI and wireless networks are intricately intertwined. On the one hand, AI is a potent tool for automating the deployment and management of wireless networks. The next-generation wireless network, on the other hand, can support the training and deployment of AI models by providing an ocean of multi-modal data and a distributed computation resource. However, at present, they are generally regarded as independent fields, preventing them from capitalizing on the superior performance of the other. To that end, we propose to facilitate the convergence of next-generation wireless networks and AI by prototyping a first-of-its-kind Pathway-of-Experts (PoE) AI system to enable user-centric applications (such as smart homes), which contain a variety of inter-connected expert models. To prove its superior performance, we propose a groundbreaking demonstration of a PoE system in a smart home environment. Our demo showcases how a variety of interconnected expert models can revolutionize smart home technology. This demo not only exemplifies the convergence of wireless networks and AI but also highlights the practical applications of this synergy in creating responsive, intelligent environments.

This paper examines a reduced necessity for forward error correction (FEC) within the application context of federated learning (FL). The common usage of FEC to reduce bit errors imposes a cost of reduced communication speeds and increased complexity. Yet, FL is shown to have some error-resilience when training. Therefore, showing a system alignment of bit error rate flexibility within a FL application can drive towards lower encoder/decoder complexity and higher communication rates. By modifying a standard physical layer to remove FEC, a wireless system can better meet the needs of FL. The resulting modifications give priority to bits based on their importance to the model's accuracy without sacrificing communication rate or integrity of the training process. This approach and demonstration establishes a path towards a more balanced importance to each system layer abstraction removing critical bottlenecks of complexity and improving overall system performance.