The advent of 5G deployments and the anticipation of 6G have initiated a concerted effort to define new network and application services that leverage these technologies. A key focus of this effort is deterministic networking, which provides the reliability, time sensitivity, and predictability essential for supporting critical services and applications. This article presents a solution to support multi-domain deterministic communications, integrating various technology-specific deterministic networking solutions---namely IEEE 802.11, IEEE 802.1 TSN, and 3GPP 5G domains---over an IETF DetNet data plane. Our goal is to create a realistic infrastructure for developing the next generation of deterministic services. Key contributions include the design, deployment, and performance characterization of the multi-domain data plane, as well as its integration into a real-life use case such as the remote control of a robotic dog.

In this demonstration, FoReCo is introduced as a solution for recovering lost control commands in remotely controlled robots. Visitors are given the opportunity to remotely control a robotic arm using a joystick, with the added challenge of experiencing packet losses in the wireless medium. The lost control commands cause the robotic arm’s trajectory to become distorted. To combat this issue, FoReCo is implemented and utilizes an ML model that has been trained on a real-world dataset to recover the lost control commands. The demonstration illustrates how FoReCo recovers the lost commands, resulting in the robotic arm operating smoothly despite the presence of wireless medium losses.

6G will lay its foundations on new paradigms and requirements. This new technology is expected to provide global coverage, exploring a huge spectral chunk (sub-6 GHz, mmWave, THz and optical frequency bands) to further increase data rates and connection density. In addition, 6G networks will enable a new range of smart applications with the aid of Artificial Intelligence, Big Data technologies and the emerging paradigms of Quantum Computing and Quantum Machine Learning. This paper focuses on these new paradigms and proposes an indoor location method based on the well known Euclidean Distance in its quantum version. Specifically, an example of this use case is shown, which is executed in one quantum computer from IBM Quantum Experience.The paper analyses the obtained results while exploring new challenges and fields of application of the technology. Results show that the quantum approach is accurate enough to calculate Euclidean Distance between two vectors while outperforming classical computation if vector size is big enough.

Industry 4.0 seeks the digitization and interconnection of all production processes. Due to the huge number of sensors, robots, machines and other devices that need to be interconnected, the Fifth Generation (5G) becomes an enabling technology. In order to address this challenge, this paper presents the implementation procedure of a 5th Generation 5G Non-NSA private network based on Open Air Interface (OAI) and presents the advantages and limitations of OAI that will be useful for academia and the industrial sector. A real use case has been evaluated to demonstrate the capabilities of the network, where an Automated Guided Vehicle (AGV) is remotely controlled and monitored using an open source 5G-NSA private network. This paper presents the key steps of the network implementation and analyzes the results measurement campaign. Indeed, a network created with OAI allows evaluating the benefits of many smart manufacturing solutions but with certain limitations in terms of bit rate, latency and number of active users in the private network.