Research Projects

The INSTINCT project is expected to enable globally sustainable, interactive, immersive, and intelligent ‘beyond communications’ 6G connectivity by developing three complementary but critical breakthrough technology pillars: i) sensing-assisted communication technologies, thus allowing localization, tracking, mapping, monitoring, imaging, incident detection and semantics become integral parts of connectivity services; ii) intelligent surfaces, holographic radios and cell free systems, which offer wavefront engineering functionalities and tuneability of the wireless environment and can act as reconfigurable and intelligent sensors; and iii) Machine Learning (ML) techniques-based co-design of Sensing and Communications. INSTINCT proposes a revolutionary path to 6G and has the ambition to specify the relevant performance indicators and their values, formulate suitable models, devise the theoretical framework, invent new technologies, evaluate via simulations and validate by means of 2 hardware and 1 software demonstrators, a networked intelligence concept able to meet the unprecedented 6G requirements. To realize this vision, the consortium brings together all relevant stakeholders from across Europe, with an impressive record of interdisciplinary research excellence, technology innovation, standardization and transfer, and implementation expertise.

The AI4CI project aims to support European educational institutions in creating a new joint master's degree program focused on the application of Artificial Intelligence to Connected Industries. The technologies adopted in Connected Industries, including programmable network devices, embedded systems, robotics, Industrial Internet-of-Things, and Cloud computing, are rapidly evolving and present specific challenges in the integration of distributed and in-network AI algorithms. They include for instance extremely low-latency and high reliability environments, network synchronisation, constrained computing for AI execution, and deterministic execution guarantees. The goal of the project is to provide a cutting-edge, up-to-date master curriculum that covers all latest advances in the design and operation of AI systems integrated with Internet of Things, Cloud Networking, and Robotics technologies shaping the Connected Industry. 

HoloMIT 2.0 is a key enabler for the next generation of the Metaverse, providing interactive and collaborative multi-user holoportation (that is, holographic teleportation) benchmark services, through volumetric and photorealistic representation of users, over 5G and 6G environments. On the one hand, the project will contribute with a modular and adaptive end-to-end platform, potentially offered as Software-as-a-Service (SaaS), taking advantage of the possibilities offered by the cloud continuum and programmatic network architectures and elastic infrastructures to increase the performance, adaptability, and scalability of holoportation services. On the other hand, the project will contribute with accessible multimodal interaction functionalities in order to enable truly interactive and collaborative multi-user holoportation services in a variety of use cases (e.g. training, culture, corporate meetings, gamification), to be selected for through user-centric activities. The anticipated technological contributions will be compatible with existing infrastructures and equipment, as well as be based on compatible extensions to existing standards, in terms of technologies, platforms, protocols and formats. This will increase the possibilities of adoption and effective deployment, avoiding incompatibility problems and also enabling the adoption of additional contributions and/or improvements by third parties. Likewise, the contributions of the project will be evaluated, validated and demonstrated in a variety of pilot actions in 5G environments and beyond, taking advantage of proprietary network and processing infrastructure. In essence, HoloMIT 2.0 will provide contributions beyond the state-of-the-art in the fields of communication networks and immersive technologies, with high potential in different verticals, and bringing economic, social, and sustainability impact.

Open Radio Access Network (O-RAN) is a major carrier-led effort to disaggregate the next generation of virtualized RANs for multi-vendor deployments. It is aimed at disrupting the vRAN ecosystem by breaking vendors lock-in and opening up a market that has been traditionally dominated by a small set of players. When successful, Open RAN will unleash an unprecedented level of innovation in the 6G space by lowering the market entrance barrier to new competitors. Harnessing the strengths of opening the radio access arena, however, entails a number of daunting challenges that need to be addressed. Managing the increased complexity of Open RAN 6G networks with traditional human-in-the-loop approaches will not be possible anymore. Instead, zero-touch technologies that fully automate the network operation will become the standard, and the success of 6G will vastly depend on the quality of the Artificial Intelligence (AI) solutions that will run at schedulers, controllers, and orchestrators across network domains, and de-facto manage the infrastructure. While Open RAN interfaces will facilitate AI network management operations, new challenges will be introduced in terms of network data processing and timely management actions decisions. This project will focus on exploring the limits of AI-driven network automation in future 6G systems. For this reason, Open6G will build on the Open RAN efforts ongoing today to tackle the aforementioned challenge and design and develop an Open 6G Testing Platform for novel networking and sensing applications.

Nanotechnology is paving the way toward nanoscale devices that are envisioned to enable several groundbreaking healthcare applications, such as molecular-level cancer detection, targeted drug delivery, and neurosurgeries. The nanodevices are expected to flow through the human body, perform actions upon commands or at certain locations, and communicate the results to the outside world. There is, therefore, a need to enable two-way communication between the nanodevices and the outside world, as well as their localization inside the body. These functionalities should be supported while simultaneously maintaining tiny form factors and a low energy consumption profile of a potentially vast number of nanodevices. In the ScaLeITN project, we will utilize wireless signals in the terahertz (THz) frequencies for enabling both localization and communication capabilities for in-body nanodevices. Localization will be enabled through THz backscattering, which is an unexplored paradigm that promises low energy and high precision localization at the nanoscale. The constrained communication range characteristic for in-body THz propagation will be mitigated through multi-hop communication. In such communication, only a selected subset of nanodevices in the multi-hop route will be awoken by utilizing wake-up radio-like signals. Selection of these nanodevices will be based on their location estimates, as well as on their energy lifecycle characterizations if available through backscattering. This is again a novel paradigm that promises enabling low power, reliable, and scalable THz nanocommunication. The main outcome of the project is to develop a pioneering prototype of a THz nanonetwork with both localization and two-way communication capabilities. Market valorization of the prototype is envisioned during and beyond the scope of the project through the Collider and i.start, two academic innovation programmes for supporting scientists in developing disruptive technology-based products.

The latest generation of WiFi technology, known as mmWave WiFi, utilizes comparatively higher frequencies than traditional WiFi. To combat high signal attenuation at mmWave frequencies, mmWave WiFi utilizes directional transmission and reception of signals. By utilizing directional communication at high frequencies, and in contrast to omni-directional and low-frequency traditional WiFi technologies, mmWave WiFi can deliver tens of gigabits per second bitrates required by various ground-breaking applications (e.g., virtual reality and aerial wireless networks). To establish strong communication links, in mmWave WiFi the directions of transmit and receive beams must be properly aligned, which is a process known as beam-steering. The current beam-steering mechanisms do not perform well under in dynamic conditions, i.e., when the communicating devices are mobile or if there are humans obstructing the communication. Therefore, there is a need for developing new beam-steering mechanisms that will be able to mitigate these negative effects. Consequently, experimental evaluation of these newly developed mechanisms will be required in order to benchmark their performance against the existing ones. To guarantee fair comparative benchmarking, there is a need for highly repeatable experimentation, i.e., different instances of an experiment must be performed in a way that preserves all experimental conditions (apart from exchanging the beam-steering mechanisms), pertaining primarily to the repeatable mobility patterns of the communicating devices, as well as the mobility patterns of humans causing obstacles. Such conditions cannot be achieved if humans are involved in the experimentation, either as carriers of devices or as obstacle generating factor. To alleviate these issues, we will develop a testbed infrastructure for fully repeatable mmWave WiFi experimentation with device mobility and moving obstacles. The repeatability will be guaranteed by utilizing drones as the carriers of mmWave WiFi devices and a combination of a robotic mobility platform and mannequin resembling a moving human-like obstacle. Once developed, this testbed infrastructure will increase the visibility of our university to a large heterogeneous audience and allow as to kick-start our research activities in the highly-promising mmWave WiFi domain. In addition, the testbed will be convenient for a broad range of experimentation with mobile wireless infrastructures going beyond the scope of the initially envisioned beam-steering in mmWave WiFi experimentation.

The IoT domain is characterized by many applications that require low-bandwidth communications over a long range, at a low cost and at low power. This has given rise to novel radio technologies that try to fill in this existing market gap of low-power wide area IoT networks, often referred to as Low Power Wide Area Networks (LPWANs). Due to the use of sub-GHz radio frequencies (typically 433 or 868 MHz), a single LPWAN base station has a large coverage area, with typical transmission ranges in the order of 1 up to 50 kilometers. As a result, a single base station can support high numbers of connected devices (> 1000 per base station). Notorious initiatives in this domain are LoRa and Sigfox. Although these standards have similar goals, they are very different in terms of technology (e.g., wideband vs ultra narrow band), operation mode (multiple versus single operator) and whether or not a company is allowed to install their own gateways. Very recently, the IEEE 802.11 family has been extended with a LPWAN solution, IEEE 802.11ah. Like existing Wi-Fi, 802.11ah provides IP-based connectivity, allowing devices to communicate with a broad range of hardware platforms. As such, this technology is a prime candidate for inclusion in future residential and industrial access points. To further complicate the technology choice for companies, other technologies such as DASH7, Weightless and IEEE 802.15.4g utilize the same radio frequencies, thereby competing (and possibly interfering) with other LPWAN technologies. The aim of the IDEAL-IoT project was to develop management and optimization solutions for real-time control, reconfiguration and optimization of LPWAN networks to improve co-existence of different technologies, improve scalability, support handovers and meshing and add differentiated QoS support. In addition, the project yielded a set of management and application services for indoor/outdoor localization, diagnostics & monitoring, scalable network & QoS management, multi-provider optimizations, network virtualization & software defined networking, and deployment & network planning.

In several areas of industry, 100%-operational mission-critical communication platforms are a must. In other industrial scenarios, asset management requires vehicles and goods to be tracked over long distances and periods of time, with a variable demand towards accuracy and data throughput. At the start of the MuSCLe-IoT project, no single wireless technology in itself was capable of assuring this type of specifications at acceptable cost. License-exempt low-power wide-area network (LPWAN) technologies (SigFox, LoRa, DASH7, 802.15.4g) are reliable and power efficient. Yet, each of them is designed for specific use cases, which limits their wider applicability. To cover the demands of a lot of today’s applications, multiple (redundant) systems - wireless and wired - are being deployed in parallel. Often resulting in the desired performance, but adding a lot of cost and complexity in the installation and interoperability. Within MuSCLe-IoT, a solution has been developed that makes optimal use of multiple LPWAN standards for mission-critical communication and long-range multi-year asset management. An example of such operation is to switch to LoRa at times when DASH7 coverage is lower or communicate over SigFox when less data throughput is needed. The MuSCLe-IoT prototype consists of a small, low-cost, battery-powered single-antenna device in combination with the required software algorithms for cloud- and edge computing to control the network. The MuSCLe-IoT technology can be seamlessly implemented with existing communication devices (e.g., DECT phones, pagers) at much lower cost of ownership compared to wired or redundant system installations. It can revolutionize the uptake of IoT solutions in sectors as marine (harbors), construction, retail, museums, and many more.

eWINE was run as a collaborative project for experimentally-driven research on top of existing experimental infrastructures including necessary extensions, adaptations or reconfigurations that serve the experiments. In order to solve the existing shortcomings in dense & dynamic wireless networks through experimentally-driven research performed on FIRE facilities offered by the FP7 CREW, Fed4FIRE, OpenLab, EVARILOS, FORGE, FLEX and H2020 WiSHFUL projects, the eWINE project pursued the following objectives: i) enabling on-demand end-to-end basic wireless connectivity capable of delivering elastic connectivity services from an Over-The-Top (OTT) service provider to fixed and mobile wireless devices within dense network scenarios, ii) enabling elastic resource sharing in dense deployments of heterogeneous and small cell networks (HetSNets) in a dynamic way to provide stable connections guaranteeing a well understood and stable probabilistic model of Quality of Service to the many wireless services running on the wireless devices, iii) developing and evaluating an open and reconfigurable physical layer that is able to intelligently adapt radio parameters to the context for addressing a wide range of services and requirements that HetSNets need to support, and iv) educating the research community, industrial professionals, as well as the general public to increase awareness of problems that occur in dense and dynamic wireless networks.

With billions in everyday use throughout the world, the smartphone is obviously a very important personal communication, data processing, and entertainment tool. It is also true that there exists a strong demand for a personal navigation capability inside buildings and in other indoor environments to complement the availability of GPS/GNSS for outdoor navigation, which is already very popular. It is anticipated that a wide variety of Location Based Services (LBS) will be enabled if/when effective indoor localization and tracking solutions are developed. The main goal of the PerfLoc project was to facilitate the development of the best possible smartphone indoor localization apps. We have achieved this goal by making the following available to the R&D community: i  ) an extensive repository of annotated smartphone sensor and RF signal strength data to enable researchers to develop smartphone indoor localization apps. ii) a web-based performance evaluation tool based on the international standard ISO/IEC 18305 to evaluate the performance of the apps developed based on the PerfLoc data repository.

The EVARILOS project addressed one of the major problems of indoor localization research: The pitfall to reproduce research results in real life scenarios suffering from uncontrolled RF interference and the weakness of numerous published solutions being evaluated under individual, not comparable and not repeatable conditions. Accurate and robust indoor localization is a key enabler for context-aware Future Internet applications, whereby robust means that the localization solutions should perform well in diverse physical indoor environments under realistic RF interference conditions.