## 4 Overview of Research Projects on AI/ML for Communications and Networking
### Europe--H2020 Research Framework
#### 4.1.5 Smart Connectivity beyond 5G
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Project Name** & **Target Areas Using AI/ML** \\ \hline
6G Brains & Implements a self-learning agent based on deep reinforcement learning which performs intelligent and dynamic resource allocation for future industrial IoT at massive scales. \\ \hline AI@Edge & Designs reusable, trustworthy and secure AI solutions at the network edge for autonomous decision making and E2E quality assurance. The targeted use cases are AI-based smart content pre-selection for in-flight infotainment, AI-assisted edge computing for infrastructure monitoring using drones, and AI for intrusion detection in industrial IoT. \\ \hline Daemon & Implements AI/ML algorithms for real-time network control and intelligent orchestration and management. Specifically, the project investigates the use of real time RIC for embedding intelligence in RIS, radio resource allocation, distribution of computational resources at the edge/fog, and backhaul traffic control. At longer timescales, energy-aware network slicing, capacity forecasting, anomaly detection and self-learning network orchestration are examined. \\ \hline Dedicat 6G & Aspires to demonstrate distributed network intelligence for dynamic coverage extension, indoor positioning, data caching, and energy-efficient distribution of computation loads across the network. Representative use cases demonstrate the developed solutions include smart warehousing, augmented and virtual reality applications, public safety and disaster relief using automated guided vehicles and drones, and connected autonomous mobility. \\ \hline Hexa-X & Applies AI for network orchestration from the end-devices through the edge to the cloud and the core network. This includes inter-connecting intelligent agents based on federated learning, proactive network slice management, instantiation of network functions, zero-touch automation, explainable AI, intelligent spectrum usage and intelligent air interface design, to name a few. \\ \hline Marsal & Integrates blockchain technology with ML-based mechanisms to foster privacy and security in multi-tenant network slicing scenarios. Furthermore, ML-based orchestration and management of radio and computational resources is studied. \\ \hline Reindeer & Develops an experimental testbed for the RadioWeaves technology. RadioWeaves leverages the ideas of RIS and cell free wireless access for offering zero-latency and high-capacity connectivity in short-range indoor applications such as immersive entertainment, health care, and smart factories. Intelligence is distributed near the end devices for an efficient use of spectral, energy and computational resources. \\ \hline Rise-6G & Designs and prototypes intelligent radio-wave propagation using RIS. \\ \hline Teraflow & Implements a novel SDN controller with cloud-native architecture, AI-based security, and zero-touch automation features. The demonstrated use cases are autonomous heybond 5G, cybersecurity and automotive. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Funded projects under the call ICT-52-2020—5G PPP—Smart Connectivity beyond 5G, along with their main application areas of AI/ML and target use cases. road traffic by recommending actions that are communicated over V2X links back to the vehicles. Finally, Hexa-X considers aspects of spectrum evolution and studies intelligent spectrum management and interference mitigation techniques. For instance, in industrial IoT environments, radio usage often experiences periodic cycles, and thus, AI-based interference prediction schemes could be used to avoid interference in certain times and areas and, subsequently, enhance the spectrum utilisation efficiency.