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This document updates the progress made in the different research areas related to 6G Access Architecture performed in the 6G BRAINS project. The updated research areas in this document include: Artificial Intelligence (AI) based scheduler for a 5G Cell-Free (CF) networks with Integrated Access and Back-haul (IAB) Grant-free NOMA for massive Machine-Type Communication Hybrid user terminal modelling for the D2D enabled cooperative network Intelligent IAB with Beam Steering based on User Location Advanced Test and Simulation Tools supporting 6G BRAINS research

This document reports the up-to-date progress towards achieving smart E2E network slicing. It focuses on the prototyping of enabling technologies for E2E network slicing across network segments, their intent-based Management and Orchestration, and AI-based RAN resource allocation enabled with the Cognitive Plane’s Multi-Agent Deep Reinforcement Learning platform. Moreover, it covers preliminary integration work between different network slicing enablers over the E2E data path, with the MANO plane, and with the MA-DRL platform. Together, these technological enablers, algorithmic advances and initial integration work lay down the foundation for implementing the targeting E2E slicing system.

This document reports the up-to-date progress towards achieving smart E2E network slicing. It focuses on the preliminary design and prototyping of enabling technologies for E2E network slicing across network segments. Moreover, it provides initial investigations into the algorithm design of AI-based radio link control and RAN slice scheduler, as well as integration aspects of Management-Orchestration and Cognitive Plane development kit. Together, these enablers form basic building blocks for deeper integrations, technologically and algorithmically, between partners in future implementation of the targeting E2E slicing system.

This deliverable reports all the activities and outcomes related to specifying the requirements for the multi-agent Deep Reinforcement Learning (MA-DRL) scheme of 6G BRAINS. Several applications of DRL are identified by the partners in relation to the general 6G BRAINS use cases defined in D2.1. For each of the DRL applications, it will identify the interfaces between the network and the MA-DRL, as well as the data that need to transit between the MA-DRL scheme and the network. This report lays the ground for the developments of the RL-applications in the MA-DRL scheme.

This document reports all the activities and outcomes related to the definition of the network requirements, key function blocks and system architecture for the 6G BRAINS project. Based on a methodology informed by use case requirements etc., the deliverable presents an overall logical architecture with key function blocks defined and organised into operational planes. Based on the logical architecture, detailed functional architectures are defined to address the major technical challenges in the project.

Wireless connectivity is an important enabler for many modern applications in various segments. The need for further improvement of wireless communication technologies is still very large. New transmission techniques, smart communication resource planning and utilization, novel optimization methods including Artificial Intelligence and Machine Learning etc., can bring known applications to a new level of their performance or can even enable totally new applications that were impossible until now. With this deliverable, we do the first step towards identifying the most obvious applications that can benefit from 6G BRAINS technologies. While carefully reviewing the segments like production manufacturing, automotive industry, smart agriculture, etc., a list of relevant use cases is created. The most challenging use cases build the basis for our requirements analysis summarized in this document in form of technical use case descriptions, derived user requirements, KPI specifications, as well as user concerns.

Testing for quality assurance (QA) is a crucial step in the development of Extended Reality (XR) systems that typically follow iterative design and development cycles. Bringing automation to these testing procedures will increase the productivity of XR developers. However, given the complexity of the XR environments and the User Experience (UX) demands, achieving this is highly challenging. We propose to address this issue through the creation of autonomous cognitive test agents that will have the ability to cope with the complexity of the interaction space by intelligently explore the most prominent interactions given a test goal and support the assessment of affective properties of the UX by playing the role of users.

“Extended Reality” (XR) systems are advanced interactive systems such as Vir tual Reality (VR) and Augmented Reality (AR) systems. They have emerged in various domains, ranging from entertainment, cultural heritage, to combat train ing and mission critical applications. As the complexity of these systems keeps increasing, testing is getting more complex too. Current toolsets do no propose XR testing technology beyond rudimentary record and replay tools that only work for simple test scenarios. The following challenges need to be addressed: 1)Fine-grained interaction space. and 2)Assessing user experience (UX).