Interactive, multi-user experiences are meant to define the present and future of Virtual Reality (VR). Such immersive experiences will typically consist of remote collaborations where content is streamed and/or synchronized over a network connection. Thus, real-time collaboration will be key. In this light, the responsiveness of the system and the network will define the overall experience. As such, understanding the effect of network distortions, especially related to time delay, on end-user’s perception (in terms of Quality-of-Experience (QoE)), performance, and collaboration becomes crucial. The existing literature, however, has mostly focused on network requirements from a system point-of-view, where the key performance parameters are only provided in the form of Quality-of-Service (QoS) parameters (such as end-to-end latency). However, the translation of these network impairments to the end-user experience is often omitted. The purpose of this paper is to fill the gap by providing a thorough investigation of the impact of latency on the perception of users while performing collaborative tasks in multi-user VR. To this end, an experimental framework was designed, developed, and tested. It is based on a multi-device synchronizing architecture, enabling two simultaneous users to work together in a gamified virtual environment. The developed test environment also allows for the identification of the most prominent network requirements and objective analysis for each traffic link. To experimentally investigate the impact of latency on user perception, a user study was conducted. Participants were paired and asked to perform the collaborative task under different latency-prone scenarios. The results show that users are able to easily distinguish between distorted and non-distorted network configurations. However, making a distinction between different manifestations of latency is much less straightforward. Moreover, factors such as the user’s role in the experience and the required task, and the level of interactivity and movement have an important influence on the subjective level of perception, the strength of the user’s preferences, and the occurrence of cybersickness. In contrast, no significant differences in objective metrics, such as system performance and user completion time were observed. These results can support the creation of collective QoE metrics that model the group as a whole rather than each individual separately. As such, this work provides an important step to dynamically counteract any drops in group dynamics and performance by means of smart interventions in the transmission system and/or virtual environment.

Monitoring and providing customers with a satisfying Quality of Experience (QoE) is a crucial business incentive for mobile network operators (MNOs). While the MNO is capable of monitoring a vast amount of network-related key performance indicators (KPIs), it typically does not have access to application-specific performance metrics. Among others, this is due to practical obstacles, such as missing standardized interfaces between the network and the application. Existing QoE models allow to map collected KPIs to the user-perceived quality. However, they are not dynamic, cumbersome to obtain, and often rely on application-level information, such as the stalling duration in the case of video streaming. The 5G networking architecture provides new features which can potentially overcome current limitations of in-network QoE monitoring. More specifically, the Application Function (AF) provides a standardized interface for communicating between 5G systems and third parties, such as application providers. The Network Data Analytics Function (NWDAF) is capable of collecting a vast number of network statistics from other 5G network functions and is dedicated to training and deploying Machine Learning (ML) models. This opens new possibilities, unimaginable for earlier mobile network generations, to dynamically learn the relationship between network KPIs and QoE by utilizing ML. Besides elaborating on how the new capabilities introduced with 5G can support an ML-based QoE estimation, we perform a simulation-based feasibility study which evaluates the estimation accuracy of different state-of-the-art regression techniques. In addition, we discuss them with respect to various qualitative aspects from an MNO’s point of view.

Network slicing is a key feature of the upcoming 5G networks allowing the design and deployment of customized communication systems to integrate services provided by vertical industries. In this context, massive Internet of Things (mIoT) is regarded as a compelling use case, both for its relevance from business perspective, and for the technical challenges it poses to network design. With their envisaged massive deployment of devices requiring sporadic connectivity and small data transmission, yet Quality of Service (QoS) constrained, mIoT services will need an ad-hoc end-to-end (E2E) slice, i.e., both access and core network with enhanced Control and User planes (CP/UP). After revising the key requirements of mIoT and identifying major shortcomings of previous generation networks, this paper presents and evaluates an E2E mIoT network slicing solution, featuring a new connectivity model overcoming the load limitations of legacy systems. Unique in its kind, this paper addresses mIoT requirements from an end-to-end perspective highlighting and solving, unlike most prior related work, the connectivity challenges posed to the core network. Results demonstrate that the proposed solution, reducing CP signaling and optimizing UP resource utilization, is a suitable candidate for next generation network standards to efficiently handle massive device deployment.

In future 6G networks, the deployment of network elements is expected to be highly distributed, going beyond the level of distribution of existing 5G deployments. To fully exploit the benefits of such a distributed architecture, there needs to be a paradigm shift from centralized to distributed management. To enable distributed management, Reinforcement Learning (RL) is a promising choice, due to its ability to learn dynamic changes in environments and to deal with complex problems. However, the deployment of highly distributed RL – termed massive distribution of intelligence – still faces a few unsolved challenges. Existing RL solutions, based on Q-Learning (QL) and Deep Q-Network (DQN) do not scale with the number of agents. Therefore, current limitations, i.e., convergence, system performance and training stability, need to be addressed, to facilitate a practical deployment of massive distribution. To this end, we propose improved Double Deep Q-Network (IDDQN), addressing the long-term stability of the agents’ training behavior. We evaluate the effectiveness of IDDQN for a beyond 5G/6G use case: auto-scaling virtual resources in a network slice. Simulation results show that IDDQN improves the training stability over DQN and converges at least 2 times sooner than QL. In terms of the number of users served by a slice, IDDQN shows good performance and only deviates on average 8% from the optimal solution. Further, IDDQN is robust and resource-efficient after convergence. We argue that IDDQN is a better alternative than QL and DQN, and holds immense potential for efficiently managing 6G networks.