The recently introduced QUIC protocol has greatly increased the flexibility of end-to-end transmissions on the Internet, surpassing the design limits of the most popular transport protocols: TCP and UDP. However, some of TCP’s main design principles were carried onto QUIC, which may not be suitable for real-time use-cases; primarily, full reliability, as it requires every packet to be retransmitted until acknowledged by the receiver. In this work, we present Dynamic Reliability (DRel), a partial reliability framework that allows for granular alteration of the reliability per packet at the transport layer. The framework is housed by QUIC and its multipath extension, yet offering no-ack and no-retransmit for the true meaning of unreliable packet transmission (congestion control does not impact and is not influenced by unreliable packets). The “dynamic” in DRel refers to interchangeable reliable and unreliable transmission, in one session and across multiple paths, depending on the volatility of the communication system; guided by reliability policies. Fluidly altering packet reliability may offer a means in meeting stringent 5G and Beyond transmission requirements, especially for xURLLC use-cases. We examine the performance of DRel in single- and multiple-path architectures through system-level simulation using Mininet. The results illustrate comparable performance to vital QoE metrics for the dynamic reliability policies compared to the original (MP)QUIC. Alternatively, the enhancements at the transport layer stem from a reduction in communication congestion by up to 80% for single- and multiple-path connections compared to the original (MP)QUIC. As a result, the amount of backlogged and out-of-order packets is reduced, downsizing intermediate and end-to-end buffer occupancies.

The use of multipath transport protocols in explored in Beyond 5G services given the various capabilities it could add to the service delivery. One of such is the possibility of exploiting multiple access connectivity, i.e. using multiple interface of the device concurrently, as well as dynamic adaptation of reliability at the transport layer. In this work, we present traffic classification that will then allow encrypted data to be classified for different level of reliability, hence enabling the delivery of dynamic reliability at the multipath transport protocol. The results show a significant improvement of up to 45 % in data overhead and of up to 20% reduction in end-to-end application-level latency, in comparison to the standard MP-QUIC implementation and intelligent state of the art reliability policies.

Due to the rapid adoption of 5G networks and the increasing number of devices and base stations (gNBs) connected to it, manually identifying malfunctioning machines or devices that cause a part of the networks to fail becomes more challenging. Furthermore, data collected from the networks are not always sufficient. To overcome these two issues, we proposed a novel root cause analysis (RCA) framework that integrates graph neural networks (GNNs) with graph structure learning (GSL) to infer hidden dependencies from available data. The learned dependencies are the graph structure utilized to predict the root cause machines or devices. We found that despite the fact that the data is often incomplete, the GSL model can infer fairly accurate hidden dependencies from data with a large number of nodes and generate informative graph representation for GNNs to identify the root cause. Our experimental results showed that higher accuracy of identifying a root cause and victim nodes can be achieved when the number of nodes in an environment is increased.

5G is the fifth generation of cellular networks. It enables billions of connected devices to gather and share information in real time; a key facilitator in Industrial Internet of Things (IoT) applications. It has more capabilities in terms of bandwidth, latency/delay, processing powers and flexibility to utilize either edge or cloud resources. Furthermore, 6G is expected to be equipped with the new capability to converge ubiquitous communication, computation, sensing and controlling for a variety of sectors, which heightens the complexity in a more heterogeneous environment This increased complexity, combined with energy efficiency and Service Level Agreement (SLA) requirements makes application of Machine Learning (ML) and distributed ML necessary. A decentralized approach stemming from distributed learning is a very attractive option compared with a centralized architecture for model learning and inference. Distributed ML exploits recent Artificial Intelligence (AI) technology advancements to allow collaborated ML, whilst safeguarding private data, minimizing both communication and computation overhead along with addressing ultra-low latency requirements. In this paper, we review a number of distributed ML architectures and designs, that focus on optimizing communication, computation and resource distribution. Privacy, information security and compute frameworks, are also analyzed and compared with respect to different distributed ML approaches. We summarize the major contributions and trends in this area and highlight the potential of distributed ML to help researchers and practitioners make informed decisions on selecting the right ML approach for 5G and Beyond related AI applications. To enable distributed ML for 5G and Beyond, communication, security, and computing platform often counter balance each other, thus, consideration and optimization of these aspects at an overall system level is crucial to realize the full potential of AI for 5G and Beyond. These different aspects do not only pertain to 5G, but will also enable careful design of distributed machine learning architectures to circumvent the same hurdles that will inevitably burden 5G and Beyond network generations. This is the first survey paper that brings together all these aspects for distributed ML.

This article presents a data-driven framework for trajectory recommendation in automated and cooperative driving. The considered cooperative driving maneuver is lane-merge coordination, and while the trajectory recommendation can only be communicated to the connected vehicles, in computation of those recommendations both connected and unconnected vehicles are taken into account. The data-driven framework is implemented centrally, comprising of two main components of a traffic orchestrator (TO) and data fusion (DF). The TO predicts the safest trajectories for connected vehicles involved in the lane-merge maneuver. The DF incorporates camera detected vehicles in order to map all vehicles, including connected and unconnected. To this end, the recommendations are built using various state-of-the-art machine learning (ML) techniques, including deep reinforcement learning and dueling deep Q-network. Our evaluations are conducted using the real-system deployed in the test track, with a mix of connected and unconnected vehicles. The results demonstrate the precision of predicted trajectories, and the percentage of successful lane merge achieved deploying different ML techniques.

In this paper, a framework for lane merge coordination is presented utilising a centralised system, for connected vehicles. The delivery of trajectory recommendations to the connected vehicles on the road is based on a Traffic Orchestrator and a Data Fusion as the main components. Deep Reinforcement Learning and data analysis is used to predict trajectory recommendations for connected vehicles, taking into account unconnected vehicles for those suggestions. The results highlight the adaptability of the Traffic Orchestrator, when employing Dueling Deep Q-Network in an unseen real world merging scenario. A performance comparison of different reinforcement learning models and evaluation against Key Performance Indicator (KPI) are also presented.