From a 5G operator’s perspective, accurate estimates of key User Equipments (UEs) performance metrics, especially One-Way Delay (OWD), can provide valuable information. These estimates can trigger management tasks such as reconfiguration to prevent violations of Service Level Objectives (SLOs). Moreover, such insights into UE performance can empower applications to adapt their services to end-users in a more effective manner. We use advanced machine learning over data gathered at the base stations to predict OWD from UEs and show that we are able to predict OWD with over a 2× reduction in percentage error compared to the considered baseline. We discover the close coupling between the performance of the OWD model and the type of UE, which poses a model generalization challenge. Addressing this problem, we demonstrate the shortcomings of the commonly used fine-tuning approach and develop a novel method based on domain adversarial neural networks, that can adapt to a target domain without compromising on the performance of the source domain. Our results show that we can adapt our source model to provide OWD prediction performance within 1-4 percentage points of the ideal scenario when the source and the target domains are the same. Also, our work is grounded in empirical experiments conducted within a 5G testbed, using commercially available hardware. © 2024 IEEE.

Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.

The inherent flexibility of 5G networks come with a high degree of configuration and management complexity. This makes the performance outcome for UEs, more than ever, dependent on intricate configurations and interplay between algorithms at various network components. In this paper, we take initial steps towards a performance exposure system at the base station using a data-driven approach for predicting performance violations in terms of RTT, as observed by the UE, in a 5G mmWave network. We present ML models to predict RTT using low-level and high-frequency base station metrics from a 5G mmWave testbed based on commercially available equipment. Predicting UE performance from a base station perspective, and exposing this knowledge, is valuable for applications to proactively address performance violations. We also compare several methods for feature reduction, which have a significant impact on monitoring load. We demonstrate our model's ability to identify RTT violations, paving the way for network providers towards an intelligent performance exposure system. 

Streaming high-quality video over dynamic radio networks is challenging. Dynamic adaptive streaming over HTTP (DASH) is a standard for delivering video in segments, and adapting its quality to adjust to a changing and limited network bandwidth. We present a machine learning-based predictive pre-fetching and caching approach for DASH video streaming, implemented at the multi-access edge computing server. We use ensemble methods for machine learning (ML) based segment request prediction and an integer linear programming (ILP) technique for pre-fetching decisions. Our approach reduces video segment access delay with a cache-hit ratio of 60% and alleviates transport network load by reducing the backhaul link utilization by 69%. We validate the ML model and the pre-fetching algorithm, and present the trade-offs involved in pre-fetching and caching for resource-constrained scenarios.

5G promises to usher in the industrial 4.0 era. In that era, intricately managed autonomous industrial sites with for example remotely controller equipment and autonomous units promise previously unseen levels of efficiency. Although such scenarios are elusive, they come with strict long-since established safety requirements. To uphold such requirements, intelligent industrial 5G networks, that actively take into account prevailing conditions and dynamics of the workers on the site, the equipment, and the network, are needed. Little is known about the dynamics of actual industrial 5G networks and the interplay between network performance and QoE. In this paper, as a step towards intelligent industrial 5G networks, we measure network performance for an industrial 5G network, and conduct QoE experiments with remote controlled industrial equipment on an operational site. The results revealed unexpected relationships between QoE and network performance that shows how important domain-specific knowledge is when researching intelligent industrial 5G networks.

Development towards 5G has introduced difficult challenges in effectively managing and operating heterogeneous infrastructures under highly varying network conditions. Enabling, for example, unified coordination and management of radio resources across coexisting, multiple radio access technologies (multi-RAT), require efficient representation using high-level abstractions of the radio network performance and state. Without such abstractions, users and networks cannot harvest the full potential of increased resource density and connectivity options resulting in failure to meet the ambitions of 5G. We present a generic probabilistic approach for unified estimation of performance variability based on attainable throughput of UDP traffic in multi-RATs, and evaluate the applicability in an interface selection control case (involving WiFi and LTE) based on obtaining probabilistic user performance guarantees. From simulations we observe that both users and operators can significantly benefit from this improved service availability at low network cost. Initial results indicate 1) 116% fewer performance violations and 2) 20% fewer performance violations with a reduction by 35 times in the number of handovers, compared to naive and state-of-the-art baselines, respectively.

Information centric networking (ICN) is a proposal for a future internetworking architecture that is more efficient and scalable. While several ICN architectures have been evaluated for networks carrying web and video traffic, the benefits and challenges it poses for Internet of Things (IoT) networks are relatively unexplored. In our work, we evaluate the performance implications for typical IoT network scenarios in the ICN paradigm. We study the behavior of in-network caching, introduce a way to make caching more efficient for periodic sensor data, and evaluate the impact of presence and location of lossy wireless links in IoT networks. In this paper, we present and discuss the results of our evaluations on IoT networks performed through emulations using a specific ICN architecture, namely, content centric networking (CCN). For example, we show that the newly proposed UTS-LRU cache replacement strategy for improved caching performance of time series content streams reduces the number of messages transmitted by up to 16%. Our findings indicate that the performance of IoT networks using ICN are influenced by the content model and the nature of its links, and motivates further studies to understand the performance implications in more varied IoT scenarios.

Development towards 5G has introduced difficultchallenges in effectively managing and operating heterogeneousinfrastructures under highly varying network conditions. En-abling, for example, unified coordination and management ofradio resources across coexisting, multiple radio access technolo-gies (multi-RAT), require efficient representation using high-levelabstractions of the radio network performance and state. Withoutsuch abstractions, users and networks cannot harvest the fullpotential of increased resource density and connectivity optionsresulting in failure to meet the ambitions of 5G.We present a generic probabilistic approach for unified estima-tion of performance variability based on attainable throughputof UDP traffic in multi-RATs, and evaluate the applicability inan interface selection control case (involving WiFi and LTE)based on obtaining probabilistic user performance guarantees.From simulations we observe that both users and operators cansignificantly benefit from this improved service availability at lownetwork cost. Initial results indicate 1) 116% fewer performanceviolations and 2) 20% fewer performance violations with areduction by 35 times in the number of handovers, comparedto naive and state-of-the-art baselines, respectively.