Conditional entropy is an important concept that naturally arises in fields such as finance, sociology, and intelligent decision making when solving problems involving statistical inferences. Formally speaking, given two random variables X and Y, one is interested in the amount and direction of information flow between X and Y . It helps to draw conclusions about Y while only observing X. Conditional entropy H(Y |X) quantifies the amount of information flow from X to Y . In practice, calculating H(Y |X) exactly is infeasible. Current estimation methods are complex and suffer from estimation bias issues. In this paper, we present a simple Machine Learning based estimation method. Our method can be used to estimate H(Y |X) for discrete X and bi-valued Y. Given X and Y observations, we first construct a natural binary classification training dataset. We then train a supervised learning algorithm on this dataset, and use its prediction accuracy to estimate H(Y |X). We also present a simple condition on the prediction accuracy to determine if there is information flow from X to Y. We support our ideas using formal arguments and through an experiment involving a gender-bias study using a part of the employee database of Karlstad University, Sweden.

The Access Traffic Steering, Switching, and Splitting (ATSSS) technology, currently under standardization by the 3rd Generation Partnership Project (3GPP), is designed to enhance wireless access through the bundling of both 3GPP and non-3GPP access. One implementation option involves tunneling data through the Multipath QUIC (MP-QUIC) protocol. The decision of which access network each packet is transmitted on can greatly impact the performance of a network flow, and may also impact the performance of other flows. While Active Queue Management (AQM) in the network could normally manage such interactions, such techniques are limited in a shared multipath-tunnel context, since all traffic in the tunnel share a single connection. This paper introduces a multi-flow, multipath tunneling framework using MP-QUIC. It employs four distinct scheduling policies for packet scheduling decisions. We investigate how such scheduling policies interact with each other, and how they may be combined in order to achieve trade-offs in terms of latency, interactivity, and throughput. Through an extensive investigation in both emulated and real-world environments, we show that Quality of Service (QoS) can be significantly improved by smart combinations of scheduling policies.

The Cellular Internet of Things (CIoT) has seen significant growth in recent years. With the deployment of 5G, it has become essential to reduce the power consumption of these devices for long-term sustainability. The upcoming 6G cellular network introduces the concept of zero-energy CIoT devices, which do not require batteries or manual charging. This paper focuses on these devices, providing insight into their feasibility and practical implementation. The paper examines how CIoT devices use simultaneous wireless information and power transfer, beamforming, and backscatter communication techniques. It also analyzes the potential use of energy harvesting and power management in zero-energy CIoT devices. Furthermore, the paper explores how low-power transceivers can lower energy usage while maintaining dependable communication functions.

Cooperative intelligent transport systems (C-ITS) enable information to be shared wirelessly between vehicles and infrastructure in order to improve transport safety and efficiency. Delivering C-ITS services using existing cellular networks offers both financial and technological advantages, not least since these networks already offer many of the features needed by C-ITS, and since many vehicles on our roads are already connected to cellular networks. Still, C-ITS pose stringent requirements in terms of availability and latency on the underlying communication system; requirements that will be hard to meet for currently deployed 3G, LTE, and early-generation 5G systems. Through a series of experiments in the MONROE testbed (a cross-national, mobile broadband testbed), the present study demonstrates how cellular multi-access selection algorithms can provide close to 100 percent availability, and significantly reduce C-ITS transaction times. The study also proposes and evaluates a number of low-complexity, low-overhead single-access selection algorithms, and shows that it is possible to design such solutions so that they offer transaction times and availability levels that rival those of multi-access solutions.

Seamless wireless connectivity and low latency communication in mobility scenarios are two fundamental requirements in the fifth generation networks to effectively cover a broader range of use cases such as massive machine type communications and ultra-reliable low-latency communications for new industrial/potential users, as well as enhanced mobile broadband for conventional users. In this paper, we aim at measuring certain key performance indicators in mobility scenarios, highlighting some practical inefficiencies that require more careful consideration when designing and enhancing mobility features of 5G networks. We investigate the round trip time, uplink throughput and the impact of handover interruption time on these metrics. Our results are based on three months experiment campaign in a mix of rural, suburban, and urban environment using the geographically distributed MONROE platform. Based on the observed discrepancies between measured and standardized LTE performance, we conclude the paper with a discussion of techniques that may need more attention for mobility scenarios in fifth generation mobile networks.

Measuring and characterizing web page performance is a challenging task. When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult. Aiming at reproducibility, we present a large scale empirical study of web page performance collected in eleven commercial mobile networks spanning four countries. By digging into measurement from nearly two million web browsing sessions, we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance. We find that the impact of mobile broadband access is sizeable. For example, the median page load time using mobile broadband increases by a third compared to wired access. Mobility clearly stresses the system, with handover causing the most evident performance penalties. Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact. To understand the intertwining of all parameters, we adopt state-of-the-art statistical methods to identify the significance of different factors on the web performance. Our analysis confirms the importance of access technology and mobility context as well as webpage composition and browser. Our work highlights the importance of large-scale measurements. Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle. For this, we are releasing the dataset as open data for validation and further research.

Open experimentation with operational Mobile Broadband (MBB) networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need of innovative solutions for mobile communications. Even more, there is a strong need for objective data about stability and performance of MBB (e.g., 3G/4G) networks, and for tools that rigorously and scientifically assess their status. In this paper, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks. The MONROE platform enables accurate, realistic and meaningful assessment of the performance and reliability of 11 MBB networks in Europe. We report on our experience designing, implementing and testing the solution we propose for the platform. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. We describe and exemplify the capabilities of the platform and the wide variety of experiments that external users already perform using the system.

Seamless wireless connectivity in high mobility scenarios (> 300 km/h), is one of the fundamental key requirements for the future 5G networks. High speed train (HST) is one of the preferred mid-range transportation systems, and highlights the challenges of providing wireless connectivity in high mobility scenarios for the 5G networks. Advanced version of Long Term Evolution (LTE-A) from the Third Generation Partnership Project (3GPP) with peak data rate up to 100 Mbps in high mobility scenarios paved the road toward high quality and cost effective onboard Internet in HSTs. However, frequent handovers (HO) of large number of onboard users increase the service interruptions that in turn inevitably decrease the experienced quality of service (QoS). In this paper, according to the two-tier architecture of the HST wireless connectivity, we propose a novel and practically viable onboard traffic offloading mechanism among the HST carriages that effectively mitigates the service interruptions caused by frequent HOs of massive number of onboard users. The proposed architecture does not imply any change on the LTE network standardization. Conclusions are supported by numerical results for realistic LTE parameters and current HST settings.

Train-mounted aggregation routers that provide WiFi access to train passengers and bundle external communication over multiple cellular modems/links is an efficient way of providing communication services on trains. However, the characteristics of such systems have received limited attention in the literature. In this paper we address this gap by examining the communication characteristics of such systems based on a large data set gathered over six months from an operational Swedish railway system. We focus our examination on the relationship between per link throughput and train velocity. Using Levenberg- Marquardt non-linear regression a noticeable critical point is observed for an RS-SINR of around 12 dB. At this point the impact of increased train velocity on per link throughput changes from being negative to becoming positive. Using a machine learning approach we also explore the relative importance of several observed metrics in relation to per link throughput.

Communication performed with mobile devices will experience varying levels of connectivity as the communication device moves in and out of coverage. A subset of mobile communication devices operate under conditions where the connectivity is characterized by relatively short contact periods occurring intermittently. In this paper we propose a model to predict the amount of data that can be transferred during such short contact periods. The model includes aspects of the transport layer slow-start behavior and is validated using data from a long-running measurement campaign in the networks of four Swedish cellular networks. Further validation of the modeling assumptions is performed by employing a numerical optimization technique based on non-linear least squares regression using the iterative Levenberg-Marquardt approach. The model is then used to explore the relevant parameter space.

Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. In this demo paper, our goal is to showcase the potential of a novel multi-homed MBB platform for measuring, monitoring and assessing the performance of MBB services in an objective manner. Our platform, MONROE, is composed of hundreds of nodes scattered over four European countries and a backend system that collects the measurement results. Through a user-friendly web client, the experimenters can schedule and deploy their experiments. The platform further embeds traffic analysis tools for real-time traffic flow analysis and a powerful visualization tool.

This paper examines the efficiency of resource utilization with respect to short-lived TCP flows in various cellular networks. The examination is done from the vantage point of an end-user who would like to use as much as possible of the cellular transmission resources that are available at any given time, thus minimizing the delays associated with communication. Based on a comprehensive measurement campaign we first derive network characteristics with regards to base RTT, RTT under load, and average throughput. A protocol efficiency metric is introduced to capture how efficiently short TCP flows are in fact able to use the instantaneously available transmission resources in a cellular network. The measurements show that short TCP connections have low efficiency in 3.5G (HSPA+) and 4G (LTE) mobile broadband networks, and that the improved latency and throughput characteristics of 4G in relation to 3.5G nevertheless results in lower short-flow efficiency for 4G.

The existence of excessively large and too filled network buffers, known as bufferbloat, has recently gained attention as a major performance problem for delay-sensitive applications. One important network scenario where bufferbloat may occur is cellular networks. This paper investigates the interaction between TCP congestion control and buffering in cellular networks. Extensive measurements have been performed in commercial 3G, 3.5G and 4G cellular networks, with a mix of long and short TCP flows using the CUBIC, NewReno and Westwood+ congestion control algorithms. The results show that the completion times of short flows increase significantly when concurrent long flow traffic is introduced. This is caused by increased buffer occupancy from the long flows. In addition, for 3G and 3.5G the completion times are shown to depend significantly on the congestion control algorithms used for the background flows, with CUBIC leading to significantly larger completion times.