Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.Comment: 18 pages, 12 Figures. Accepted on IEEE Acces

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2

Molecular ecological chemistry in Arctic fjords at different stages of deglaciation, Cruise No. MSM56, July 2 - July 25, 2016, Longyearbyen (Svalbard, Norway) - Reykjavík (Iceland)

The 22 participating scientists from Germany, Norway, the Netherlands, Denmark, Sweden, Finland and the United States covered scientific expertise in (micro-) biology, chemistry, and oceanography. Apart from aerosol and rainwater collection, which was applied to assess atmospheric deposition, sampling was restricted to the water column. Phyto and zooplankton were sampled by vertical net hauls using a plankton net, multinet and a pump system for the filtration of large water volumes to collect different size classes of phytoplankton, followed by DNA and RNA extraction. Phytoplankton was also characterized and quantified onboard by microscopy and flow cytometry. Primary productivity was assessed in incubations in the isotope container using radiocarbon labels. Clonal cultures were established to identify selected key species. Bacterial abundance, community composition and production were also determined onboard. Chemical sampling and analytical parameters, most of which taken from the CTD water sampler, will be measured back in the home labs. The final dataset will cover inorganic nutrients, oxygen concentration, dissolved inorganic carbon, total alkalinity, He/Ne ratios for the estimation of basal melt water, δ18O for the contribution of meteoric water, particulate and dissolved organic carbon and nitrogen, optical properties (fluorescence), molecular characterization and radiocarbon age of organic matter. A FerryBox system continuously recorded surface water information on turbidity, chlorophyll fluorescence, temperature, salinity, colored dissolved organic matter and salinity. At each station, salinity and temperature profiles were recorded by the CTD system and by profiler deployments, which also recorded the spectral light profile in the water column. The vertical material flux was investigated by the deployment of drifting sediment traps, a camera system and a marine snow catcher