94 citations · 587 across the 47 of their papers we have counts for
47 papers
System Modeling and Performance Evaluation of Predictive QoS for Future Tele-Operated Driving
Hendrik Schippers, Cedrik Schüler, Benjamin Sliwa +1
Future Tele-operated Driving (ToD) applications place challenging Quality of Service (QoS) demands on existing mobile communication networks that are of highly important to comply…
DRaGon: Mining Latent Radio Channel Information from Geographical Data Leveraging Deep Learning
Benjamin Sliwa, Melina Geis, Caner Bektas +3
Radio channel modeling is one of the most fundamental aspects in the process of designing, optimizing, and simulating wireless communication networks. In this field, long-establish…
Machine Learning-Enabled Data Rate Prediction for 5G NSA Vehicle-to-Cloud Communications
Benjamin Sliwa, Hendrik Schippers, Christian Wietfeld
In order to satisfy the ever-growing Quality of Service (QoS) requirements of innovative services, cellular communication networks are constantly evolving. Recently, the 5G NonStan…
Modeling and Simulation of Reconfigurable Intelligent Surfaces for Hybrid Aerial and Ground-based Vehicular Communications
Karsten Heimann, Benjamin Sliwa, Manuel Patchou +1
The requirements of vehicular communications grow with increasing level of automated driving and future applications of intelligent transportation systems (ITS). Beside the ever-in…
Towards Machine Learning-Enabled Context Adaption for Reliable Aerial Mesh Routing
Cedrik Schüler, Benjamin Sliwa, Christian Wietfeld
In this paper, we present Context-Adaptive PARRoT (CA-PARRoT) as an extension of our previous work Predictive Ad-hoc Routing fueled by Reinforcement learning and Trajectory knowled…
Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G Network
Benjamin Sliwa, Rick Adam, Christian Wietfeld
Vehicular big data is anticipated to become the "new oil" of the automotive industry which fuels the development of novel crowdsensing-enabled services. However, the tremendous amo…
A Low Cost Modular Radio Tomography System for Bicycle and Vehicle Detection and Classification
Marcus Haferkamp, Benjamin Sliwa, Christian Wietfeld
The advancing deployment of ubiquitous Internet of Things (IoT)-powered vehicle detection and classification systems will successively turn the existing road infrastructure into a…
Flying Robots for Safe and Efficient Parcel Delivery Within the COVID-19 Pandemic
Manuel Patchou, Benjamin Sliwa, Christian Wietfeld
The integration of small-scale Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITSs) will empower novel smart-city applications and services. After the unf…
PARRoT: Predictive Ad-hoc Routing Fueled by Reinforcement Learning and Trajectory Knowledge
Benjamin Sliwa, Cedrik Schüler, Manuel Patchou +1
Swarms of collaborating Unmanned Aerial Vehicles (UAVs) that utilize ad-hoc networking technologies for coordinating their actions offer the potential to catalyze emerging research…
Deep Learning-based Signal Strength Prediction Using Geographical Images and Expert Knowledge
Jakob Thrane, Benjamin Sliwa, Christian Wietfeld +1
Methods for accurate prediction of radio signal quality parameters are crucial for optimization of mobile networks, and a necessity for future autonomous driving solutions. The pow…
The Best of Both Worlds: Hybrid Data-Driven and Model-Based Vehicular Network Simulation
Benjamin Sliwa, Manuel Patchou, Christian Wietfeld
The analysis of the end-to-end behavior of novel mobile communication methods in concrete evaluation scenarios frequently results in a methodological dilemma: Real world measuremen…
Acting Selfish for the Good of All: Contextual Bandits for Resource-Efficient Transmission of Vehicular Sensor Data
Benjamin Sliwa, Rick Adam, Christian Wietfeld
as a novel client-based method for resource-efficient opportunistic transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid approach which brings together all…
6G White Paper on Machine Learning in Wireless Communication Networks
Samad Ali, Walid Saad, Nandana Rajatheva +24
The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of socie…
The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting
Benjamin Sliwa, Niko Piatkowski, Christian Wietfeld
Ubiquitously deployed Internet of Things (IoT)- based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will t…
Simulating Hybrid Aerial- and Ground-based Vehicular Networks with ns-3 and LIMoSim
Benjamin Sliwa, Manuel Patchou, Karsten Heimann +1
Integrating Unmanned Aerial Vehicles (UAVs) into future Intelligent Transportation Systems (ITSs) allows to exploit their unique mobility potentials for improving the performance o…
LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations
Benjamin Sliwa, Nico Piatkowski, Christian Wietfeld
Exploiting big data knowledge on small devices will pave the way for building truly cognitive Internet of Things (IoT) systems. Although machine learning has led to great advanceme…
Towards Cooperative Data Rate Prediction for Future Mobile and Vehicular 6G Networks
Benjamin Sliwa, Robert Falkenberg, Christian Wietfeld
Machine learning-based data rate prediction is one of the key drivers for anticipatory mobile networking with applications such as dynamic Radio Access Technology (RAT) selection,…
A Reinforcement Learning Approach for Efficient Opportunistic Vehicle-to-Cloud Data Transfer
Benjamin Sliwa, Christian Wietfeld
Vehicular crowdsensing is anticipated to become a key catalyst for data-driven optimization in the Intelligent Transportation System (ITS) domain. Yet, the expected growth in massi…
Data-driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems
Benjamin Sliwa, Christian Wietfeld
The provision of reliable connectivity is envisioned as a key enabler for future autonomous driving. Anticipatory communication techniques have been proposed for proactively consid…
Unmanned Aerial Vehicles in Logistics: Efficiency Gains and Communication Performance of Hybrid Combinations of Ground and Aerial Vehicles
Manuel Patchou, Benjamin Sliwa, Christian Wietfeld
Unmanned Aerial Vehicles (UAVs) have drastically gained popularity in various Intelligent Transportation System (ITS) applications to improve the safety and efficiency of transport…
Lightweight Simulation of Hybrid Aerial- and Ground-based Vehicular Communication Networks
Benjamin Sliwa, Manuel Patchou, Christian Wietfeld
Cooperating small-scale Unmanned Aerial Vehicles (UAVs) will open up new application fields within next-generation Intelligent Transportation Sytems (ITSs), e.g., airborne near fie…
Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction
Benjamin Sliwa, Robert Falkenberg, Thomas Liebig +2
The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. Ho…
Towards Data-driven Simulation of End-to-end Network Performance Indicators
Benjamin Sliwa, Christian Wietfeld
Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the hig…
Empirical Analysis of Client-based Network Quality Prediction in Vehicular Multi-MNO Networks
Benjamin Sliwa, Christian Wietfeld
Multi-Mobile Network Operator (MNO) networking is a promising method to exploit the joint force of multiple available cellular data connections within vehicular networks. By applyi…