94 citations · 587 across the 47 of their papers we have counts for
10 papers · 1 filter
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…