784 citations · 861 across the 28 of their papers we have counts for
29 papers
Accuracy of Joint Time-Based and Carrier-Phase Positioning in 5G Networks under Correlated Measurement Errors
Nahidul Islam, Mohammad Razzaghpour, Marwan Hammouda +2
High-accuracy positioning is critical for emerging applications such as autonomous driving, industrial automation, augmented reality, and smart cities. 3GPP Release 18 introduced c…
Optimized Sampling of Angle-Resolved Scatterometry Data Using End-to-End Compressed Learning Model for Nanograss Deficiency Detection
Mehdi Abdollahpour, Carsten Bockelmann, Armin Dekorsy
Reliable inspection of nanosurfaces is essential to ensure the quality of nanostructure manufacturing. Angle-resolved scatterometry provides a non-invasive inspection method that c…
Model-Free Robust Beamforming in Satellite Downlink using Reinforcement Learning
Alea Schröder, Steffen Gracla, Carsten Bockelmann +2
Satellite-based communications are expected to be a substantial future market in 6G networks. As satellite constellations grow denser and transmission resources remain limited, fre…
Semantic Communication for Task Execution and Data Reconstruction in Multi-View Scenarios
Maximilian H. V. Tillmann, Avinash Kankari, Carsten Bockelmann +1
Semantic communication has gained significant attention with the advances in machine learning. Most semantic communication works focus on either task execution or data reconstructi…
Compressed Learning for Nanosurface Deficiency Recognition Using Angle-resolved Scatterometry Data
Mehdi Abdollahpour, Carsten Bockelmann, Tajim Md Hasibur Rahman +2
Nanoscale manufacturing requires high-precision surface inspection to guarantee the quality of the produced nanostructures. For production environments, angle-resolved scatterometr…
CQI-Based Interference Prediction for Link Adaptation in Industrial Sub-networks
Pramesh Gautam, Ravi Sharan Bhagavathula, Paolo Baracca +3
We propose a novel interference prediction scheme to improve link adaptation (LA) in densely deployed industrial sub-networks (SNs) with high-reliability and low-latency communicat…
Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks
Pramesh Gautam, Sushmita Sapkota, Carsten Bockelmann +2
Interference prediction that accounts for extreme and rare events remains a key challenge for ultra-densely deployed sub-networks (SNs) requiring hyper-reliable low-latency communi…
Semantic Communication for Cooperative Multi-Tasking over Rate-Limited Wireless Channels with Implicit Optimal Prior
Ahmad Halimi Razlighi, Carsten Bockelmann, Armin Dekorsy
In this work, we expand the cooperative multi-task semantic communication framework (CMT-SemCom) introduced in [1], which divides the semantic encoder on the transmitter side into…
Dynamic Interference Prediction for In-X 6G Sub-networks
Pramesh Gautam, Ravi Sharan Bhagavathula, Paolo Baracca +3
The sixth generation (6G) industrial Sub-networks (SNs) face several challenges in meeting extreme latency and reliability requirements in the order of 0.1-1 ms and 99.999 -to-99.9…
Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources
Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck +2
In this paper, we explore a multi-task semantic communication (SemCom) system for distributed sources, extending the existing focus on collaborative single-task execution. We build…
Throughput Requirements for RAN Functional Splits in 3D-Networks
MohammadAmin Vakilifard, Tim Düe, Mohammad Rihan +4
The rapid growth of non-terrestrial communication necessitates its integration with existing terrestrial networks, as highlighted in 3GPP Releases 16 and 17. This paper analyses th…
Instantaneous Bandwidth Estimation from Level-Crossing Samples via LSTM-based Encoder-Decoder Architecture
Johannes Königs, Carsten Bockelmann, Armin Dekorsy
This paper presents an approach for instantaneous bandwidth estimation from level-crossing (LC) samples using a long short-term memory (LSTM) encoder-decoder architecture. LC sampl…
Semantic Communication for Cooperative Multi-Task Processing over Wireless Networks
Ahmad Halimi Razlighi, Carsten Bockelmann, Armin Dekorsy
In this paper, we investigated semantic communication for multi-task processing using an information-theoretic approach. We introduced the concept of a "semantic source", allowing…
Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement Learning
Alea Schröder, Steffen Gracla, Maik Röper +3
Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications. Space-Division Multiple Access (SDMA) precoding is a method that mitigates int…
Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient
Edgar Beck, Carsten Bockelmann, Armin Dekorsy
Following the recent success of Machine Learning tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shann…
A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation
Steffen Gracla, Carsten Bockelmann, Armin Dekorsy
With increasing complexity of modern communication systems, machine learning algorithms have become a focal point of research. However, performance demands have tightened in parall…
On the Importance of Exploration for Real Life Learned Algorithms
Steffen Gracla, Carsten Bockelmann, Armin Dekorsy
The quality of data driven learning algorithms scales significantly with the quality of data available. One of the most straight-forward ways to generate good data is to sample or…
Robust Deep Reinforcement Learning Scheduling via Weight Anchoring
Steffen Gracla, Edgar Beck, Carsten Bockelmann +1
Questions remain on the robustness of data-driven learning methods when crossing the gap from simulation to reality. We utilize weight anchoring, a method known from continual lear…
Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients
Steffen Gracla, Edgar Beck, Carsten Bockelmann +1
Advances in mobile communication capabilities open the door for closer integration of pre-hospital and in-hospital care processes. For example, medical specialists can be enabled t…
Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications
Steffen Gracla, Alea Schröder, Maik Röper +3
Direct Low Earth Orbit satellite-to-handheld links are expected to be part of a new era in satellite communications. Space-Division Multiple Access precoding is a technique that re…
Energy and Bandwidth Efficiency of Event-Based Communication
Christopher Willuweit, Carsten Bockelmann, Armin Dekorsy
Wireless sensor nodes need a drastically reduced technical complexity to fit constraints of future applications. Reducing complexity often results in a degradation of energy and ba…
Semantic Information Recovery in Wireless Networks
Edgar Beck, Carsten Bockelmann, Armin Dekorsy
Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks wi…
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care
Steffen Gracla, Edgar Beck, Carsten Bockelmann +1
Greater capabilities of mobile communications technology enable interconnection of on-site medical care at a scale previously unavailable. However, embedding such critical, demandi…
CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity
Edgar Beck, Carsten Bockelmann, Armin Dekorsy
Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detec…