784 citations · 1.2k across the 33 of their papers we have counts for
34 papers
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…
RAN Functional Split Options for Integrated Terrestrial and Non-Terrestrial 6G Networks
Mohamed Rihan, Tim Due, MohammadAmin Vakilifard +2
Leveraging non-terrestrial platforms in 6G networks holds immense significance as it opens up opportunities to expand network coverage, enhance connectivity, and support a wide ran…
On-board Federated Learning for Satellite Clusters with Inter-Satellite Links
Nasrin Razmi, Bho Matthiesen, Armin Dekorsy +1
The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks, while simultaneously offe…
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…
Robust Precoding via Characteristic Functions for VSAT to Multi-Satellite Uplink Transmission
Maik Röper, Bho Matthiesen, Dirk Wübben +2
The uplink from a very small aperture terminal (VSAT) towards multiple satellites is considered, in this paper. VSATs can be equipped with multiple antennas, allowing parallel tran…
A Comparison between RSMA, SDMA, and OMA in Multibeam LEO Satellite Systems
Alea Schröder, Maik Röper, Dirk Wübben +3
Low Earth orbit (LEO) satellite systems enable close to global coverage and are therefore expected to become important pillars of future communication standards. However, a particu…
Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations
Nasrin Razmi, Bho Matthiesen, Armin Dekorsy +1
Distributed training of machine learning models directly on satellites in low Earth orbit (LEO) is considered. Based on a federated learning (FL) algorithm specifically targeted at…
Federated Learning in Satellite Constellations
Bho Matthiesen, Nasrin Razmi, Israel Leyva-Mayorga +2
Federated learning (FL) has recently emerged as a distributed machine learning paradigm for systems with limited and intermittent connectivity. This paper presents the new context…
Distributed Downlink Precoding and Equalization in Satellite Swarms
Maik Röper, Bho Matthiesen, Dirk Wübben +2
In this paper, we propose a novel approach for downlink transmission from a satellite swarm towards a ground station (GS). These swarms have the benefit of much higher spatial sepa…
NGSO Constellation Design for Global Connectivity
Israel Leyva-Mayorga, Beatriz Soret, Bho Matthiesen +4
Non-geostationary orbit (NGSO) satellite constellations represent a cornerstone in the NewSpace paradigm and thus have become one of the hottest topics for the industry, academia,…
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…
Globally Optimal Spectrum- and Energy-Efficient Beamforming for Rate Splitting Multiple Access
Bho Matthiesen, Yijie Mao, Armin Dekorsy +2
Rate splitting multiple access (RSMA) is a promising non-orthogonal transmission strategy for next-generation wireless networks. It has been shown to outperform existing multiple a…
Beamspace MIMO for Satellite Swarms
Maik Röper, Bho Matthiesen, Dirk Wübben +2
Systems of small distributed satellites in low Earth orbit (LEO) transmitting cooperatively to a multiple antenna ground station (GS) are investigated. These satellite swarms have…
On-Board Federated Learning for Dense LEO Constellations
Nasrin Razmi, Bho Matthiesen, Armin Dekorsy +1
Mega-constellations of small-size Low Earth Orbit (LEO) satellites are currently planned and deployed by various private and public entities. While global connectivity is the main…
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…