News and Announcements

• TKN at WONS 2026

  March 04, 2026
  
  Our team member Dr. Doganalp Ergenc and guest researcher Elena Tonini presented their works at WONS 2026 in Switzerland. In his paper Multi-Link Scheduling with Restricted Target Wake Time in Wi-Fi 7, Doganalp presents a novel time-sensitive scheduling approach levaraging the new features of Wi-Fi 7. In Leveraging Mutual Information in Stochastic CSI Analysis for Wi-Fi Sensing, Elena investigates the existence of mutual information across different CSI measurements in the context of Wi-Fi sensing. Last but not least, our external researchers Laura Finarelli and Berk Buzcu took part in the conference as web chair and local organizer, respectively. We thank and congratulate all our researchers for their contributions!
  

• New IEEE Transactions on Control Systems Technology article

  March 03, 2026
  
  Our article Decentralized Model Predictive Control for Platooning: Enhancing Human-Driver Collaboration has been accepted for publication in IEEE Transactions on Control Systems Technology. Recent advances in cooperative adaptive cruise control have demonstrated the potential for vehicle platooning to revolutionize road transportation through enhanced safety, reduced congestion, and improved energy efficiency. While autonomous vehicle technology continues to evolve rapidly, current regulatory frameworks and safety considerations necessitate the human driver supervision. This creates a unique challenge in developing control systems that can effectively balance autonomous operation with human intervention. To enhance the human-driver collaboration with autonomous vehicle platooning, in this paper, we present a novel decentralized model predictive control framework that explicitly incorporates human-driver interaction while maintaining desired inter-vehicle distances and velocities in platoon formations. This framework employs a distributed architecture where each vehicle operates independently and exchanges local measurements through vehicle-to-vehicle communication. To overcome the inherent unreliability of wireless communications in real-world scenarios, we develop a robust distributed state estimation strategy. This approach enables each vehicle to combine local sensor measurements with received data to construct accurate estimates of the full platoon state. Based on these estimates, vehicles compute optimal control actions locally while achieving performance comparable to an ideal centralized controller with perfect communication.
  (link to more information)

• Presentation at xG-Incubator Networking Event

  February 20, 2026
  
  At the xG-Incubator networking event, our team member Jorge Torres Gómez presented the BACT-ID device concept, a microfluidic circuit for early detection of bacterial infections. The core idea is to embed physical signal processing directly into the geometry of a microfluidic chip, allowing it to amplify weak biomarker signals without increasing power consumption or system complexity. The concept integrates micro-sampling, BioFET sensing, and Bluetooth connectivity into a portable device. We aim to significantly improve sensitivity while reducing false positives to enable faster, more reliable detection of pathogen-specific biomarkers. Such a device would be used for applications including wound monitoring, sepsis screening, and antibiotic testing.
  

• New staff member: Dr. Dilara Aktas

  February 20, 2026
  
  We welcome Dr. Dilara Aktas who joined our group in February 2026.
  

• Plenary Talk at IEEE ICNC 2026

  February 17, 2026
  
  Falko Dressler gave a plenary talk titled Virtualized Edge Computing: Bridging Communications, Computing, and AI at the IEEE International Conference on Computing, Networking and Communications (ICNC 2026), which was held in Maui, HI.
  (link to more information)

• New IEEE Transactions on Parallel and Distributed Systems article

  February 16, 2026
  
  Our article Fed-Grow: Federating to Grow Transformers for Resource-Constrained Users without Model Sharing has been accepted for publication in IEEE Transactions on Parallel and Distributed Systems. The growing resource demands of large-scale transformer models pose significant challenges for resource-constrained users, particularly in distributed environments. To address this issue, we propose a federated learning framework called Fed-Grow, which enables multiple participants to collaboratively learn a lightweight scaling operation that transfers knowledge from pretrained small models to a large transformer model. In Fed-Grow, we introduce the Dual-LiGO (Dual Linear Growth Operator) architecture, consisting of Local-LiGO and Global-LiGO components. Local-LiGO addresses model heterogeneity by adapting each participant’s pre-trained model to a common intermediate form, while Global-LiGO facilitates knowledge sharing across participants without sharing local models or raw data, ensuring privacy preservation. This federated approach offers a scalable solution for growing large transformers in a distributed manner, where only the Global-LiGO is shared, significantly reducing communication overhead while maintaining comparable model performance under the same communication constraints.
  (link to more information)

• Tutorials at IEEE CCNC 2026

  January 09, 2026
  TKN presented two tutorial lectures at the IEEE Consumer Communications and Networking Conference (CCNC 2026), which was held in Las Vegas, NV. Doğanalp Ergenç and Nurefşan Sertbaş Bülbül talked about IEEE 802.1 Time-Sensitive Networking Beyond Theory: Convergence, Resilience, and Practical Insights. In parallel, Falko Dressler and Onur Altintas talked about Cooperative Computing and AI on Cars using 6G Virtualized Edge Computing.
  

• Keynote at IEEE ACM 2026

  January 09, 2026
  
  Falko Dressler gave a keynote titled Virtualized Edge Computing for Resilient Edge-AI at the 1st International Workshop on AI-Native Connected Mobility (ACM 2026), which was held in Las Vegas, NV.
  (link to more information)

• New BMBF project xG-RIC

  January 05, 2026
  
  We are happy to be part of the xG Research and Innovation Cluster (xG-RIC). In this project, we will explore AI-native ORAN solutions, channel prediction techniques, the use of intelligent reflective surfaces, and advanced edge computing.
  (link to more information)

• New IEEE Transactions on Mobile Computing article

  December 30, 2025
  
  Our article Fed-RAA: Resource-Adaptive Asynchronous Federated Edge Learning with Theoretical Guarantee has been accepted for publication in IEEE Transactions on Mobile Computing. This paper studies an efficient federated learning (FL) problem involving multiple edge-based clients with heterogeneous constrained resources. Compared with numerous training parameters, the computing and communication resources of clients in edge scenarios are usually insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heterogeneous resources may result in the straggler problem, which delays the convergence of FL. To address these issues, we propose Fed-RAA: a Resource-Adaptive Asynchronous Federated learning algorithm. Different from vanilla FL methods, where all parameters are trained by each participating client regardless of resource diversity, Fed-RAA adaptively allocates submodels of the global model to clients based on their computing and communication capabilities. Each client then individually trains its assigned submodel and asynchronously uploads the updated result. Theoretical analysis confirms the convergence of our approach. Additionally, an online greedy-based algorithm is designed for asynchronous submodel assignment in Fed-RAA, improving the convergence of Fed-RAA by optimal minimization on the training delay bound of submodels. Compared to state-of-the-art methods, our Fed-RAA algorithm reduces the time required to achieve the target accuracy by an average of 30.89%, demonstrating its superior efficiency on heterogeneous constrained computing and communication resources. To the best of our knowledge, this paper is the first resource-adaptive asynchronous method for submodel-based FL with guaranteed theoretical convergence.
  (link to more information)

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