News and Announcements

  • New IEEE Transactions on Mobile Computing article

    September 26, 2026

    Our article Distributed Fast and Fair Physical-to-Virtual Communications in Digital Twin Edge Networks has been accepted for publication in IEEE Transactions on Mobile Computing. Physical-to-Virtual Communication (PTVC) is essential for Digital Twin Edge Networks (DITENs) to synchronize physical objects with their digital twins. In practice, update messages from physical objects to their digital twins often have different priorities based on their urgency, necessitating priority-aware scheduling to ensure that critical messages are updated with low latency. While existing approaches can achieve fairness by collecting global priority information and solving optimization problems, when the number and priorities of nodes change, it takes substantial communication resources to timely update the global priority information in large-scale edge networks. To address these limitations, we propose DFF-PTVC, a fully distributed fast and fair communication algorithm where each end node makes scheduling decisions without requiring knowledge of other nodes’ priorities. Through an edge-end collaborative framework, the edge server adaptively broadcasts a meta probability to manage channel contention, while each end node autonomously determines its transmission probability by combining the meta probability with its local priority. We rigorously prove that DFF-PTVC achieves asymptotically optimal throughput of Ω(1), guarantees expected latency of O(Kn/k) for messages with priority k, and ensures weighted fairness, where n is the number of end nodes and K is the upper bound of message priority. We further develop DFF-PTVC-DRL, a deep reinforcement learning variant for enhanced performance when the edge server has sufficient computational resources. Extensive simulations demonstrate that our distributed algorithms, using only local priority information, achieve performance within a small constant factor of the centralized optimal solution that requires global network knowledge.
    (link to more information)

  • TKN Team at ACM NanoCom 2026

    September 23, 2026

    Our team presented their research at the 13th ACM International Conference on Nanoscale Computing and Communication (NanoCom 2026) at Memorial University in St. John's, Canada. Sunasheer Bhattacharjee presented our paper on breath pattern classification using machine learning in Breath-Source Localization in Air-Based Molecular Communication: A Learning-Driven Proof-of-Concept. Jorge Torres Gómez lead a tutorial session on Artificial Intelligence in Microfluidic Circuits: From Theory to Hands-On Design. Lisa Y. Debus presented our poster on the simulation of the HPA axis for health digital twins in Full-Body Particle Simulation of the HPA Axis: Comparison to Mathematical Models and Experimental Results.

  • New IEEE Transactions on Networking article

    September 22, 2026

    Our article One-Shot Federated Model Editing for Device Dynamics at the Edge has been accepted for publication in IEEE Transactions on Networking. Standard federated learning (FL) requires persistent device connectivity across multiple communication rounds, making it unsuitable for dynamic environments such as vehicular networks, where devices frequently join and leave. One-shot FL offers a promising alternative by completing training in a single communication round. However, one-shot FL suffers from model solidification and cannot readily adapt to subsequent client dynamics, including new client contributions and the withdrawal of previous contributions. We therefore consider exact model editing, which removes withdrawn contributions through unlearning or incorporates newly contributed ones through incremental learning without retraining from scratch. We identify that efficient editing fundamentally hinges on learning stability. To this end, we propose F-SOSA, the first stable one-shot FL algorithm that enables rapid and exact model editing. F-SOSA leverages sub-sampling of local models with carefully tuned rate for stable aggregation and performs curvature-aware model fusion to preserve utility. We prove that F-SOSA achieves vanishing training error and reasonably small generalization error bounds. Building on this stability, we further develop two single-round editing algorithms: F-SOSA-U for unlearning and F-SOSA-I for incremental learning. They ensure that the edited model, along with all intermediate algorithmic states, is statistically indistinguishable from retraining while incurring only an expected O(ρ) fraction of its cost, where ρ quantifies stability. Experiments demonstrate that our approach matches retraining performance while reducing cumulative time and communication overhead by at least 36% and 22%, respectively, under realistic urban mobility simulations.
    (link to more information)

  • Mengfan Wu just defended his PhD - congratulations!

    September 21, 2026

    Mengfan Wu successfully defended his PhD on September 21, 2029. His dissertation is titled Scalable Federated Learning in Heterogeneous Networks: Strategies for Asynchronous, Hierarchical, and Decentralized Systems. He was awarded a Dr.-Ing. degree from TU Berlin.
    (link to more information)

  • TKN Team at Berlin 6G Conference 2026

    September 10, 2026

    The TKN team joined the 4th Berlin 6G Conference to present our latest research spanning AI-native communications, reconfigurable intelligent surfaces (RIS), and integrated sensing and communication (ISAC). As part of the xG-RIC Project, we enjoyed exchanging ideas with the community on advancing open, resilient, and intelligent 6G infrastructure. Our team member Joana Angjo presented recent results from our testbed implementation "NLoS Sensing with Reconfigurable Intelligent Surfaces."

  • XRain Selected for Deutsche Telekom Incubator Program

    September 09, 2026

    Our team member Osman Tugay Basaran’s deep-tech startup, XRain—developing a Trustworthy AI Operating System for Autonomous Next-Generation Wireless Communications(6G and Beyond)—has been selected to join Deutsche Telekom’s hubraum Tangible Tomorrow Program. The program connects high-potential startups directly with Deutsche Telekom’s business and technology units to validate practical use cases and accelerate industry collaboration. Through this engagement, XRain will work alongside D-Telekom’s teams to bring its trustworthy autonomous network solutions closer to real-world deployment for public operators, smart hospitals, and smart factories.

  • Keynote at GTTI Annual Meeting 2026

    September 09, 2026

    Falko Dressler gave a keynote titled Resilient Wireless Communication and Computing in the 6G Era at the GTTI (Gruppo Telecomunicazioni e Tecnologie dell'Informazione) Annual Meeting 2026.
    (link to more information)

  • New DFG project HemoCom

    September 04, 2026
    Our proposal HemoCom: Hemodynamics for Molecular Communication in the Flow of Life has been accepted for funding by the German research foundation DFG. In this project, we will explore molecular communication channel characterization in blood, spatio-temporal detection of molecular communication signals, multi-scale simulation and digital twin of molecular communication systems, and application-oriented system demonstration and feedback-based operation.
    (link to more information)

  • IEEE ComSoc MBMC Startup Session

    September 03, 2026

    Our team members Dilara Aktaş and Jorge Torres Gómez organized the IEEE ComSoc MBMC Startup Session, “From Molecular Communication Research to Startups and Real-World Impact,” on September 3, 2026. The online session brought together researchers, startup experts, and members of the molecular communication community to discuss entrepreneurship, commercialization, funding, and the path from research ideas to real-world impact. Participants actively joined the talks and Q&A discussions. We thank Alyssa Kirst, Prof. Frank H. P. Fitzek, Pit Hofmann, and our team members Sunasheer Bhattacharjee and Osman Tugay Basaran for sharing their valuable experiences and insights.

  • New IEEE Open Journal of the Communications Society article

    August 31, 2026

    Our article Joint RIS-RSMA Optimization for Asynchronous Multi-user Cell-Free MIMO has been accepted for publication in IEEE Open Journal of the Communications Society. Cell-free multiple-input multiple-output (CF-MIMO) systems assisted by reconfigurable intelligent surfaces (RISs) and rate-splitting multiple access (RSMA) are promising for interference management in distributed sixth-generation (6G) networks. In practical CF-MIMO deployments, independent access-point (AP) oscillators and unequal propagation delays introduce AP-dependent phase variations that reduce coherent combining and degrade precoding performance. This paper develops a unified RIS-RSMA optimization framework for asynchronous downlink CF-MIMO. The RSMA common precoder, private precoders, and passive RIS phase shifts are jointly optimized within a sum-rate maximization problem subject to total transmit-power, per-user quality-of-service, and RIS unit-modulus constraints. The resulting non-convex problem is addressed through a weighted minimum mean-square error (WMMSE)-based alternating optimization framework with Gauss–Seidel coordinate optimization for the RIS phase subproblem. An analytical common-stream power expression is derived to explain the limited benefit of decoupled common-precoder designs. The analysis shows that independent oscillator phase drifts at the APs attenuate coherent cross-AP combining terms and can restrict the common rate through the weakest-user decoding constraint. In the default overloaded CF-MIMO configuration, the tested heuristic common-precoder schemes provide less than 0.2% gain over the corresponding optimized space-division multiple access (SDMA) baseline when combined with WMMSE-optimized private precoders. In contrast, the proposed joint RSMA design achieves approximately 12–15% sum-rate gain at moderate-to-high transmit powers and retains a similar high-power advantage in a larger-scale validation. Targeted comparisons using WMMSE and regularized zero-forcing (RZF) private precoders, together with the common-power allocation results, support the need for joint common/private precoder design. Additional results show that the gain persists across the tested RIS sizes, oscillator phase-noise variances, Rician factors, loading ratios, channel realizations, and multi-RIS deployments. Distributed multi-RIS deployment also improves spatial coverage uniformity under a fixed total element budget.
    (link to more information)

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Last modified: 2024-04-28