News and Announcements

• 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)

• Paper Presentation at IEEE FINE 2026

  August 30, 2026
  
  Our team member Tehmina Bibi presented our paper titled PhySynth: A Physics-Based Synthetic UWB/IMU Data Generator for Training of ML-based Tracking at the IEEE International Conference on Future and Intelligent Networking (FINE 2026), which took place in Osaka, Japan.
  

• New IEEE Transactions on Mobile Computing article

  August 16, 2026
  
  Our article Efficient and Green Service Function Chain Deployment and Scheduling for Mobile Edge Computing has been accepted for publication in IEEE Transactions on Mobile Computing. Service Function Chain (SFC) decomposes a user's overall service into multiple service components and links them in an orderly manner, providing more flexible, efficient, and responsive services for compute-intensive applications in mobile edge computing. Existing research primarily focuses on optimizing the deployment cost and system efficiency of SFC. However, the dynamic nature of user trajectories, along with the absence of adaptive scheduling and service component sharing in complex and heterogeneous environments, hinders existing approaches from achieving optimal resource efficiency. To achieve adaptive optimization of SFC deployment and efficient component sharing across multiple SFCs, we propose an SFC Deployment and Scheduling strategy based on the Soft Actor-Critic (SAC) algorithm, named SAC-DS. The proposed SAC-DS strategy first employs a Long Short-Term Memory (LSTM) model to predict user trajectories, thereby accurately forecasting future service demands. Based on these predictions, an SAC-based agent is trained to dynamically optimize SFC deployment and scheduling policies in real time. SAC-DS integrates SFC deployment, component sharing, and scheduling into a unified framework, effectively improving system throughput while minimizing overall deployment costs, thereby enhancing user experience. Compared with existing baseline methods, SAC-DS improves network throughput by 3.1%–18.1% and reduces average cost by 1.8%-10.0%.
  (link to more information)

• Special issue in IEEE TMBMC

  August 04, 2026
  Our team member Jorge Torres Gómez is leading a special issue for the IEEE Trans. On Molecular, Biological, and Multi-Scale Communications. As lead guest editor he is teaming with life science partners at Charité Universitätsmedizin and Michigan State University. The special issue is planned to publish contributions that demonstrate the creation of datasets for molecular communication scenarios.
  

• New IEEE Transactions on Mobile Computing article

  July 31, 2026
  
  Our article Edge-assisted Video Object Detection with Adaptive Resource Allocation and Model Selection has been accepted for publication in IEEE Transactions on Mobile Computing. With the rise of applications such as facial recognition and intelligent monitoring, demand for video object detection has surged. To optimize detection performance, existing works primarily focus on either video transmission or processing independently, often overlooking small object detection, which is particularly difficult for resource-limited terminals. To address these limitations, we propose a solution that leverages edge servers to assist video object detection, enabling real-time and high-precision performance under resource constraints. We introduce Adaptive-EVOD, the first edge-assisted video detection system that optimizes communication and computation simultaneously through adaptive resolution and frame rate adjustment, resource allocation, and model selection strategies, with a particular emphasis on small object detection. Specifically, Adaptive-EVOD introduces three core innovations: (1) proposing a detection strategy for small objects to enhance precision; (2) employing deep reinforcement learning to dynamically determine detection location, resolution, and frame rate based on heterogeneous environments; and (3) applying Bayesian optimization to dynamically allocate GPU resources and select Low-Rank Adaptation (LoRA) ranks for detection models adaptively.
  (link to more information)

• Interdisciplinary Workshop on Precision Medicine in the Era of the Internet of Bio-Nano Things

  July 30, 2026
  
  On July 30 - 31, 2026, our TKNano group together with the Institute of Telematics of the Universität zu Lübeck organized an interdisciplinary workshop on Precision Medicine in the Era of the Internet of Bio-Nano Things. At the MediaDocks in Lübeck, we discussed future in-body applications of molecular communication together with many colleagues from biomedical engineering, biology, and medicine. Saswati Pal and Jorge Torres Gomez presented our current research on ML-based cancer localization, smart drug-delivery systems, and future goals in our DFG project NaboCom III.
  

• New DFG project ML4WIFI2

  July 27, 2026
  Our proposal Machine Learning-based Wi-Fi Optimization 2 (ML4WiFi2) has been accepted for funding by the German research foundation DFG. In this project, we will explore learning-based approaches to optimize resource allocation in WiFi networks as well as the coexistence with legacy accesss points.
  (link to more information)

• New IEEE Internet of Things Journal article

  July 19, 2026
  
  Our article Wi-Lo++: WiFi-to-LoRa Cross-Technology Communication with Multi-Packet Emulation has been accepted for publication in IEEE Internet of Things Journal. This work presents Wi-Lo++, a WiFi-to-LoRa CTC mechanism. In contrast to existing WiFi-to-LoRa CTC approaches, it removes the constraint of the maximum packet length of WiFi and therefore can emulate LoRa transmissions with higher spreading factors (SFs). This is achieved by emulating a single LoRa frame through a train of consecutive IEEE 802.11b frames, each emulating only a part of the overall LoRa waveform while preserving symbol-level continuity across packet boundaries. In order to mitigate waveform distortions caused by the variety of immutable WiFi preambles and headers, we shift these distortions into less critical regions of the LoRa waveform. Using a second WiFi interface, these distortions can be further reduced further using overlapping WiFi frames.
  (link to more information)

• Tutorial at IEEE NetSoft 2026

  July 03, 2026
  
  Our member Doğanalp Ergenç presented a tutorial lecture on "IEEE 802.1 Time-Sensitive Networking Beyond Theory: Convergence, Resilience, and Practical Insights" at the 12th IEEE International Conference on Network Softwarization (NetSoft 2026), which was held in Berlin, Germany.
  

• Keynote at IEEE GreenNet 2026

  July 03, 2026
  
  Falko Dressler gave a keynote titled 6G Edge Computing: Improving Coverage and Resilience at the 5th IEEE International Workshop on Green and Sustainable Networking (GreenNet 2026), which was held in Berlin, Germany.
  (link to more information)

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