arXiv subjects

Wolfgang Kellerer

Publications and source records attributed to Wolfgang Kellerer.

At least 19 recordsLinked to original sources

arXiv2026

Talking Ink: A Flow-Based Multi-Molecule Molecular Communication Testbed for Effective Channel Modeling and Detector Benchmarking

Practical molecular communication (MC) testbeds are essential for translating theoretical concepts toward future applications while retaining physical realism, accessibility, and experimental repeatability. We present a flowbased MC experimental system for real-time multi-molecule (MUMO) transmission using cyan, magenta, and yellow inks as distinguishable molecule-like signaling carriers. Three micropumps and a multi-needle injection system release the inks into a background-flow channel, and a non-invasive spectral sensor estimates received color traces at variable transmitter-receiver distances. The platform supports measurements of isolated-pulse responses across distances. We interpret these measurements as effective end-to-end channel impulse responses (CIRs) shaped by finite release, flow propagation, and receiver readout, and compare two compact model families. Both reproduce much of the dominant arrival timing and pulse shape, while late-tail mismatch remains the main limitation. We connect this channel characterization to communication performance through continuous MUMO on-off keying (OOK) payload measurements. The resulting retrospective benchmark with full-data parameter optimization includes traceonly and CIR-assisted methods. We introduce modified energydifference detection (MEDD), an adaptive extension of standard energy-difference detection (EDD). MEDD outperforms all other trace-only detectors with 34/4500 = 0.76% bit error rate (BER) and closely approaches the best overall minimum mean-square error (MMSE) result of 32/4500 = 0.71%. After targeted parameter tuning, the detector benchmark produces zero errors over the evaluated payloads at 6 bit/s over 8 cm and at 3 bit/s over 24 cm. Overall, the results establish the platform as a reusable experimental setup for effective channel modeling, practical detector benchmarking, and future multi-molecule networking experiments.

cs.ET↗

arXiv2026

Strict-Priority Packet Delay in Switches with Transmit-Ring Buffering

Strict Priority (SP) scheduling is widely used at switch egress to provide low-latency service to high-priority (HP) traffic. Existing deterministic and stochastic latency models typically account for scheduler behavior and packet transmission, but omit a common switch implementation detail: the transmit ring (TXR) between the scheduler and the physical port. Because the switch must prepare the next packet before the current transmission completes, packets already placed in the TXR can further delay HP packets. This changes both the worst-case delay and the per-hop delay distribution of HP packets. This paper identifies this modeling gap, extends standard SP latency models to include the TXR, and validates the revised model through measurements on multiple switches. It also provides a measurement method for estimating the TXR size, a parameter that is often not reported in switch datasheets. The resulting model provides a closer representation of switch behavior for systems that use SP scheduling and require either delay bounds or delay distributions.

cs.NI↗

arXiv2026

DBMC-aNOMAly: Asynchronous NOMA with Pilot-Symbol Optimization Protocol for Diffusion-Based Molecular Communication Networks

Multiple access (MA) schemes can enable cooperation between multiple nodes in future diffusion-based molecular communication (DBMC) networks. Non-orthogonal MA for DBMC networks (DBMC-NOMA) is a promising option for efficient simultaneous MA using a single molecule type. This paper studies parameter optimization and bit error probability (BEP) reduction for asynchronous DBMC-NOMA. First, we analytically derive the associated BEP and compare DBMCNOMA with time-division and molecule-division MA. We show that asynchronous offsets can improve performance, and the upper-bound performance can be approached under almost all considered conditions by avoiding a small set of worst-case offset configurations, for which we propose and characterize a dedicated avoidance mechanism. We then propose DBMCaNOMAly, a pilot-symbol-based optimization protocol for asynchronous DBMC-NOMA, and evaluate it using Monte Carlo simulations. DBMC-aNOMAly provides robust BEP reduction across different network sizes and noise levels, under sampling jitter, and under changing runtime conditions, outperforming protocols from previous work. An end-to-end efficiency analysis further shows that these gains translate into increased net throughput after compensating for the pilot overhead. DBMCaNOMAly uses simple operations such as comparisons and additions that are compatible with chemical reaction networks, motivating future realistic modeling of the protocol.

cs.ET↗

arXiv2026

A Causal Diffusion Model for Video Reconstruction from Ultra-Low-Bitrate Representations

We study video reconstruction from ultra-low-bitrate representations, where the primary challenge shifts from encoding to decoding. In this regime, reconstruction with classical and neural codecs introduces blur, while generative and semantic approaches often struggle to jointly preserve fidelity, temporal consistency, and perceptual quality. To address these limitations, we propose a causal video diffusion model that reconstructs videos from ultra-low-bitrate semantics and highly compressed frames by jointly modeling their complementary information. We further introduce temporal-only distillation from a bidirectional teacher to enable parameter-efficient training and causal few-step inference. Through extensive quantitative, qualitative, and subjective evaluation, we show that the proposed method outperforms classical, neural, generative, and semantic baselines in ultra-low-bitrate video reconstruction.

cs.CV↗

arXiv2026

BIR-Adapter: A parameter-efficient diffusion adapter for blind image restoration

We introduce the BIR-Adapter, a parameter-efficient diffusion adapter for blind image restoration. Diffusion-based restoration methods have demonstrated promising performance in addressing this fundamental problem in computer vision, typically relying on auxiliary feature extractors or extensive fine-tuning of pre-trained models. Building on the observation that large-scale pretrained diffusion models can retain informative representations under image degradations, BIR-Adapter introduces a parameter-efficient, plug-and-play attention mechanism that substantially reduces the number of trained parameters. To further improve reliability, we adapt a sampling guidance mechanism that mitigates hallucinations during restoration. Experiments on synthetic and real-world degradations demonstrate that BIR-Adapter achieves competitive, and in several settings superior, performance compared to state-of-the-art methods while requiring up to 36x fewer trained parameters. Moreover, the adapter-based design enables integration into existing models. We validate this generality by extending a super-resolution-only diffusion model to handle additional unknown degradations, highlighting the adaptability of our approach for broader image restoration tasks.

cs.CV↗

arXiv2026

Improving Network Clock Synchronization by Marking Congestion

Achieving consistent time across devices in distributed systems often involves exchanging timestamped messages over a network. Precise time synchronization is crucial for applications such as cellular networks, industrial automation, and transactional databases. However, delay variation in synchronization packets-often caused by congestion from competing traffic-degrades synchronization accuracy. Detecting whether a packet experienced congestion can help improve synchronization through filtering and statistical methods. We propose an in-network congestion indication and filtering mechanism for synchronization messages used in protocols such as the Network Time Protocol (NTP) and Precision Time Protocol (PTP). Network devices mark packets that experienced queuing, allowing clocks to correct errors caused by varying delays. Our approach requires only simple changes at switches or routers, avoiding deep packet inspection or protocol modifications. The method is backward compatible, using standard but currently unused fields in IP, PTP, or NTP headers. We implement our method on a Tofino P4 target and demonstrate an improvement of over 80% in synchronization performance over a single hop. Moreover, we show that the performance of traditional statistical filters, such as min-RTT and median-delay, is improved by 90% over the one-hop hardware setup. We further demonstrate the effectiveness of our proposed method across multiple hops, both analytically and through simulation. Congestion marking improves the root-mean-squared clock offset estimation error by 30% to 80%, depending on network conditions and filtering techniques.

cs.NI↗

arXiv2026

5G Puppeteer: Chaining Hidden Command and Control Channels in 5G Core Networks

Mobile networks are essential for modern societies. The most recent generation of mobile networks will be even more ubiquitous than previous ones. Therefore, the security of these networks as part of the critical infrastructure with essential communication services is of the uttermost importance. However, these systems are still vulnerable to being compromised, as showcased in the recent discussion on supply chain security and other challenges. This work addresses problems arising from compromised 5G core network components. The investigations reveal how attacks based on command and control communication can be designed so that they cannot be detected or prevented. This way, various attacks against the security and privacy of subscribers can be performed for which no effective countermeasures are available.

cs.CR↗

arXiv2026

ChemSICal-Net: Timing-Controlled Chemical Reaction Network for Successive Interference Cancellation in Molecular Multiple Access

MC networks are envisioned to enable synthetic information exchange between nanoscale biological entities. For many algorithm proposals in the MC research field, the question of implementation at nanoscales and in biological environments remains open. Chemical reaction networks (CRNs) provide a natural framework to model computing processes in biological systems, while detailed simulations capture realistic stochastic effects. In this work, we present ChemSICal-Net, a comprehensive CRN simulation model of a chemical receiver implementing successive interference cancellation (SIC) to differentiate messages from multiple transmitters. We present the structure of the SIC algorithm in the form of basic chemical building blocks and incorporate clocked timing control by a chemical oscillator. We propose an adaptive Bayesian optimization (BO) scheme with a Gaussian process surrogate to find appropriate values for the reaction rate constants and the initial concentrations and show that it outperforms baseline methods from related work based on a fair computational cost metric. Then, the performance of the ChemSICal-Net framework is evaluated stochastically across a range of clock speeds and in different configurations focusing on communication system metrics such as detection accuracy and decision time. Our results highlight that the timing via a chemical clock can improve the detection accuracy by a factor of 2 in scenarios with shorter decision times, which underlines how the trade-off between decision time and detection probability can shape CRN design choices. The BO scheme is shown to reliably optimize parameters for different configurations by approximately one order of magnitude compared to the non-optimized case. Our system reveals the need for a multi-scale approach with external BO and stochastic simulation of molecular reaction dynamics for communication-metric-focused system design.

cs.ET↗

arXiv2026

Fast-MCS: A Scalable Open-Source Tool to Find Minimal Cut Sets

A network is represented as a graph consisting of nodes and edges. A cut set for a source-destination pair in a network is a set of elements that, when failed, cause the source-destination pair to lose connectivity. A Minimal Cut Set (MCS) is a cut set that cannot be further reduced while maintaining its status as a cut set. MCSs are crucial in identifying the critical elements in the network that have the most significant impact on failure. This work introduces Fast-MCS, an open-source, scalable tool for evaluating MCSs in large, complex networks. Additionally, we compare the computation time of Fast-MCS with the state-of-the-art.

cs.NI↗

arXiv2025

A Hands-On Molecular Communication Testbed for Undergraduate Education

This work presents a hands-on molecular communication (MC) testbed developed for the undergraduate Communication Engineering lab course at the Institute for Communications Technology (IfN), TU~Braunschweig. The goal of the experiment is to provide students with an intuitive and reproducible introduction to MC concepts using a low-cost and accessible fluidic setup. The system employs a background water flow into which three dye colors are injected and symbols are detected by a multi-wavelength photosensor. A zero-forcing--based estimator is used to separate the spectral components and reliably identify the transmitted colors. The experiment is designed to be completed independently by students within a single laboratory session and requires only basic prior knowledge from introductory communication engineering courses. A detailed script accompanies the experiment, guiding students through channel characterization, color detection, pseudoinverse computation, and simple data transmission using on-off keying. In pilot trials, students successfully reproduced the entire communication chain and achieved stable data rates of up to 0.5~bit/s over a 15~cm channel. The proposed testbed demonstrates that fundamental principles of MC can be taught effectively using a compact and inexpensive experimental setup. The experiment will be integrated into an undergraduate lab course.

physics.ed-ph↗

arXiv2025

Joint Resource Allocation to Transparently Integrate 5G TDD Uplink with Time-Aware TSN

To enable mobility in industrial communication systems, the seamless integration of 5G with Time-Sensitive Networking (TSN) is a promising approach. Deterministic communication across heterogeneous 5G-TSN systems requires joint scheduling between both domains. A key prerequisite for time-aware end-to-end scheduling is determining the forwarding delay for each TSN Traffic Class at every bridge, referred to as Bridge Delay (BD). Hence, to integrate 5G as a transparent TSN bridge, the 5G BD must be determined and guaranteed. Unlike wired bridges, the 5G BD relies on wireless resource management characteristics, such as the Time Division Duplex pattern and radio resource allocation procedure. In particular, traditional Uplink (UL) schedulers are optimized for throughput but often fail to meet the deadline requirements. To address this challenge, we propose a heterogeneous radio resource scheduler that integrates static and dynamic scheduling. The algorithm pre-allocates resources for time-sensitive periodic streams based on the reported BDs, ensuring alignment with the TSN mechanisms Time-Aware Shaper and Per-Stream Filtering and Policing. Meanwhile, remaining resources are dynamically allocated to non-deterministic flows using established strategies such as Proportional Fair, Max C/I, or a Quality of Service-aware priority-based scheduler. The scheduler's performance is evaluated through OMNeT++ simulations. The results demonstrate support for diverse TSN flows while ensuring deadline-aware scheduling of time-sensitive UL traffic in mobility scenarios. Periodic time-sensitive flows are end-to-end scheduled across domains, improving the resource efficiency by 28% compared to the Configured Grant baseline. While reliability is preserved, non-deterministic rate-sensitive flows benefit from the improved resource utilization, resulting in higher throughput

cs.NI↗

arXiv2025

On the Optimization of Model Aggregation for Federated Learning at the Network Edge

The rapid increase in connected devices has signifi- cantly intensified the computational and communication demands on modern telecommunication networks. To address these chal- lenges, integrating advanced Machine Learning (ML) techniques like Federated Learning (FL) with emerging paradigms such as Multi-access Edge Computing (MEC) and Software-Defined Wide Area Networks (SD-WANs) is crucial. This paper intro- duces online resource management strategies specifically designed for FL model aggregation, utilizing intermediate aggregation at edge nodes. Our analysis highlights the benefits of incorporating edge aggregators to reduce network link congestion and maximize the potential of edge computing nodes. However, the risk of network congestion persists. To mitigate this, we propose a novel aggregation approach that deploys an aggregator overlay network. We present an Integer Linear Programming (ILP) model and a heuristic algorithm to optimize the routing within this overlay network. Our solution demonstrates improved adapt- ability to network resource utilization, significantly reducing FL training round failure rates by up to 15% while also alleviating cloud link congestion.

cs.NI↗

arXiv2025

How to build a sovereign network? -- A proposal to measure network sovereignty

Network sovereignty is a network operator's ability to reduce the dependency on component manufacturers to minimize the impact of manufacturer failures. Network operators now face new design challenges to increase network sovereignty and avoid vendor lock-in problems because a high dependency on a manufacturer corresponds to low survivability if that manufacturer is unavailable. The main contribution of this work is the proposal of a novel metric to measure network sovereignty, the Cut Set Coloring (CSC) score. Based on the CSC core metric CSC-ILP, our Integer Linear Program formulation is presented to maximize network sovereignty. We compare CSC-ILP's performance with state of the art manufacturer assignment strategies.

cs.NI↗

arXiv2025

Exhaled Breath Analysis Through the Lens of Molecular Communication: A Survey

Molecular Communication (MC) has long been envisioned to enable an Internet of Bio-Nano Things (IoBNT) with medical applications, where nanomachines within the human body conduct monitoring, diagnosis, and therapy at micro- and nanoscale levels. MC involves information transfer via molecules and is supported by well-established theoretical models. However, practically achieving reliable, energy-efficient, and bio-compatible communication at these scales still remains a challenge. Air-Based Molecular Communication (ABMC) is a type of MC that operates over larger, meter-scale distances and extends even outside the human body. Therefore, devices and techniques to realize ABMC are readily accessible, and associated use cases can be very promising in the near future. Exhaled breath analysis has previously been proposed. It provides a non-invasive approach for health monitoring, leveraging existing commercial sensor technologies and reducing deployment barriers. The breath contains a diverse range of molecules and particles that serve as biomarkers linked to various physiological and pathological conditions. The plethora of proven methods, models, and optimization approaches in MC enable macroscale breath analysis, treating human as the transmitter, the breath as the information carrier, and macroscale sensors as the receiver. Using ABMC to interface with the inherent dynamic networks of cells, tissues, and organs could create a novel Internet of Bio Things (IoBT), a preliminary macroscale stage of the IoBNT. This survey extensively reviews exhaled breath modeling and analysis through the lens of MC, offering insights into theoretical frameworks and practical implementations from ABMC, bringing the IoBT a step closer to real-world use.

cs.ET↗

arXiv2025

Evaluation of a Multi-Molecule Molecular Communication Testbed Based on Spectral Sensing

This work presents a novel flow-based molecular communication (MC) testbed using spectral sensing and ink intensity estimation to enable real-time multi-molecule (MUMO) transmission. MUMO communication opens up crucial opportunities for increased throughput as well as implementing more complex coding, modulation, and resource allocation strategies for MC testbeds. An estimator using non-invasive spectral sensing at the receiver is proposed based on a simple absorption model. We conduct in-depth channel impulse response (CIR) measurements and a preliminary communication performance evaluation. Additionally, a simple analytical model is used to check the consistency of the CIRs. The results indicate that by utilizing MUMO transmission, on-off-keying, and a simple difference detector, the testbed can achieve up to 3 bits per second for near-error-free communication, which is on par with comparable testbeds that utilize more sophisticated coding or detection methods. Our platform lays the ground for implementing MUMO communication and evaluating various physical layer and networking techniques based on multiple molecule types in future MC testbeds in real time.

cs.ET↗

arXiv2025

Goal-Oriented Middleware Filtering at Transport Layer Based on Value of Updates

This work explores employing the concept of goal-oriented (GO) semantic communication for real-time monitoring and control. Generally, GO communication advocates for the deep integration of application targets into the network design. We consider CPS and IoT applications where sensors generate a tremendous amount of network traffic toward monitors or controllers. Here, the practical introduction of GO communication must address several challenges. These include stringent timing requirements, challenging network setups, and limited computing and communication capabilities of the devices involved. Moreover, real-life CPS deployments often rely on heterogeneous communication standards prompted by specific hardware. To address these issues, we introduce a middleware design of a GO distributed Transport Layer (TL) framework for control applications. It offers end-to-end performance improvements for diverse setups and transmitting hardware. The proposed TL protocol evaluates the Value of sampled state Updates (VoU) for the application goal. It decides whether to admit or discard the corresponding packets, thus offloading the network. VoU captures the contribution of utilizing the updates at the receiver into the application's performance. We introduce a belief network and the augmentation procedure used by the sensor to predict the evolution of the control process, including possible delays and losses of status updates in the network. The prediction is made either using a control model dynamics or a Long-Short Term Memory neural network approach. We test the performance of the proposed TL in the experimental framework using Industrial IoT Zolertia ReMote sensors. We show that while existing approaches fail to deliver sufficient control performance, our VoU-based TL scheme ensures stability and performs $\sim$$60\%$ better than the naive GO TL we proposed in our previous work.

cs.NI↗

arXiv2025

TwinRAN: Twinning the 5G RAN in Azure Cloud

The proliferation of 5G technology necessitates advanced network management strategies to ensure optimal performance and reliability. Digital Twin (DT)s have emerged as a promising paradigm for modeling and simulating complex systems like the 5G Radio Access Network (RAN). In this paper, we present TwinRAN, a DT of the 5G RAN built leveraging the Azure DT platform. TwinRAN is built on top of the Open RAN (O-RAN) architecture and is agnostic to the vendor of the underlying equipment. We demonstrate three applications using TwinRAN and evaluate the required resources and their performance for a network with 800 users and eight gNBs. We first evaluate the performance and limitations of the Azure DT platform, measuring the latency under different conditions. The results from this evaluation allow us to optimize TwinRAN for the DT platform it uses. Then, we present the system's architectural design, emphasizing its components and interactions. We propose that two types of twin graphs be simultaneously maintained on the cloud: one for intercell operations, keeping a broad overview of all the cells in the network, and another where each cell is spawned in a separate Azure DT instance for more granular operation and monitoring of intracell tasks. We evaluate the performance and operating costs of TwinRAN for each of the three applications. The TwinRAN DT in the cloud can keep track of its physical twin within a few hundred milliseconds, extending its utility to many 5G network management tasks, some of which are shown in this paper. The novel framework for building and maintaining a DT of the 5G RAN presented in this paper offers network operators enhanced capabilities, empowering efficient deployments and management.

cs.NI↗

arXiv2024

Advanced Plaque Modeling for Atherosclerosis Detection Using Molecular Communication

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods.

cs.ET↗