Wolfgang Kellerer

Alexander Wietfeld, Wolfgang Kellerer

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.

Onur Ayan, Mikhail Vilgelm, Markus Klügel et al.

Age-of-Information (AoI) is a recently introduced metric for network operation with sensor applications which quantifies the freshness of data. In the context of networked control systems (NCSs), we compare the worth of the AoI metric with the value-of-information (VoI) metric, which is related to the uncertainty reduction in stochastic processes. First, we show that the uncertainty propagates non-linearly over time depending on system dynamics. Next, we define the value of a new update of the process of interest as a function of AoI and system parameters of the NCSs. We use the aggregated update value as a utility for the centralized scheduling problem in a cellular NCS composed of multiple heterogeneous control loops. By conducting a simulative analysis, we show that prioritizing transmissions with higher VoI improves performance of the NCSs compared with providing fair data freshness to all sub-systems equally.

Zenit Music, Fabio Molinari, Sebastian Gallenmüller et al.

The topic of this paper is to use an intuitive model-based approach to design a networked controller for a recent benchmark scenario. The benchmark problem is to remotely control a two-wheeled inverted pendulum robot via W-LAN communication. The robot has to keep a vertical upright position. Incorporating wireless communication in the control loop introduces multiple uncertainties and affects system performance and stability. The proposed networked control scheme employs model predictive techniques and deliberately extends delays in order to make them constant and deterministic. The performance of the resulting networked control system is evaluated experimentally with a predefined benchmarking experiment and is compared to local control involving no delays.

Cem Eteke, Batuhan Tosun, Martin Piccolrovazzi et al.

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.

Yash Deshpande, Quirin Vogel, Laura Becker et al.

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.

Hasan Yagiz Ozkan, Poul Einer Heegaard, Wolfgang Kellerer et al.

Bugs are inevitable in software development, and their reporting in open repositories can enhance software transparency and reliability assessment. This study aims to extract information from the issue tracking system Jira and proposes a methodology to estimate resolution time for new bugs. The methodology is applied to network project ONAP, addressing concerns of network operators and manufacturers. This research provides insights into bug resolution times and related aspects in network softwarization projects.

Alexander Wietfeld, Oguz Turgut, Eneritz Somoza Rodríguez et al.

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.

Christian Sieber, Korbinian Hagn, Christian Moldovan et al.

Mobile video consumption is increasing and sophisticated video quality adaptation strategies are required to deal with mobile throughput fluctuations. These adaptation strategies have to keep the switching frequency low, the average quality high and prevent stalling occurrences to ensure customer satisfaction. This paper proposes a novel methodology for the design of machine learning-based adaptation logics named HASBRAIN. Furthermore, the performance of a trained neural network against two algorithms from the literature is evaluated. We first use a modified existing optimization formulation to calculate optimal adaptation paths with a minimum number of quality switches for a wide range of videos and for challenging mobile throughput patterns. Afterwards we use the resulting optimal adaptation paths to train and compare different machine learning models. The evaluation shows that an artificial neural network-based model can reach a high average quality with a low number of switches in the mobile scenario. The proposed methodology is general enough to be extended for further designs of machine learning-based algorithms and the provided model can be deployed in on-demand streaming scenarios or be further refined using reward-based mechanisms such as reinforcement learning. All tools, models and datasets created during the work are provided as open-source software.

Julian Sturm, Daniel Fraunholz, Oliver Zeidler et al.

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.

Cem Eteke, Alexander Griessel, Wolfgang Kellerer et al.

This paper introduces BIR-Adapter, a low-complexity blind image restoration adapter for diffusion models. The BIR-Adapter enables the utilization of the prior of pre-trained large-scale diffusion models on blind image restoration without training any auxiliary feature extractor. We take advantage of the robustness of pretrained models. We extract features from degraded images via the model itself and extend the self-attention mechanism with these degraded features. We introduce a sampling guidance mechanism to reduce hallucinations. We perform experiments on synthetic and real-world degradations and demonstrate that BIR-Adapter achieves competitive or better performance compared to state-of-the-art methods while having significantly lower complexity. Additionally, its adapter-based design enables integration into other diffusion models, enabling broader applications in image restoration tasks. We showcase this by extending a super-resolution-only model to perform better under additional unknown degradations.

Alperen Gündogan, H. Murat Gürsu, Volker Pauli et al.

We consider the distributed resource selection problem in Vehicle-to-vehicle (V2V) communication in the absence of a base station. Each vehicle autonomously selects transmission resources from a pool of shared resources to disseminate Cooperative Awareness Messages (CAMs). This is a consensus problem where each vehicle has to select a unique resource. The problem becomes more challenging when---due to mobility---the number of vehicles in vicinity of each other is changing dynamically. In a congested scenario, allocation of unique resources for each vehicle becomes infeasible and a congested resource allocation strategy has to be developed. The standardized approach in 5G, namely semi-persistent scheduling (SPS) suffers from effects caused by spatial distribution of the vehicles. In our approach, we turn this into an advantage. We propose a novel DIstributed Resource Allocation mechanism using multi-agent reinforcement Learning (DIRAL) which builds on a unique state representation. One challenging issue is to cope with the non-stationarity introduced by concurrently learning agents which causes convergence problems in multi-agent learning systems. We aimed to tackle non-stationarity with unique state representation. Specifically, we deploy view-based positional distribution as a state representation to tackle non-stationarity and perform complex joint behavior in a distributed fashion. Our results showed that DIRAL improves PRR by 20% compared to SPS in challenging congested scenarios.

Serkut Ayvaşık, H. Murat Gürsu, Wolfgang Kellerer

The upcoming industrial revolution requires deployment of critical wireless sensor networks for automation and monitoring purposes. However, the reliability of the wireless communication is rendered unpredictable by mobile elements in the communication environment such as humans or mobile robots which lead to dynamically changing radio environments. Changes in the wireless channel can be monitored with frequent pilot transmission. However, that would stress the battery life of sensors. In this work a new wireless channel estimation technique, Veni Vidi Dixi, VVD, is proposed. VVD leverages the redundant information in depth images obtained from the surveillance cameras in the communication environment and utilizes Convolutional Neural Networks CNNs to map the depth images of the communication environment to complex wireless channel estimations. VVD increases the wireless communication reliability without the need for frequent pilot transmission and with no additional complexity on the receiver. The proposed method is tested by conducting measurements in an indoor environment with a single mobile human. Up to authors best knowledge our work is the first to obtain complex wireless channel estimation from only depth images without any pilot transmission. The collected wireless trace, depth images and codes are publicly available.

Christian Sieber, Susanna Schwarzmann, Andreas Blenk et al.

Scalable user- and application-aware resource allocation for heterogeneous applications sharing an enterprise network is still an unresolved problem. The main challenges are: (i) How to define user- and application-aware shares of resources? (ii) How to determine an allocation of shares of network resources to applications? (iii) How to allocate the shares per application in heterogeneous networks at scale? In this paper we propose solutions to the three challenges and introduce a system design for enterprise deployment. Defining the necessary resource shares per application is hard, as the intended use case and user's preferences influence the resource demand. Utility functions based on user experience enable a mapping of network resources in terms of throughput and latency budget to a common user-level utility scale. A multi-objective MILP is formulated to solve the throughput- and delay-aware embedding of each utility function for a max-min fairness criteria. The allocation of resources in traditional networks with policing and scheduling cannot distinguish large numbers of classes. We propose a resource allocation system design for enterprise networks based on Software-Defined Networking principles to achieve delay-constrained routing in the network and application pacing at the end-hosts. The system design is evaluated against best effort networks with applications competing for the throughput of a constrained link. The competing applications belong to the five application classes web browsing, file download, remote terminal work, video streaming, and Voice-over-IP. The results show that the proposed methodology improves the minimum and total utility, minimizes packet loss and queuing delay at bottlenecks, establishes fairness in terms of utility between applications, and achieves predictable application performance at high link utilization.

Hiroyuki Kasai, Wolfgang Kellerer, Martin Kleinsteuber

This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows. Since traffic data is large-scale time-structured data accompanied with noise and outliers under partial observations, an efficient modeling method is essential. To this end, this paper proposes an online subspace tracking of a Hankelized time-structured traffic tensor for normal flows based on the Candecomp/PARAFAC decomposition exploiting the recursive least squares (RLS) algorithm. We estimate abnormal flows as outlier sparse flows via sparsity maximization in the underlying under-constrained linear-inverse problem. A major advantage is that our algorithm estimates normal flows by low-dimensional matrices with time-directional features as well as the spatial correlation of multiple links without using the past observed measurements and the past model parameters. Extensive numerical evaluations show that the proposed algorithm achieves faster convergence per iteration of model approximation, and better volume anomaly detection performance compared to state-of-the-art algorithms.