Wireless communication links used in control and automation systems must deliver timely and reliable information despite fading and packet losses. Hybrid Automatic Repeat reQuest (HARQ) protocols -combining forward error correction and retransmissions- can significantly improve reliability. However, retransmitting outdated packets can delay fresh information, leading to performance degradation in closed-loop systems.

This thesis aims to analyze and optimize HARQ scheduling for networked control from a communication-theoretic perspective. The project will study how retransmission strategies, feedback mechanisms, and channel dynamics jointly influence information freshness and system performance. The problem will be formulated within a stochastic decision framework, such as a Markov decision process (MDP), to characterize the trade-off between reliability, latency, and communication cost.

Possible research directions include:

Designing scheduling strategies that decide between new transmissions and retransmissions; 



The thesis combines ideas from communication theory, stochastic modeling, and decision-making under uncertainty, and provides an opportunity to contribute to ongoing research in low-latency and reliable communication for cyber-physical systems.

Voraussetzungen

Modern networked systems increasingly rely on intelligent information exchange between sensing devices and decision-making agents. Rather than sending data continuously or periodically, future communication networks aim to transmit only what matters: information that is useful, timely, and effective for achieving a specific goal.

Such goal-oriented communication is a key enabler of efficient cyber-physical systems, ranging from remote health monitoring and autonomous vehicles to industrial automation. Designing these systems requires new models that balance data freshness, communication cost, and decision accuracy.

This thesis will explore and model decision-making mechanisms for intelligent update systems where both sender and receiver actively decide when and what information to exchange. The goal is to investigate how coordinated or independent policies can improve overall system efficiency and effectiveness.

Possible directions include:

• Modeling joint decision processes between sensing and actuation agents.
• Analyzing when an agent should send (push) or request (pull) updates.
• Developing and simulating policies that account for usefulness, timeliness, and cost of communication.
• Comparing rule-based (model-based) and learning-based (reinforcement) approaches

Voraussetzungen

How can wireless communication systems remain undetectable when monitored by mobile aerial wardens (e.g., drones)? The cryptography secures the content of messages but does not hide their existence. In this seminar, we will explore low probability of detection (LPD) communication, focusing on covert communication under multi-UAV surveillance.

The provided papers introduce a novel method that combines Graph Neural Networks (GNNs) with Koopman operator theory to predict UAV trajectories and enable real-time covert communication in wireless ad-hoc networks. In this method, GNNs are used to model the spatial interactions between multiple UAVs using a mechanism known as message passing, where each UAV node updates its internal state based on information received from its neighbors in the network graph. This helps capture complex group dynamics more accurately.

Students will learn how such predictions allow ground nodes to adapt their transmission behavior to remain below detection thresholds. They will also see how data-driven techniques complement traditional communication-theoretic approaches to security.

Voraussetzungen

This project investigates control strategies for remotely stabilizing a stochastic system under communication constraints. The goal is to minimize the average time the system spends in undesirable states—interpreted as the age of false alarm. We compare two approaches: an optimal policy derived from dynamic programming and a heuristic based on Lyapunov drift minimization. Simulations show that while both achieve similar average performance, the Lyapunov-based policy can better suppress long instability bursts, offering a simple yet effective alternative to exact methods.

This project will focus on frame synchronization for IoT applications using non-terrestrial communication. For this scenario, a random access channel with frequency uncertainty due to the high Doppler shift in Low Earth Orbit will be investigated. In particular, different preamble designs will be evaluated and compared regarding channel impairments such as Doppler shift, Doppler rate, phase offset, etc. The following tasks shall be investigated: - Comparison between a good sequence, a random sequence and the sequence for our waveform. - Impact of channel impairments (doppler, doppler rate, phase offset) - Impact of using preamble and postamble - Impact of using preamble, postamble and pilots in between. The channel as well as the detector will be simulated in Matlab. A detection algorithm suited for large frequency uncertainties will be implemented and used for evaluating different preamble sequences. First steps: - Build Point-to-point transmission in Matlab - Integrate a correlator - Plot Receiver Operating Characteristic for preamble detection Afterwards, increase the complexity of the model (Noise, multiple users, interference, …) and evaluate different preamble designs (pre, post, distributed, pilots, sequence, length, …)

This thesis investigates how to optimally monitor and estimate real-time information from remote systems when communication resources are limited or delayed. In modern networked systems—such as sensor networks, autonomous agents, or industrial control applications—measurements often traverse unreliable or congested channels before reaching a central estimator. This raises a fundamental question: When should data be sampled and transmitted to ensure accurate and timely understanding of the system state?

The thesis will explore strategies that balance the freshness of information (often quantified by metrics like the Age of Information) with the quality of estimation. It will involve studying optimal sampling policies, understanding the impact of transmission delays, and characterizing how these factors affect overall system performance. The student will engage with both theoretical models and simulation tools to gain insights relevant to real-world applications in remote monitoring, edge computing, and cyber-physical systems.

Voraussetzungen

In this seminar, we will examine communication-efficient policies tailored to the unique demands of IoT applications. Rather than focusing solely on maximizing throughput, we will explore how messages contribute to overarching objectives—such as monitoring system status or controlling physical processes—and balance these factors to prevent undue network congestion. Over the course of the seminar, students will gain a deeper understanding of relevant modeling approaches, design considerations, and performance trade-offs. Depending on the student’s interests and background, the seminar can be oriented toward methods grounded in reinforcement learning or dynamic programming to devise and analyze these communication-efficient strategies.

Inspired by the framework of real-time tracking under imperfect forward and feedback channels (as discussed in [arXiv:2407.06749v2]), we will examine how to minimize a time-averaged distortion (or estimation error) while respecting energy constraints. The overarching theme is deciding “when” and “how” to send status updates so that network congestion is avoided and physical processes are accurately tracked, despite error-prone acknowledgments and partial knowledge at the transmitter.

o1  

Voraussetzungen