Timeliness and Accuracy in Remote Estimation over Communication Networks
Beschreibung
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
the student should ideally have the following background to work effectively on this thesis:
>>Understanding of random processes, in particular the Wiener (Brownian motion) process, and basic stochastic calculus concepts.
>>Basic knowledge of optimization techniques, particularly dynamic programming and threshold-based policies.
Frame Synchronization in Random Access Channels with Frequency Uncertainty
Beschreibung
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, …)
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
The student sould be familiar with the following topics: 1-Probability Theory and Stochastic Processes 2-Optimization & Dynamic Programming 3-(Optional) Reinforcement Learning
Asgari, H; Munari, A; Liva, G: Remote Monitoring of Two-State Markov Sources in Random Access Channels: Joint Model and State Estimation. 14th International ITG Conference on Systems, Communications and Coding (SCC), 2025 mehr…
Volltext (mediaTUM)
Asgari, Houman; Babazadeh, Maryam: Distributed model-free optimisation in community-based energy market. International Journal of Systems Science, 2025, 1-16 mehr…
Volltext (
DOI
)
2024
Asgari, Houman; Munari, Andrea; Liva, Gianluigi: Age of Information for Frame Asynchronous Coded Slotted ALOHA. ICC 2024 - IEEE International Conference on Communications, IEEE, 2024, 2871-2876 mehr…
Volltext (
DOI
)
2022
Houman Asgari; Gianluigi Liva; Andrea Munari;: Bounds on the Error Probability over Finite-State Erasure Channels. Workshop on Next Generation Networks and Applications , 2022 mehr…