arXiv subjects

Markus Landmann

Publications and source records attributed to Markus Landmann.

5 recordsLinked to original sources

arXiv2026

A Road-Mobile GNSS-Disciplined Oscillator for Accurate Synchronization of Vehicular Microwave Measurements

Precise synchronization is essential in various technical disciplines, being especially challenging in mobile scenarios. Unfortunately, state-of-the-art global navigation satellite system (GNSS) disciplined oscillators (GNSSDOs) are designed and optimized for stationary operation. We present a novel solution that is optimized for mobile use from the ground up. The centerpiece is a precise oven-controlled crystal oscillator (OCXO) that is optimized for low sensitivity to dynamic accelerations. A state-of-the-art GNSS timing module is used to discipline it. We evaluate the system by comparing it with state-of-the-art test equipment in a real-world test drive through diverse environments. After compensating for the stationary offset, the state-of-the-art devices deviated by up to 2315 ns, while with our devices, the deviation never exceeded 22.6 ns. It is evident that the devices designed for laboratory use perform inadequately in mobile operation and that our novel solution enables a significant leap in accuracy.

eess.SY↗

arXiv2026

Temporal Graph Neural Network for ISAC Target Detection and Tracking

Integrated sensing and communication (ISAC) is a key enabler of 6G, supporting environment-aware services. A fundamental sensing task in this setting is reliable multi-target detection and tracking. This paper proposes a temporal graph neural network (TGNN)-based tracking method that exploits delay and Doppler information from the wireless channel. The delay-Doppler map is modeled as a sequence of graphs, and tracking is formulated as a temporal node classification problem, enabling joint clustering and data association of dynamic targets. Using ray-tracing-based channel outputs as ground truth, the method is evaluated across multiple scenes with varying target positions, velocities, and trajectories and is compared with a Kalman filter baseline. Results demonstrate reduced normalized mean squared error (NMSE) in delay and Doppler, leading to more accurate multi-target tracking.

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arXiv2024

Accelerating Innovation in 6G Research: Real-Time Capable SDR System Architecture for Rapid Prototyping

The upcoming 3GPP global mobile communication standard 6G strives to push the technological limits of radio frequency (RF) communication even further than its predecessors: Sum data rates beyond 100 Gbit/s, RF bandwidths above 1 GHz per link, and sub-millisecond latency necessitate very high performance development tools. We propose a new SDR firmware and software architecture designed explicitly to meet these challenging requirements. It relies on Ethernet and commercial off-the-shelf network and server components to maximize flexibility and to reduce costs. We analyze state-of-the-art solutions (USRP X440 and other RFSoC-based systems), derive architectural design goals, explain resulting design decision in detail, and exemplify our architecture's implementation on the XCZU48DR RFSoC. Finally, we validate its performance via measurements and outline how the architecture surpasses the state-of-the-art with respect to sustained RF recording, while maintaining high Ethernet bandwidth efficiency. Building a 6G integrated sensing and communication (ISAC) example, we demonstrate its real-time and rapid application development capabilities.

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arXiv2022

Estimating Multi-Modal Dense Multipath Components using Auto-Encoders

We present a maximum-likelihood estimation algorithm for radio channel measurements exhibiting a mixture of independent Dense Multipath Components. The novelty of our approach is in the algorithms initialization using a deep learning architecture. Currently, available approaches can only deal with scenarios where a single mode is present. However, in measurements, two or more modes are often observed. This much more challenging multi-modal setting bears two important questions: How many modes are there, and how can we estimate those? To this end, we propose a Neural Net-architecture that can reliably estimate the number of modes present in the data and also provide an initial assessment of their shape. These predictions are used to initialize for gradient- and model-based optimization algorithm to further refine the estimates. We demonstrate numerically how the presented architecture performs on measurement data and analytically study its influence on the estimation of specular paths in a setting where the single-modal approach fails.

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arXiv2016

Design and Analysis of Compressive Antenna Arrays for Direction of Arrival Estimation

In this paper we investigate the design of compressive antenna arrays for direction of arrival (DOA) estimation that aim to provide a larger aperture with a reduced hardware complexity by a linear combination of the antenna outputs to a lower number of receiver channels. We present a basic receiver architecture of such a compressive array and introduce a generic system model that includes different options for the hardware implementation. We then discuss the design of the analog combining network that performs the receiver channel reduction, and propose two design approaches. The first approach is based on the spatial correlation function which is a low-complexity scheme that in certain cases admits a closed-form solution. The second approach is based on minimizing the Cramer-Rao Bound (CRB) with the constraint to limit the probability of false detection of paths to a pre-specified level. Our numerical simulations demonstrate the superiority of the proposed optimized compressive arrays compared to the sparse arrays of the same complexity and to compressive arrays with randomly chosen combining kernels.

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