This work presents an approach to realize a joint communication and sensing (JCAS) system using orthogonal frequency division multiplexing (OFDM). Signal processing approaches are discussed and the waveform is optimized towards its performance for a JCAS system. The proposed methods are equalization and channel estimation, sub-carrier thinning for the reduction of inter-carrier interference due to the phase noise and scaling with peak clipping for a low peak-to-average-power ratio (PAPR). The effect of these parameters is discussed and evaluated in a measurement setup. A JCAS system concept with a high bit rate of 862 Mbit/s and a high dynamic range of 57.8 dB is demonstrated.

This paper presents a deep learning-based Feature Pyramid Network (FPN) for object detection on range-Doppler maps in a stationary short-range radar setup operating at 60 GHz with 4 GHz bandwidth. The FPN model enables multiscale feature extraction, detecting large and small objects in complex, cluttered environments. It offers an alternative to traditional CA- and OS-CFAR detectors combined with DBSCAN, which can struggle with such variations. Holistic image processing through convolutional kernels in a one-stage architecture can help overcome limitations from clutter and noise points by enabling detection and clustering within a single process. Achieving an F1 score of 0.8683 at a 95% confidence threshold, the FPN balances precision (0.9050) and recall (0.8345), demonstrating competitiveness with OS-CFAR (F1: 0.8534) and CA-CFAR (F1: 0.8776). By capturing hierarchical features, the FPN learns contextual patterns beyond fixed-threshold methods, ensuring reliable detection of diverse target dimensions. Its real-time, edge-based data processing capabilities open opportunities for applications in smart homes, industrial automation, and infrastructure management.

Recounts the career and contributions of Andreas Stelzer.

This paper presents an overview of key challenges that arise during the development of broadband signal lines on organic interposer substrates for heterogeneous chiplet systems. This work identifies and analyzes four critical aspects that significantly impact signal integrity at high frequencies. Conductor surface roughness, surface plating effects, manufacturing process variations and the complexity of accurate crosstalk measurement. To validate the discussed effects, simulations and benchmark measurements were performed using an advanced 110 GHz 8-port VNA setup. This enable the designer to tackle these challenges starting right at the beginning of the design flow by choosing correct modeling approaches.

This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continuous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs.

This work presents a cross correlation-based true-speed-over-ground estimation approach based on experiences from an earlier system. The radar system parameters as well as the antenna configuration were adapted to fit the requirements of the presented application. Also, challenges regarding the previously limited velocity range are addressed and an appropriate solution is proposed. The entire system was thoroughly tested in a train scenario over a wide range of velocities. Very promising measurement results are presented and compared to related approaches.

Wireless surface acoustic wave (SAW) sensors have some unique features that make them promising for industrial metrology. Their decisive advantage lies in their purely passive operation and the wireless readout capability allowing the installation also at particularly inaccessible locations. Furthermore, they are small, low-cost and rugged components on highly stable substrate materials and thus particularly suited for harsh environments. Nevertheless, a sensor itself does not carry out any measurement but always requires a suitable excitation and interrogation circuit: a reader. A variety of different architectures have been presented and investigated up to now. This review paper gives a comprehensive survey of the present state of reader architectures such as time domain sampling (TDS), frequency domain sampling (FDS) and hybrid concepts for both SAW resonators and reflective SAW delay line sensors. Furthermore, critical performance parameters such as measurement accuracy, dynamic range, update rate, and hardware costs of the state of the art in science and industry are presented, compared and discussed.