A Bayesian Framework for Integrated Deep Metric Learning and Tracking of Vulnerable Road Users Using Automotive Radars - Dubey, Anand; Santra, Avik; Fuchs, Jonas; Lübke, Maximilian; Weigel, Robert; Lurz, Fabian (IEEE Access, 2021)
With the recent advancements in radar systems, radar sensors offer a promising and effective perception of the surrounding. This includes target detection, classification and tracking. Compared to the state-of-the-art, where the state vector of classical tracker considers only localization parameters, this paper proposes an integrated Bayesian framework by augmenting state vector with feature embedding as appearance parameter together with localization parameter. In context of automotive vulnerable road users (VRUs) such as pedestrian and cyclist, the classical tracker poses multiple challenges to preserve the identity of the tracked target during partial or complete occlusion, due to low inter-class (pedestrian-cyclist) variations and strong similarity between intra-class (pedestrian-pedestrian). Subsequently, feature embedding corresponding to target’s micro-Doppler signature are learned using novel Bayesian based deep metric learning approaches. The tracker’s performance is optimized due to a better separability of the targets. At the same time, the classififiers’ performance is enhanced due to Bayesian formulation utilizing the temporal smoothing of the classififier’s embedding vector. In this work, we demonstrate the performance of the proposed Bayesian framework using several vulnerable user targets based on a 77 GHz automotive radar.
Keywords: Automotive Radar; Bayesian Framework; Deep Metric learning; Integrated Classification-Tracking; Unscented Kalman Filter

Accelerating Deep Learning in Radar Systems: A Simulation Framework for 60 GHz Indoor Radar - Reitz, Philipp; Maiwald, Timo; Bönsch, Jonas; Franchi, Norman; Lübke, Maximilian (Remote Sensing, 2024)
FMCW radar systems are increasingly used in diverse applications, and emerging technologies like JCAS offer new opportunities. However, machine learning for radar faces challenges due to limited application-specific datasets, often requiring advanced simulations to supplement real-world data. This paper presents a setup for generating synthetic radar data for indoor environments, evaluated using CNNs. The setup involves comprehensive modeling, including far-field antenna simulations, variations in human radar cross-section, and detailed representations of indoor environments with their corresponding propagation channel properties. These synthetic data are used to train CNNs, and their performance is assessed on real measurement data. The results demonstrate that CNNs trained on synthetic data can perform well when tested on real measurement data. Specifically, the models trained with synthetic data showed performance comparable to models trained with real measurement data, which required a minimum of 300 samples to reach similar levels of accuracy. This result demonstrates that synthetic data can effectively train neural networks, providing an alternative to real measurement data, particularly when collecting sufficient real-world samples is difficult or costly. This approach significantly reduces the time required for generating datasets, and the ability to quickly label data in simulations simplifies and accelerates post-processing. Additionally, the generated datasets can be made more heterogeneous by introducing varying signal conditions, enhancing the diversity and robustness of the training dat
Keywords: convolutional neural network; FMCW radar; mm-wave; radar cross section; simulation and measurement

A code-orthogonal PMCW transmission scheme for improving communications performance in JCAS systems - Su, Yanpeng; Shatov, Victor; Franchi, Norman; Lübke, Maximilian (IEEE Access, 2024)

A Feature Pyramid Network Based Detection Approach for Multi-Scale Target Detection in Stationary Short-Range Radar - Reitz, Philipp; Veihelmann, Tobias Christian; Künzle, Christian; Franchi, Norman; Weigel, Robert; Lübke, Maximilian (IEEE Wireless and Microwave Technology Conference (WAMICON), 2025)

A Geometry-Based ISAC Channel Model for Vehicle-To-Vehicle Scenarios - Zhang, Guojin; Cai, Xuesong; Franchi, Norman; Lübke, Maximilian (2025 IEEE European Conference on Antennas and Propagation (EuCAP), 2025)
Integrated sensing and communication (ISAC) technique has recently gained significant attention in both academic and industrial sectors, as it can provide both sensing and communication functions using the same transmission and frequency spectrum. In this paper, a geometry-based statistical ISAC channel model for vehicle-to-vehicle (V2V) scenario is established, based on the ray-tracing simulation on the highway scenario at 77 GHz. An improved multipath-component-distance (MCD) threshold-based clustering algorithm is proposed to jointly group the paths for sensing and communication channels as clusters. These clusters are matched to the physical interaction objects in the scenarios. Based on the clustering results, cluster level parameters (i.e., intra-cluster delay, azimuth and Doppler spread) were explored. Furthermore, the clusters contributed from the same physical interaction object in both communication and sensing channels are regarded as ``sharing sensing clusters''. To investigate the correlation and differences between communication and sensing channel, shared degree (SD) metric is analyzed.<br

A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation - Fuchs, Jonas; Gardill, Markus; Lübke, Maximilian; Dubey, Anand; Lurz, Fabian (IEEE Access, 2022)
Millimeter-wave sensing using automotive radar imposes high requirements on the applied signal processing in order to obtain the necessary resolution for current imaging radar. High-resolution direction of arrival estimation is needed to achieve the desired spatial resolution, limited by the total antenna array aperture. This work gives an overview of the recent progress and work in the field of deep learning based direction of arrival estimation in the automotive radar context, i.e. using only a single measurement snapshot. Additionally, several deep learning models are compared and investigated with respect to their suitability for automotive angle estimation. The models are trained with model- and data-based approaches for data generation, including simulated scenarios as well as real measurement data from more than 400 automotive radar sensors. Finally, their performance is compared to several baseline angle estimation algorithms like the maximum-likelihood estimator. All results are discussed with respect to the estimation error, the resolution of closely spaced targets and the total estimation accuracy. The overall results demonstrate the viability and advantages of the proposed data generation methods, as well as super-resolution capabilities of several architectures.
CRIS-Team WoS Importer:2022-02-04

Analysis of 60 GHz Radar Channel Characteristics in Indoor and Outdoor Environments - Reitz, Philipp; Künzle, Christian; Franchi, Norman; Lübke, Maximilian (Kleinheubacher Tagung, 2024)

Antenna Setup for Future Joint Radar-Communications – Characteristics and Mounting Positions - Lübke, Maximilian; Fuchs, Jonas; Dubey, Anand; Frank, Martin; Franchi, Norman; Lurz, Fabian (Advances in Radio Science : Kleinheubacher Berichte, 2023)

A Simple and Low-Cost Technique to Measure the Magnetic Susceptibility of Ferrofluids - Thalmayer, Angelika; Xiao, Keyu; Lübke, Maximilian; Borin, Dmitry; Odenbach, Stefan; Unterweger, Harald; Helmreich, Klaus; Fischer, Georg (IEEE Sensors, 2023)

Automotive Radar Interference Mitigation using a Convolutional Autoencoder - Fuchs, Jonas; Dubey, Anand; Lübke, Maximilian; Weigel, Robert; Lurz, Fabian (2020 International Conference on Radar (RADAR), 2020)
Automotive radar interference imposes big challenges on signal processing algorithms as it raises the noise floor and consequently lowers the detection probability. With limited frequency bands and increasing number of sensors per car, avoidance techniques such as frequency hopping or beamforming quickly become insufficient. Detect-and-repair strategies have been studied intensively for the automotive field, to reconstruct the affected signal samples. However depending on the type of interference, reconstruction of the time domain signals is a highly non-trivial task, which can affect following signal processing modules. In this work an autoencoder based convolutional neural network is proposed to perform image based denoising. Interference mitigation is phrased as a denoising task directly on the range-Doppler spectrum. The neural networks shows significant improvement with respect to signal-to-noise-plus-interference ratio in comparison to other state-of-the-art mitigation techniques, while better preserving phase information of the spectrum compared to other technique

BayesRadar : Bayesian Metric-Kalman Filter Learning for Improved and Reliable Radar Target Classification - Dubey, Anand; Santra, Avik; Fuchs, Jonas; Lübke, Maximilian; Weigel, Robert; Lurz, Fabian (2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021)
Automotive radar sensors offer a promising and effective modality for perception and assessment of the surrounding environment. A key element of environment sensing in automotive radars is the reliable detection, classification and tracking of vulnerable road users such as pedestrians and cyclists. In this paper, we propose an integrated Bayesian framework dubbed BayesRadar, which improves the overall classification accuracy by tracking the embedding vector of a neural network and its prediction uncertainty via recursive Kalman filtering over time. Apart from the classification accuracy of a model, a critical measure includes the analysis of statistical confidence over the target class score. Such measures for predicting the true correctness likelihood of the classification estimates are essential in safety-critical automotive applications. Therefore, in this paper, we present and evaluate the classification, embedding cluster score and statistical confidence performance of the proposed framework in the context of classifying vulnerable road users compared to state-of-art deep learning approaches. Furthermore, we demonstrate superior performance of the BayesRadar for unseen classes compared to long short-term memory based temporal tracking of the embedding vectors.<br
Keywords: Automotive radar; Bayesian framework; uncertainty estimation; vulnerable road user classification

Channel Characterization at 77 GHz for Vehicular Communication - Lübke, Maximilian; Hamoud, Hussein; Fuchs, Jonas; Dubey, Anand; Weigel, Robert; Lurz, Fabian (IEEE Vehicular Networking Conference, 2020)
In this paper, the investigation of the channel characteristics for mmWaves is addressed due to its recent sparked interest in modern communication technologies. Modern intelligent transportation systems require higher data rates and, therefore, more bandwidth, utilization of higher frequencies, which can provide these needs, get more and more in focus in the context of 5G. As part of our current research, the proposed work aims to give a deeper understanding of the channel characteristics and wave propagation at 77 GHz, especially for automotive use cases like platooning or intersection assistant. Typical traffic scenarios, starting with a rural street and proceeding with a highway scene, were reconstructed using the commercial simulator WinProp. Furthermore, the scenarios are evaluated by including additional obstacles, like vehicles and traffic signs. Time-varying environments with driving vehicles with either line-of-sight (LOS) as well as non-LOS (NLOS) connections were investigated. The results reveal significant changes in the channel impulse response for one time step, when, especially relevant in the NLOS case, a new reflection path gets available as scattering and reflection paths have more than 25 dB difference in receiving amplitude. Therefore, the antenna alignment and the environment had a strong influence. Thus, it could be shown that scatters like traffics signs cause power fading for the LOS connection, whereas they benefit the NLOS case, by enabling communication at all. In future research, the channel models will be further addressed and expanded to design a link that is capable of communicating safety-relevant information and hold the high demands on latenc

Code-Orthogonal PMCW MIMO ISAC System: Digital Beamforming for Pilot and Data Separation - Su, Yanpeng; Franchi, Norman; Lübke, Maximilian (WSA 2025 - Proceedings of the 28th International Workshop on Smart Antennas, 2025)
Phase-modulated continuous waveform (PMCW) has attracted wide attention for integrated sensing and communications (ISAC). Aiming at the drawback of limited communications spectrum efficiency, a code-orthogonal PMCW (CO-PMCW) transmission scheme was proposed, which improves the data rate by the simultaneous transmission of radar and communication signals while raising the problem of mutual interference (MI) between them. This paper introduces the design strategy of the CO-PMCW-based multiple-input and multiple-output (MIMO) ISAC system with customized digital beamforming for MI suppression. The simulation results demonstrate the applicability of the designed system and its merits and demerits compared to PMCW.
CRIS-Team Scopus Importer:2025-11-28
Keywords: 6G; digital beamforming; integrated sensing and communications (ISAC); mmWave; multiple-input and multiple-output (MIMO); phase-modulated continuous waveform (PMCW)

Combining Radar and Communication at 77 GHz Using a CDMA Technique - Lübke, Maximilian; Fuchs, Jonas; Shatov, Victor; Dubey, Anand; Weigel, Robert; Lurz, Fabian (2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), 2020)

Commercial and Scientific Solutions for Blood Glucose Monitoring—A Review - Xue, Yirui; Thalmayer, Angelika; Zeising, Samuel; Fischer, Georg; Lübke, Maximilian (Sensors, 2022)

Comparative Evaluation of a New Sensor for Superparamagnetic Iron Oxide Nanoparticles in a Molecular Communication Setting - Bartunik, Max; Unterweger, Harald; Alexiou, Christoph; Schober, Robert; Lübke, Maximilian; Fischer, Georg; Kirchner, Jens (EAI BICT 2020 - 12th EAI International Conference on Bio-inspired Information and Communications Technologies, 2020)
Testbeds are required to assess concepts and devices in the context of molecular communication. These allow the observation of real-life phenomena in a controlled environment and therefore present the basis of future work. A testbed using superparamagnetic iron oxide nanoparticles (SPIONs) as information carriers was constructed with regard to this context and requires a sensitive receiver for the detection of SPIONs. This paper focusses on the comparison between a newly presented device (inductance sensor), a previously constructed SPION sensor (resonance bridge), and a commercial susceptometer as reference. The new inductance sensor is intended to improve on a low sensitivity achieved with the previous device and restrictions with respect to sample rate and measurement aperture encountered with the susceptometer. The signal-to-noise ratio (SNR) for each device is assessed at a variety of SPION concentrations. Furthermore, the sensors bit error rates (BER) for a random bit sequence are determined. The results show the device based on an inductance sensor to be the most promising for further investigation as values both for BER and SNR exceed those of the resonance bridge while providing a suciently high sample rate. On average the SNR of the new device is 13 dB higher while the BER for the worst transmission scenario is 9% lower. The commercial susceptometer, although returning the highest SNR, lacks adaptability for the given use case.<br
Keywords: Molecular Communication; Superparamagnetic Iron Oxide; Nanoparticles; SPION; Testbed; Susceptometer; Resonance Bridge; Inductance Sensor; SNR; BER

Comparing mmWave Channel Simulators in Vehicular Environments - Lübke, Maximilian; Dimce, Sigrid; Schettler, Max; Lurz, Fabian; Weigel, Robert; Dressler, Falko (2021 IEEE 93rd Vehicular Technology Conference, 2021)

Coordinated Multipoint JCAS in 6G Mobile Networks - Su, Yanpeng; Lübke, Maximilian; Franchi, Norman (IEEE Access, 2024)
Joint communications and sensing (JCAS) is regarded as a key technology of 6G communications since it allows the integration of sensing functions into communications networks with lower hardware costs, less energy consumption, and saved spectrum. As distributed sensing benefits from increased spatial diversity and accuracy, the combination of coordinated multipoint (CoMP) transmission technology and JCAS has become an attractive research direction. CoMP transmission has been applied in 4G and 5G communications networks due to its attractive capability in suppressing inter-cell interference and improving channel capacity, while CoMP sensing and CoMP JCAS have been widely discussed in recent years. However, relevant researches mainly focus on waveform design and optimization issues in the processes of CoMP signal transception and processing, while their system architecture and operation modes are less discussed. To fill the blank in these areas and discuss the benefits provided by CoMP JCAS, in this work, based on the modern CoMP transmission technologies in state-of-the-art mobile network architecture, we analyze the network architecture and required enabler technologies of CoMP JCAS in future mobile networks, focusing on the sensing aspect. In addition, we propose the potential sensing operation and clustering types and discuss their respective advantages and shortcomings. In the simulation, the sensing coverage and accuracy of different CoMP sensing types and conventional standalone sensing are compared. The simulation results show that CoMP sensing outperforms conventional single-radar sensing in accuracy, and the difference between them increases with the distance between the sensors. In addition, it is described that the coordinated passive sensing scheme extends the service area, while the active sensing scheme benefits from higher accuracy with the same bandwidth.<br
Keywords: 6G; B5G; coordinated multipoint (CoMP); cloud radio access network (C-RAN); joint communications and sensing (JCAS); mobile network; networked radar.

Data Consistency Determination with Multi-Event Detection for Heterogeneous Sensors - Ruhland, Tim; Lübke, Maximilian; Tobola, Andreas; Franchi, Norman (IEEE I2MTC – International Instrumentation and Measurement Technology Conference, 2025)

Data Quality in IoT Temperature Sensor Systems: Demonstrated on Time-Dependent Temperature Fluctuations - Ruhland, Tim; Tobola, Andreas; Scholl, Christoph; Lübke, Maximilian; Franchi, Norman (IEEE Sensors Journal, 2024)
The growing integration of the Internet of Things (IoT) in Industry 4.0 is primarily attributed to its low cost and high adaptability. Our research addresses the expansive application of IoT temperature sensor systems within industrial environments characterized by harsh conditions and rapid temperature fluctuations. Typically, these sensors lack awareness of the data quality they measure and provide. The article mitigates this limitation by introducing data quality models that enhance the accuracy and timeliness of temperature sensor data. These models interpret temperature readings affected by thermal lag and delayed response times. Timeliness is determined using three distinct methods that reflect data volatility, while accuracy is assessed by applying the Savitzky-Golay filter, which provides temperature transients. Both data quality models precede a knowledge-based approach, accumulating necessary information for the sensor system through calibration. These quality categories are combined into a unified Quality of Sensing (QoS) parameter, utilizing fuzzy logic to abstract the quality dimensions, thereby having profound implications for the IoT sensor system. This methodology has been encapsulated within a comprehensive framework and validated using an industrial-grade IoT temperature sensor in a real-world measurement setup. By transcending the conventional reliance on raw sensor outputs, our framework empowers the extraction of intrinsic quality information associated with each temperature reading, enhancing the resilience and reliability of IoT temperature sensor systems.<br

Data Set of Channel Simulations of an Urban Platooning Scene at the 'Leipziger Platz' at the City Center of Berlin - Lübke, Maximilian; Hamoud, Hussein (2021)
This archive contains simulation data corresponding to platoon simulation at the "Leipziger Platz" in the city center of Berlin at 60 GHz. Two platoons of 10 vehicles each drive towards each other. The cars/trucks of the lower platoon act as transmitters, the vehicles of the upper platoon have each receiving antennas mounted at 0.5m at the front bumper. Two antenna setups were evaluated: one omnidirectional pattern as well as one additional antenna whose pattern is shown in detail in the director

Deep Learning-Based Multi-Radar Fusion for Robust Real-Time Object Detection - Reitz, Philipp; Veihelmann, Tobias Christian; Bönsch, Jonas; Franchi, Norman; Lübke, Maximilian (IEEE Sensors Letters, 2026)
Low resolution, sparse reflections, and environmental noise limit the reliability of radar-based object detection. This letter presents a YOLO-inspired deep learning model with dual-radar fusion to enhance detection robustness. Range-Doppler maps from two static 60 GHz FMCW radars are processed using a dual-backbone architecture with CBAM-based attention and a lightweight dynamic weighting module. The system monitors moving humans in a parking garage. At the best operating point, the proposed fusion improves the F1-score from 0.944 (single radar) to 0.962, with precision/recall increasing from 0.930/0.959 to 0.953/0.972. At matched recall (≈0.967), the false positive rate decreases from 0.070 to 0.031, corresponding to a reduction of about 55%. Real-time performance is maintained with inference speeds above 100 FPS on a desktop CPU. These results demonstrate that dual-radar feature fusion enables accurate and efficient radar perception in cluttered environment

Determination of Changes in NaCl Concentration in Aqueous Solutions Using an M-Sequence Based Sensor System - Schmidt, Christian; Lübke, Maximilian; Dietz, Marco; Weigel, Robert; Kissinger, Dietmar; Hagelauer, Amelie Marietta (IEEE International Microwave Bio-Conference, 2017)

Digital Twins for Radar Object Detection: Improving Deep Learning with Synthetic Data - Reitz, Philipp; Künzle, Christian; Franchi, Norman; Lübke, Maximilian (EuMW, 2025)

Dual radar vision: A feature fusion approach for advanced object detection in IoT radar networks - Reitz, Philipp; Veihelmann, Tobias Christian; Franchi, Norman; Lübke, Maximilian (Machine Learning with Applications, 2025)

Dual-Task Supervised Learning for Distributed Spectrum Monitoring Applications - Shatov, Victor; Shanin, Nikita; Veihelmann, Tobias Christian; Perner, Bastian; Franchi, Norman; Lübke, Maximilian (Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2025)

Dynamic Channel Characterization for Vehicular ISAC in Intersection Scenarios at 77 GHz - Zhang, Guojin; Franchi, Norman; Lübke, Maximilian (WSA 2025 - Proceedings of the 28th International Workshop on Smart Antennas, 2025)
Integrated sensing and communication (ISAC) for vehicular applications has attracted significant attention due to its potential to improve traffic safety and enhance spectral efficiency. To enable reliable system design and performance evaluation for vehicular applications, an accurate understanding and modeling of the time-varying characteristics of vehicular ISAC channels are essential. In this paper, a dynamic sensing channel model is proposed for vehicular ISAC scenarios, based on 3D Ray tracing simulations conducted at the frequency of 77 GHz in the intersection scenario for total 12 s. An improved multipath-component-distance (MCD) threshold-based cluster identification and tracking algorithm for sensing channel is proposed, which groups the multipath components (MPCs) contributed from the same target vehicles or surrounding buildings into clusters and subsequently tracks their evolution over time. To model the dynamic evolution process of channel, parameters characterizing the birth-to-death behavior of clusters are investigated. These include the cluster survival duration, initial (birth) point, number of born clusters and cluster evolution in the delay, azimuth and Doppler domains, which constitute a dynamic model for the outdoor vehicular ISAC channel.
CRIS-Team Scopus Importer:2025-12-05
Keywords: cluster tracking; dynamic channel modeling; Integrated sensing and communication (ISAC); intersection scenarios; vehicular scenario

Enabling Mobility-Oriented JCAS in 6G Networks: An Architecture Proposal - Rosemann, Philipp; Partani, Sanket; Miranda, Marc; Mähn, Jannik; Karrenbauer, Michael; Meli, William; Hernangómez, Rodrigo; Lübke, Maximilian; Kochems, Jacob; Köpsell, Stefan; Aziz-Koch, Anosch; Askar, Ramez; et al. (2024 IEEE 4th International Symposium on Joint Communications & Sensing (JC&S), 2024)

Estimating Global Horizontal Irradiance Using Tilted Off-the-Shelf Light Sensors via Reverse Transposition - Veihelmann, Tobias Christian; Reitz, Philipp; Lübke, Maximilian; Franchi, Norman (IEEE Photovoltaic Specialists Conference, 2025)

Evaluating Communication and Sensing Quality in Phase-Modulated FMCW Radar - Bönsch, Jonas; Reitz, Philipp; Franchi, Norman; Lübke, Maximilian (5th IEEE International Symposium on Joint Communications & Sensing, 2025)

Evaluating RF Hardware Characteristics for Automotive JCRS Systems Based on PMCW-CDMA at 77 GHz - Lübke, Maximilian; Su, Yanpeng; Franchi, Norman (IEEE Access, 2023)

Evaluating Synthetic Data Potential for 60 GHz FMCW Radar Simulations with Real Measurements - Reitz, Philipp; Maiwald, Timo; Franchi, Norman; Weigel, Robert; Lübke, Maximilian (11th Microwave and Radar Week, 2024)
A fully integrated simulation environment for an FMCW radar system in an indoor environment operating at 60 GHz has been proposed. The simulation of the entire signal processing chain, starting with a CST simulation of the antenna pattern and extending to the radar signal processing, enabled the demonstration of the radar hardware’s and the channel’s inherent properties. Measurements and simulations have revealed the inherent fluctuation paths of a human target by showing a variation of 10 dB in the reflection power of the human and 0.5 m in the range profile in measurements. The papers results could demonstrate that synthetic datasets can be considered valid alternatives in radar detection. The channel properties could be examined by investigating different up-tilt configurations, and problems such as ground reflections caused by side lobes and multipath propagation within the human target could be further investigated.<br
Keywords: radar cross section; FMCW radar; simulation and measurement; mm-wave

Evaluating the Performance of UE-Side Bistatic Sensing using PRS on a Highway Entry Scenario - Gröneweg, Jan Steffen; Gherekhloo, Soheil; Lübke, Maximilian; Franchi, Norman (WSA 2025 - Proceedings of the 28th International Workshop on Smart Antennas, 2025)
This paper evaluates the performance of User Equipment (UE)-side bistatic sensing, utilizing the Position Reference Signal (PRS). The study investigates the feasibility of using PRS for integrated sensing and communication (ISAC) in a highway entry scenario for automotive applications. A system model is presented in which a UE located in a vehicle on the slip road of the highway entry, along with a Base Station (BS) transmitting the PRS, employs a bistatic radar system. The BS shares the configuration of the PRS resources with the UE, enabling the UE to determine the angle of departure of the PRS resources. This system is used to observe the traffic on the highway and to estimate suitable gaps for entering the highway. Simulation results, obtained using the Simulation of Urban Mobility (SUMO) traffic simulation, analyse the False Alarm Rate (FAR) and the vehicle merge rate while varying the number of vehicles per hour (vph) on the highway. The findings demonstrate that excluding sections with a Line of Sight path between the UE and BS improves sensing performance, and that lower numbers of vph leads to better overall results.
CRIS-Team Scopus Importer:2025-11-28
Keywords: 5G; 6G; automotive radar; highway scenario; ICAS; OFDM-Radar; PRS; SUMO

Evaluating the Performance of UE-Side Sensing in Multistatic ISAC Systems for Highway Scenarios - Gröneweg, Jan-Steffen; Gherekhloo, Soheil; Lübke, Maximilian; Franchi, Norman (IEEE International Symposium on Joint Communications & Sensing, 2025)

Evaluation of Different Antenna Positions for Joint Radar-Communication at 77 GHz - Lübke, Maximilian; Fuchs, Jonas; Dubey, Anand; Frank, Martin; Weigel, Robert; Lurz, Fabian (Kleinheubacher Tagung 2021, 2021)
The development of millimeter wave systems is driven by the strong trend towards the new communication generations and especially by the emerging joint radar and communication design approach. Safety critical applications like platooning or intersection assistance will significantly benefit from the combination of sensing and communication. This combination requires that both operate on the same frequency bands either in a co-design, a cooperation or even in a coexistence, using the same hardware. However, each technology has its own requirements. While radar performs a channel measurement and thus, needs a wide field of view, communication aims to minimize the interference for other not addressed receivers (e. g. in a platoon) by a focused antenna design. The proposed work analyses the effect of antenna positioning and antenna characteristics (omnidirectional and focused) for a typical automotive scene by evaluating both the communication and sensing performance. Especially, the positions at the front bumper – typical for radar sensors– and at the left mirror convinced regarding the vehicular communication as well as the sensing behaviour. <br

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments - Reitz, Philipp; Künzle, Christian; Franchi, Norman; Lübke, Maximilian (IEEE Access, 2024)
Frequency-modulated continuous-wave (FMCW) radars are becoming increasingly important in industrial applications due to their low cost, simple signal processing, and high resolution. The research is driven by the growing trend of radar applications in industrial settings, including tasks like autonomous robot navigation, radar-based monitoring of humans, and enhancing safety across various scenarios, such as smart factories, traffic control, or environmental monitoring. The network of distributed sensor nodes is a feature to be implemented by the sixth generation of wireless mobile networks. As a result, 6G will become an enabler technology that will develop even further use cases and advance the expansion of these technologies. Therefore, the mutual interference of these sensor systems is becoming highly relevant since they can be avoided by scheduling the individual resources of the sensor nodes by a network structure. This paper investigates the interference behavior of FMCW radar sensors in a dense indoor scenario. In these environments, in particular, the reliability of the sensor system plays an important role, and disruption can have critical consequences like not recognizing a target, triggering a false alarm, or the complete failure of the sensing. To correctly classify the expected interference in the overall context, the paper investigates the physical layer effects on the performance, such as multipath propagation, channel spread, delay, Doppler shift, and attenuation. By evaluating these effects using state-of-the-art signal processing, it was revealed that a high channel spread in particular and the dynamics of the radar channel can significantly reduce the detection quality, as the interference power can exceed the received power by up to 20 dB, depending on the scenario. The simulation results given in this paper show that a potential communication between the radars and compliance with a synchronization delay, the interference problems can be reliably prevented with an appropriately designed anti-aliasing filter. Our simulations have quantified the detection performance under interference through the entire signal chain by evaluating the physical layer, the radar signal processing with non-idealities, and the hardware. The paper has investigated interference probability in indoor environments by examining radars with uncoordinated transmissions. Integrating findings from the physical propagation characteristics, the deviations in chirp durations, and the frequency of interference cases offers insights into the potential risk of interferenc
Keywords: Interference; Chirp; Radar; Sensors; Delays; Radar signal processing; Time-frequency analysis; Frequency modulation; Continuous wavelet transforms; Channel estimation; Robot sensing systems; Radar signal processing; Sensor systems; Physical layer; Wireless sensor networks; Wireless communication; Environmental monitoring; Telecommunication traffic; Detection algorithms; Performance evaluation; Channel simulation; CIR; FMCW radar; industrial radar; interference; interference mitigation; mmWave; multipath

Experimental and Simulative Analysis of Magnetic Nanoparticle Accumulation Using Various Halbach Arrays - Thalmayer, Angelika; Götz, Kilian; Lübke, Maximilian; Fischer, Georg (Current Directions in Biomedical Engineering, 2024)

Feasibility of Orthogonal Taguchi Arrays for Determining Parameter Trends in MDT - Thalmayer, Angelika; Xiao, Keyu; Fischer, Georg; Lübke, Maximilian (Current Directions in Biomedical Engineering, 2025)

Full Physical Layer Simulation Tool to Design Future 77 GHz JCRS-Applications - Lübke, Maximilian; Su, Yanpeng; Cherian, Athira Jess; Fuchs, Jonas; Dubey, Anand; Weigel, Robert; Franchi, Norman (IEEE Access, 2022)
Vehicular communication systems get more and more attention with the upcoming fifth and sixth generation. Hereby, the focus lies on the development of the co-design or co-existence of communications and sensing. So called joints communications and radar sensing systems are seen as one key technology of 6G. As joint systems will have shared waveforms and hardware platforms, there is a huge benefit in cost and space which is one essential argument for the automotive industry. However, to design such a system for new applications like platooning or intersection assistance a physical layer has to be set up to represent the real physical layer properly. The proposed system design closes the lack of such a simulation tool and allows for full physical layer simulations, including e. g. the hardware non-idealities and the channel model for 77 GHz. The whole signal processing chain of the physical layer is built up and will be integrated in the higher layer state-of-the-art simulation frameworks like Veins or Artery in the next step. The communications design is developed in Simulink, whereas the sensing part is discussed. The proposed communications architecture covers several transmission approaches (serial, parallel), a CDMA based spreading, a radio frequency representation, the channel (simulated in a 3D-Ray-Tracing-tool) and the receiver structure (e. g. the synchronisation or the channel estimation). Several design criteria are discussed, like the serial or parallel design architecture, the maximum ratio combining or the phase and frequency compensation method. The whole system architecture is freely available (https://doi.org/10.5281/zenodo.6482565) and in consequence, the different signal blocks and parameters can be enabled or disabled for evaluations according to future design criteria requirements. <br

Generative Adversial Network based Extended Target Detection for Automotive MIMO Radar - Dubey, Anand; Fuchs, Jonas; Lübke, Maximilian; Weigel, Robert; Lurz, Fabian (2020 International Conference on Radar (RADAR), 2020)
In recent years, the automotive radar systems has gained substantial interest for different applications of autonomous driving. The performance of most applications likes classification and tracking directly relies on accurate target detection. The state-of-the-art detection pipeline is vulnerable to multi-path reflections, clutter noise, interference from another radar and leads to false or ghost detections. To address this issue, an end-to-end target detection pipeline using a residual based U-Net architecture is proposed. In contrast to the conventional approach, the network directly generates the detection map from range-Doppler map. The network uses a generative adversarial training over multiple real world measurements. We demonstrate that the proposed network can learn effectively to detect extended targets and shows significant improvement under increased noise floor in comparison to the state-of-the-art detection technique

German Perspective on 6G – Use Cases, Technical Building Blocks and Requirements Insights by the 6G Platform Germany - Franchi, Norman; Dressler, Falko; Ergenc, Doganalp; Guter, Paulus; Lassen, Vincent; Lübke, Maximilian; Veihelmann, Tobias Christian; Baradie, Shaden; Bassbouss, Louay; Bathelt, Andreas; Eichhorn, Fabian; Fink, Lucas; et al. (FAU University Press, 2024)

HARadNet: Anchor-free target detection for radar point clouds using hierarchical attention and multi-task learning - Dubey, Anand; Santra, Avik; Fuchs, Jonas; Lübke, Maximilian; Weigel, Robert; Lurz, Fabian (Machine Learning with Applications, 2022)
Target localization and classification from radar point clouds is a challenging task due to the inherently sparse nature of the data with highly non-uniform target distribution. This work presents HARadNet, a novel attention based anchor free target detection and classification network architecture in a multi-task learning framework for radar point clouds data. A direction field vector is used as motion modality to achieve attention inside the network. The attention operates at different hierarchy of the feature abstraction layer with each point sampled according to a conditional direction field vector, allowing the network to exploit and learn a joint feature representation and correlation to its neighborhood. This leads to a significant improvement in the performance of the classification. Additionally, a parameter-free target localization is proposed using Bayesian sampling conditioned on a pre-trained direction field vector. The extensive evaluation on a public radar dataset shows an substantial increase in localization and classification performance.<br
Keywords: Multi-task learning; Radar detection; Scene understanding

High-Resolution Direction-of-Arrival Estimation using Distributed Radar Sensors - Fuchs, Jonas; Kasper, Alexander; Lübke, Maximilian; Dubey, Anand; Lurz, Fabian (2022 IEEE Radio and Wireless Symposium (RWS), 2022)
We present an approach to high-resolution direction-of-arrival estimation for distributed radar sensors without the need for synchronization or wired connections. The sensor signals are post-processed and combined after range-Doppler processing achieving a larger virtual aperture and multiple snapshots. The proposed approach is validated via simulations and the effectiveness is compared to other baseline estimators. Finally, the applicability on real data is demonstrated using measurement
Keywords: Radar Signal Processing; Direction-of-Arrival Estimation; Wireless Sensor Networks; Spectrum Estimation

How Connected Cars Can Improve Solar Forecasting - Expanding the Scale of Local Sensor Networks - Veihelmann, Tobias Christian; Lübke, Maximilian; Franchi, Norman (41st European Photovoltaic Solar Energy Conference and Exhibition, 2024)

How Connected Cars Could Capture Cloud Dynamics for Solar Forecasting—Evidence from Two Simulation Scenarios - Veihelmann, Tobias Christian; Reitz, Philipp; Lübke, Maximilian; Franchi, Norman (Solar RRL, 2024)

Hybrid Predictive Modeling for Single-Sensor Data Consistency in Industrial IoT - Ruhland, Tim; Müller, Kilian; Lübke, Maximilian; Tobola, Andreas; Franchi, Norman (21st International Conference on Distributed Computing in Smart Systems and the Internet of Things, 2025)

Impact of parallel interference on FMCW radar detection in indoor environments - Reitz, Philipp; Franchi, Norman; Lübke, Maximilian (13th edition of the IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, 2024)

Improving Resource Efficiency of PMCW-Based JCRS Systems: Simultaneous Transmission of Pilot and Data via Orthogonal Codes - Su, Yanpeng; Shatov, Victor; Lübke, Maximilian; Franchi, Norman (IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - Workshop: International Workshop for Integrated Sensing and Communication, 2023)

Integrated Classification and Localization of Targets using Bayesian Framework in Automotive Radars - Dubey, Anand; Santra, Avik; Fuchs, Jonas; Lübke, Maximilian; Weigel, Robert; Lurz, Fabian (2021 IEEE International Conference on Acoustics, Speech and Signal Processing, 2021)
Automatic radar based classification of automotive targets, such as pedestrians and cyclist, poses several challenges due to low inter-class variations among different classes and large intra-class variations. Further, different targets required to track in typical automotive scenario can have completely varying dynamics which gets challenging for tracker using conventional state vectors. Compared to state-of-the-art using independent classification and tracking, in this paper, we propose an integrated tracker and classifier leading to a novel Bayesian framework. The tracker’s state vector in the proposed framework not only includes the localization parameters of the targets but is also augmented with the targets’s feature embedding vector. In consequence, the tracker’s performance is optimized due to a better separability of the targets. Furthermore, the classifier’s performance is enhanced due to Bayesian formulation utilizing the temporal smoothing of classifier’s embedding vecto
Keywords: Automotive Radar; Bayesian Classification; Triplet Variational Auto-encoder; Unscented Kalman Filter

Integrated Sensing and Communication Based on Road Side Units for Intersection Monitoring - Bönsch, Jonas; Reitz, Philipp; Motzelt, Julian; Franchi, Norman; Lübke, Maximilian (2025 28th International Workshop on Smart Antennas (WSA), 2025)
Combining sensing and communication into a single system allows the integration of radar sensors mounted on locally installed road side units into future intelligent transportation systems. These units have great potential to improve safety in dense intersection scenarios. However, this combined functionality is limited due to their potential interference. This paper investigates the influences of antenna characteristics on sensing and communication performance in crossing scenarios at 77 GHz. Different antenna characteristics are compared in ray-tracing simulations. The radar sensing performance is evaluated based on the maximum peak detection in the range-Doppler map, while the communication is evaluated based on the channel characteristics. The different antenna types achieve either a higher detection power in the angular region or a higher antenna focus. The communication is compared with respect to the root mean square delay spread and the minimum timing delay for an interferencefree frequency-modulated continuous wave (FMCW) sensing. The power of the line-of-sight path between the radar-equipped road side units varies over 20 dB between the different antennas. The alignment with each other results in even higher gains and losses. The results show that coordinating the different RSUs with an additional time or frequency scheduling is necessary. In the future, the intersection scene will be improved with more road users to investigate the antenna characteristics' impacts furthe

ISAC-Enabled OFDM Radar: ICI and Off-Grid Effect Suppression via Signal Reconstruction - Su, Yanpeng; Zhang, Guojin; Franchi, Norman; Lübke, Maximilian (IEEE Transactions on Vehicular Technology, 2025)
Orthogonal frequency-division multiplexing (OFDM) has drawn a lot of attention in the study of integrated sensing and communications (ISAC). However, the fundamental drawback of Doppler intolerance limits its applicability in highly dynamic scenarios such as vehicular applications. In addition, the delay and Doppler shift that are non-integer times the resolution cause the off-grid effect, leading to scalloping loss and increasing the sidelobes. In this work, a super-resolution range-doppler map (RDM) modeling algorithm with low complexity is proposed to suppress these impacts. The proposed algorithm models the RDM contributed by the detected targets jointly via 2D surface fitting and then reconstructs and subtracts the corresponding signal components from the original received signal. Consequently, the weaker targets hiding under the inter-carrier interference (ICI) and off-grid effect become detectable. The communications capability is not impacted since the algorithm is only deployed at the radar receiver. In addition, two variants of the algorithm are illustrated, showing a trade-off between accuracy and complexity. Simulation results demonstrate that the proposed algorithms can effectively suppress ICI and mitigate off-grid effects, while simultaneously enhancing the accuracy of delay and Doppler estimation. It is indicated that the proposed algorithms can provide sufficient improvements in multi-target scenario
Keywords: OFDM; Radar; Symbols; Estimation; Doppler radar; Delays; Interference; Integrated sensing and communication; Accuracy; Doppler shift

ISAC in Dense Automotive Scenarios: Influence of Antenna Characteristics and Placement - Lübke, Maximilian; Weigel, Robert; Franchi, Norman (Kleinheubacher Tagung 2025, 2025)

Joint Communications & Radar Sensing: Analyse der physikalischen Schicht - Lübke, Maximilian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2023)

Joint Radar and Communications: Architectures, Use Cases, Aspects of Radio Access, Signal Processing, and Hardware - Shatov, Victor; Nuß, Benjamin; Schieler, Steffen; Bishoyi, Pradyumna; Wimmer, Lara; Lübke, Maximilian; Keshtiarast, Navid; Fischer, Christoph; Lindenschmitt, Daniel; Geiger, Benedikt; Thomae, Reiner S.; Fellan, Amina; et al. (IEEE Access, 2024)
Keywords: 6G; joint communications and sensing (JCAS); joint radar and communications (JRC); integrated sensing and communications (ISAC); radar-communications (RadCom); radio access network (RAN) architecture

Leveraging gradient-boosted trees to combine FFT, correlation and Fréchet-distance features for clear-sky detection - Veihelmann, Tobias Christian; Seifaei, Masoud; Lübke, Maximilian; Franchi, Norman (40th European Photovoltaic Solar Energy Conference and Exhibition, 2023)

Localization of Passively Guided Capsule Endoscopes-A Review - Zeising, Samuel; Thalmayer, Angelika; Lübke, Maximilian; Fischer, Georg; Kirchner, Jens (IEEE Sensors Journal, 2022)

LSTM-DNN-Based Joint Wireless Signal Waveform Classification and Blind Transmitter Localization - Shatov, Victor; Shanin, Nikita; Perner, Bastian; Su, Yanpeng; Franchi, Norman; Lübke, Maximilian (International Symposium on Wireless Communication Systems (ISWCS), 2024)

Novel Receiver for Superparamagnetic Iron Oxide Nanoparticles in a Molecular Communication Setting - Bartunik, Max; Lübke, Maximilian; Unterweger, Harald; Alexiou, Christoph; Meyer, Sebastian; Ahmed, Doaa; Fischer, Georg; Wicke, Wayan; Jamali Kooshkghazi, Vahid; Schober, Robert; Kirchner, Jens (Proceedings of the 6th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2019, 2019)
Superparamagnetic iron oxide nanoparticles (SPIONs) have recently been introduced as information carriers in a testbed for molecular communication (MC) in duct flow. Here, a new receiver for this testbed is presented, based on the concept of a Wheatstone bridge. The capability for a reliable transmission using the testbed and detection of the proposed receiver was evaluated by sending a text message and a 80 bit random sequence at a bit rate of 1/s, which resulted in a bit error rate of 0 %. Furthermore, the sensitivity of the device was assessed by a dilution series, which gave a limit of detectability of peaks between 0:1 to 0:5 mg/mL. Compared to the commercial susceptometer that was previously used as receiver, the new detector provides an increased sampling rate of 100 samples/s and flexibility in the dimensions of the propagation channel. Furthermore, it allows to implement both single-ended and differential signaling in SPION-bases MC testbeds <br
CRIS-Team Scopus Importer:2019-10-29
Keywords: Molecular communication; superparamagnetic iron oxide nanoparticles; SPION; differtial signaling; receiver

On Integrated Cooperative Radio Sensing for Spatial Electromagnetic Analysis in 6G - Shatov, Victor; Lübke, Maximilian; Su, Yanpeng; Franchi, Norman (2023 IEEE Future Networks World Forum (FNWF), 2023)

On the Detection of Vulnerable Road Users—Simulating Limits of an FMCW Radar - Yüksekkaya, Fatih; Franchi, Norman; Lübke, Maximilian (Electronics, 2024)
The complexity of urban traffic environments poses unique challenges for automotive radar sensors. Limited range and Doppler resolution hinder the ability to distinguish closely spaced objects and detect vulnerable road users, particularly pedestrians. Therefore, it is important to know the limits of FMCW radars. To assess these limitations, the WinProp channel simulator is used to model and simulate traffic scenarios regarding the detection of the range, velocity, and SNR of targets. To validate WinProp as a simulation tool for traffic scenarios, a real traffic scenario is configured and the simulation results compared to the measurement results in order to demonstrate the suitability of WinProp for simulating traffic scenarios and evaluating radar detection performance. The close agreement between the simulation and measurement results provides validation for WinProp’s accuracy in modeling detections and signal processing within traffic scenarios. To showcase the limitations of FMCW radars, two scenarios with challenging detection conditions are simulated: an occluded pedestrian between parked vehicles and a densely populated road. A monostatic setup with a single patch antenna and a 16 × 16 patch antenna array is evaluated, using chirp bandwidths of 1 GHz and 4 GHz. The simulations have shown that although it is feasible to detect an occluded pedestrian by multipath propagation, there is still a need in improving the detection performance when there is no multipath from an occluded pedestrian. Furthermore, the results indicate that higher bandwidth improves target separation but is insufficient for occluded pedestrian detection without multipath. However, with some targets, even the range resolution at 4 GHz was not sufficient, which required a separation in the Doppler dimension, emphasizing the need for overall resolution improvement of FMCW radars. Future work should focus on developing more complex road user models and simulating a broader range of scenarios to comprehensively evaluate radar performance in road traffic environments.<br
Keywords: FMCW radar; raytracing simulation; vulnerable road users

Opportunistic 5G Spectrum Usage for Nomadic Ad-Hoc Emergency Communication - Horbach, Konrad; Perner, Bastian; Rehmann, Willi; Kretschmer, Mathias; Lübke, Maximilian; Rademacher, Michael; Franchi, Norman (2024 IEEE Future Networks World Forum (FNWF), 2024)

Optimized Measurement Location Planning for Interpolation-based Radio Environment Maps - Perner, Bastian; Franchi, Norman; Lübke, Maximilian (EUSIPCO, 2025)

Parking Occupancy Detection through Adaptive Multi-Sensor Camera-CNN Fusion - Lassen, Vincent; Lübke, Maximilian; Franchi, Norman (IEEE Sensors Letters, 2025)
A robust multi-camera sensor system for parking-occupancy detection is introduced, combining convolutional neural networks (CNNs) with an adaptive fusion mechanism that leverages angular diversity. The proposed pipeline integrates viewpoint-specific bounding-box components and a distortion-reduction module that compensates for perspective-induced deformations. Under different azimuth angles and illumination conditions, including overcast, sunny, and nighttime scenarios, the fusion approach consistently outperformed single-camera systems. Notably, fusing cameras at 0∘ and 90∘ yielded an Intersection-over-Union (IoU) of 0.898 without correction, while the distortion-reduction module improved IoU from 0.734 to 0.856 in geometrically challenging cases. The method also maintained robust performance in low-light environments, where individual camera views degraded. Designed for scalability and minimal calibration effort, the architecture supports geometry-consistent localization across multiple sensor perspectives. These results demonstrate that combining angular fusion with correction-aware processing offers substantial gains in precision and robustness. The system is particularly suited for real-world deployment in smart parking applications under complex environmental conditions.<br

Predictive Algorithms for Communications-Control Co-Design in Multi-Robot Systems: Demonstrated with Two Drones in a Leader-Follower Setup - Seifaei, Zahra; Lübke, Maximilian; Franchi, Norman (EW24, 2024)
This paper investigates the joint design of communications and control systems for robots collaborating using Device to-Device connection. Factors like noise and interference in wireless channels can lead to data packet errors, potentially failing in task execution, and robot positioning. This paper addresses these challenges by proposing an outage probability model tailored explicitly for robotic engineers. This model empowers robots as receivers to decide about utilizing the received data. By analyzing the communications channel quality through the outage probability model, the receiver can assess the likelihood of errors and determine whether the data is reliable enough for the task. The model leverages Physical Layer Abstraction models for efficient simulations, and we demonstrate its application through a case study of two drones using a leader-follower approach. By tuning the model and selecting an optimal transmitting time, we minimize locational error at the receiver, crossing a balance between communications overhead and data reliability for improved task performance. As a result of these evaluations, an outage probability model was developed, which can be utilized at the receiver to perform reliable data transmission during the same leader-follower scenari
Keywords: Communications control co-design; D2D communication; leader-follower robotics; outage probability model; robotic collaboration

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3 - Druml, Norbert; Ryabokon, Anna; Schorn, Rupert; Koszescha, Jochen; Ozols, Kaspars; Levinskis, Aleksandrs; Novickis, Rihards; Nigussie, Ethiopia; Isoaho, Jouni; Solmaz, Selim; Stettinger, Georg; Diaz, Sergio; et al. (2021 24TH EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN (DSD 2021), 2021)
Created from Fastlane, WoS look-up

Real-Time Instance Segmentation for Parking Occupancy Detection Using CNNs - Lassen, Vincent; Lübke, Maximilian; Franchi, Norman (2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media, 2025)

Region based Single-Stage Interference Mitigation and Target Detection - Dubey, Anand; Fuchs, Jonas; Madhavan, Venkat; Lübke, Maximilian; Weigel, Robert; Lurz, Fabian (IEEE Radar Conference, 2020)
The inherent smaller radar cross sections of vulnerable road users resulting in smaller signal-to-noise-ratios make an accurate detection of them somewhat challenging. Mutual radar interference in typical automotive scenarios further imposes the difficulty of a target detection by additionally raising the noise floor. The traditional signal processing pipeline consists of multiple but separate stages for interference detection, mitigation and target detection. In this paper, a convolutional neural network based autoencoder architecture is used to perform a combined single-stage target detection while generalizing over different interference noise. The proposed approach achieves significant improvement over state-of-the-art methods while preserving the instance of each target and is able to identify them uniquely in case of a partial occlusion or overlapping of multiple target
Keywords: automotive radar; convolutional autoencoder; interference mitigation; VRU detection

Simulation Environment of a Communication System Using CDMA at 77 GHz - Lübke, Maximilian; Fuchs, Jonas; Shatov, Victor; Dubey, Anand; Weigel, Robert; Lurz, Fabian (2020 International Wireless Communications and Mobile Computing, IWCMC 2020, 2020)
In this paper, an overall concept for a joint communication-sensing system at 77 GHz is presented with special focus on the communication part. To take advantage of the reduced interference between vehicles, code division multiplexing using direct sequence spread spectrum signals is applied. The system, which covers the whole signal processing chain, is introduced, explained and simulated using Simulink/Matlab software. A design, capable of spreading, modulating, including non-idealities of radio frequency-blocks and synchronization, is built up and evaluated. Additionally, a channel model is simulated in the software WinProp and integrated in the Simulink simulation. In consequence, a more realistic model compared to an estimation-based Rician-or Rayleigh channel model is realized. Furthermore, different modulation schemes, binary phase shift keying, 4- and 16-quadrature amplitude modulation, are investigated. The system is rated with respect to the bit-error-rate by applying additive white Gaussian noise. The functionality is verified as the the results match with the theoretical assumptions The system is further improved by building up a Rake-Receiver structure.
CRIS-Team Scopus Importer:2020-08-28
Keywords: 77 GHz; direct spread spectrum communication; joint sensing-communications; rake demodulator

Supervised Learning of Wireless Signal Waveforms in the Era of Radar-Communications Convergence - Shatov, Victor; Vagollari, Adela; Su, Yanpeng; Gerstacker, Wolfgang; Lübke, Maximilian; Franchi, Norman (16th IFIP Wireless and Mobile Networking Conference (WMNC), 2025)

The Demand and the Path to Trustworthy Continuous Blood Glucose Monitoring - Lübke, Maximilian; Thalmayer, Angelika; Lassen, Vincent; Franchi, Norman; Etzlinger, Bernhard (Current Directions in Biomedical Engineering, 2025)

Towards Realisation of a Non-Invasive Blood Glucose Sensor Using Microstripline - Zeising, Samuel; Kirchner, Jens; Fazeli Khalili, Hossein; Ahmed, Doaa; Lübke, Maximilian; Thalmayer, Angelika; Fischer, Georg (International Instrumentation and Measurement Technology Conference (I2MTC), 2020)

Towards Steering Magnetic Nanoparticles in Drug Targeting Using a Linear Halbach Array - Thalmayer, Angelika; Zeising, Samuel; Lübke, Maximilian; Fischer, Georg (Advances in Radio Science : Kleinheubacher Berichte, 2023)

Tracking the Traveled Distance of Capsule Endoscopes along a Gastrointestinal-Tract Model Using Differential Static Magnetic Localization - Zeising, Samuel; Chen, Lu; Thalmayer, Angelika; Lübke, Maximilian; Fischer, Georg; Kirchner, Jens (Diagnostics, 2022)

Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic Simulation - Veihelmann, Tobias Christian; Shatov, Victor; Lübke, Maximilian; Franchi, Norman (Vehicles, 2024)

Validation and Analysis of the Propagation Channel at 60 GHz for Vehicular Communication - Lübke, Maximilian; Fuchs, Jonas; Dubey, Anand; Hamoud, Hussein; Dressler, Falko; Weigel, Robert; Lurz, Fabian (2021 94th Vehicular Technology Conference, 2021)

Validation Data for WinProp and NYUSIM - Lübke, Maximilian; Dimce, Sigrid (2020)
This archive contains simulation data the same scenario that is used in the measurement campaign published in [1-3]. The directories correspond to the respective simulators that have been used. [1] M. Peter, R. Weiler, B. Goktepe, W. Keusgen, and K. Sakaguchi, "Channel measurement and modeling for 5G urban microcellular scenarios," MDPI Sensors, vol. 16, no. 8, p. 1330, 2016 [2] R. Weiler, M. Peter, W. Keusgen, A. Maltsev, I. Karls, A. Pudeyev, I. Bolotin, I. Siaud, and A.-M. Ulmer-Moll, "Quasi-deterministic millimeter-wave channel models in MiWEBA," EURASIP Journal on Wireless Communications and Networking, vol. 2016, Dec. 2016. [3] R. Weiler, M. Peter, W. Keusgen, and M. Wisotzki, "Measuring the busy urban 60 GHz outdoor access radio channel," in IEEE International Conference on Ultra-WideBand (ICUWB 2014), Paris, France, Sep. 2014