Misspecification under the narrowband assumption: a Cramér-Rao bound perspective. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1524-1528
To efficiently extract estimates about the propagation behavior of electromagnetic waves in a radio environment it is common to invoke the narrowband-assumption. It essentially states that the relative bandwidth of the measurement system is so low that the frequency response of a single propagation path only depends on it Time-of-Flight and the response of the measurement device can be calibrated independently of the measured channel. Recent advances into higher relative bandwidths and antenna arrays with larger spatial aperture render this assumption less likely to be satisfied, which leads to a model mismatch during estimation. In this case estimates are inherently biased and have a special statistical behavior. This behavior can be captured by the so-called Misspecified Cramér-Rao Bound, which formulates a lower bound for the variance of estimates that are biased due to model mismatch. We analyze this bound in contrast to the traditional Cramér-Rao Bound and show the shortcomings in the setting of joint ToF-DoA estimation in the mmWave spectrum. The conducted numerical studies also show that planar array geometries inherently suffer from violation of the narrowband assumption irrespective of the individual elements' frequency response, whereas circular structures show it to a lesser degree.
https://doi.org/10.23919/EUSIPCO58844.2023.10289949
Low complexity PMI selection for BICM-MIMO rate maximization in 5G new radio systems. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1445-1449
This paper presents novel methods for selecting the best precoding matrix index (PMI) from the Type- I codebook adopted in 5G New Radio (5G NR (gNB) , in terms of the achievable rate for MIMO-BICM systems. To overcome the complexity of dealing with a multi-variable problem with discrete domains, we introduce heuristic algorithms that exploit the Kronecker and DFT structure of the codebook. Our proposed methods utilize a combination of direct estimation and a low-dimensional search to derive the optimal PMI indices, and the singular value (SV) pre-coder serves as an optimal reference. The approach significantly reduces the number of codebook precoder candidates, resulting in a much lower complexity compared to the exhaustive search methods. Simulation results demonstrate the effectiveness of our proposed algorithms in achieving a performance comparable to the performance obtained by an exhaustive search.
https://doi.org/10.23919/EUSIPCO58844.2023.10290121
Optimization of transmission parameters in fast pulse-echo ultrasound imaging using sparse recovery. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 441-445
In pulse-echo ultrasound imaging, the goal is to achieve a certain image quality while minimizing the duration of the signal acquisition. In the past, fast ultrasound imaging methods applying sparse signal recovery have been implemented by accepting a single pulse-echo measurement. However, they have experienced a certain amount of reconstruction error. In sparse signal recovery, reducing the correlation between the samples of the measurements observed by the different receivers is beneficial for lowering the reconstruction error. Exploiting the Born approximation and Green's function for the wave equation, the analytical inverse scattering problem can be defined in matrix-vector form. Adopting this setting, it has been suggested in the past to reduce the correlation between the samples of the measurement using Cylindrical Waves (CWs) with randomly selected delays and weights. In a similar setting, we created an optimization problem accepting transmission delays and weights as variables to minimize the correlation between the samples of the measurement in each receiver. We demonstrate via simulations that CWs employing the optimized transmission parameters outperformed the cases with Plane Wave Imaging (PWI) and CWs with random transmission parameters in terms of reconstruction accuracy.
https://doi.org/10.23919/EUSIPCO58844.2023.10290105
Machine learning based channel prediction for NR type II CSI reporting. - In: IEEE ICC 2023, (2023), S. 4967-4972
The application of artificial intelligence and machine learning (AI/ML) into the wireless physical layer is under discussion at 3GPP. Channel state information (CSI) prediction is among the sub use cases being studied. In this work, we propose an AI/ML CSI predictor that aims to compensate the scheduling delays at the base station. The AI/ML CSI predictor operates at the user equipment side and generates the channel reporting based on its prediction. Our AI/ML CSI predictor is designed for the intended prediction time, e.g., 5 ms, by collecting a few past measurements at the input. Our architecture is flexible regarding the number of physical resource blocks and can be used by all user equipments within the cell. Our results show that the proposed AI/ML CSI predictor has the 90 % normalized squared error performance around −13 dB and less than 1.4 % of the predicted eigenvectors have a squared generalized cosine similarity below 0.9, which is much better than zero order hold.
https://doi.org/10.1109/ICC45041.2023.10279531
Learning based compressive beam detection using real-valued beamspace covariance processing for mmWave communications. - In: 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), (2023), S. 641-645
In this paper, we present a learning based beam detection scheme using compressive measurements in mmWave band communications. By considering that the measured beam-space covariance (BSC) is the compressive projection of the antenna-element-space covariance (AESC) of the spatial channel, while the latter is directly associated to the optimal communication beam, the upper triangular part of the BSC matrix is selected as the input feature of a feed-forward neural network (NN) which directly detects the best communication beam. We also show that, by designing the training beams with structured random phases to be conjugate symmetric, the real part of the BSC becomes the compressive projection of the forward-backward (FB) averaged version of the AESC. This property leads to a small real-valued NN with less nodes. Simulations show that the proposed scheme outperforms the traditional two-step approach, with only a few measurements.
https://doi.org/10.1109/SPAWC53906.2023.10304433
Circularly polarized patch antenna array for 5G automotive satellite communications. - In: 2023 53rd European Microwave Conference, (2023), S. 794-797
5G low-earth orbit (LEO) satellite communication plays a crucial role in enhancing the reliability and coverage of wireless connectivity for automated and connected driving. Compactness of user equipment antennas presents a key requirement for automotive mass-market applications offering non-terrestrial connectivity in addition to terrestrial mobile communications. Latest studies reveal the potential for moderate-gain antenna terminals for such applications. We present a circularly polarized 4 × 4 patch antenna array operating at a center frequency of 2S GHz in the 5G new-radio band n257 suitable for LEO satellite communications. The antenna is feasible for integration into the rear spoiler of a car, roof-top shark-fin antenna, or other plastic-covered antenna mounting locations. The embedded array offers 12 dBi measured realized gain and 4 GHz of -10 dB impedance bandwidth. It offers a 3-dB axial-ratio bandwidth of 900 MHz, demonstrating its circular polarization purity along the broadside direction. Realistic link budget calculations predict an uplink data rate of 6 Mbit/s, promising for various automotive mobility applications.
https://doi.org/10.23919/EuMC58039.2023.10290591
Deep learning based positioning with beamformed CSI fingerprints. - In: Proceedings of the 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), (2023), insges. 6 S.
User positioning with deep learning (DL) models based on channel state information (CSI) fingerprints, e.g., obtained at a base station (BS), has emerged as a promising technology. Related prior works generally assume a CSI fingerprint with multiple spatial dimensions (i.e antennas or beams) at the BS but only a single spatial dimension at the user equipment (UE). However, a UE may be equipped with multiple antennas or may need to perform beamforming, e.g., to support transmissions at higher frequencies. In this work we consider user positioning with DL models based on uplink beamformed CSI fingerprints considering multiple spatial dimensions at both the BS and the UE. By considering a single or multiple beams at the BS and UE, the use of different CSI fingerprints is proposed. The positioning accuracy achieved with the different beamformed CSI fingerprints is evaluated and compared. The different orientation during training and UE deployment is also considered. In addition, we also consider the positioning of UEs with different spatial capabilities, i.e. with different number of beams. This work provides valuable insights into the design of wireless positioning with CSI fingerprints considering multiple spatial dimensions at both the BS and UE.
https://doi.org/10.1109/IPIN57070.2023.10332494
Demonstration and validation of a 3D wave field synthesis setup for multiple GNSS satellite emulation via over-the-air testing. - In: AMTA 2023 proceedings, (2023), insges. 10 S.
Wireless devices supporting global navigation satellite systems (GNSS) services have become an essential tool in different areas of technology such as agriculture, construction, automotive, etc. Therefore the performance and reliability of such devices are important aspects that need to be addressed in the testing stage during the development of the units. The integration of the Over-the-Air (OTA) testing method with the 3D Wave Field Synthesis (3DWFS) technique offer not only the benefit of having tests under controllable and repeatable conditions but also the ability to recreate complex and realistic scenarios in a controlled environment with full polarimetric support for the testing of wireless devices. This contribution applies this technology to emulate a GNSS scenario within an anechoic chamber. For the results validation, a realistic GNSS outdoor scenario was recorded and compared with the emulated scenario where 3DWFS was applied for each individual satellite. This represents a significant step for the GNSS community and also for the future development and testing of wireless devices.
https://doi.org/10.23919/AMTA58553.2023.10293372
Distributed ISAC systems - multisensor radio access and coordination. - In: 2023 20th European Radar Conference, (2023), S. 351-354
Integrated sensing and communication (ISAC) qualifies mobile radio systems for detecting and localizing of passive objects by means of radar sensing. Advanced ISAC networks rely on distributed infrastructure, multisensor uplink and downlink, or meshed sidelink access. In this way, ISAC develops into a MS-MIMO (multisensor multiple input multiple output) network which constitutes a distributed MIMO radar network. Multisensor link coordination and synchronization are becoming crucial. Many multisensor access and signaling techniques find their communication counterpart in multiuser MIMO and cooperative multilink communications (CoMP) and can be adopted from there.
https://doi.org/10.23919/EuRAD58043.2023.10289611
Numerical synthesis of radar target detections based on measured reference data. - In: 2023 20th European Radar Conference, (2023), S. 26-29
Virtual validation methods strive to reduce the testing efforts of automated driving functions significantly. However, assuring the fidelity of deployed sensor models based on objective criteria remains an unsolved challenge, especially for radar target detection point clouds, since they are subject to major stochastic fluctuations.This work focuses on the scenario-based derivation of requirements for synthetic radar target detections, which are deduced from sensor data recorded in real-world test drives. Based on these reference data, deviations between the radar point clouds from different recordings are quantified using metrics for both point clouds from single measurement cycles and relative probability distributions for accumulated point clouds. Finally, the suitability of the calculated reference values as adequate metrics of deviations between recorded and simulated sensor data is evaluated.
https://doi.org/10.23919/EuRAD58043.2023.10289571