Resolution Improvement in OFDM-based Joint Communication and Sensing through Combined Tracking and Interpolation

We investigate a monostatic orthogonal frequency-division multiplexing (OFDM)-based joint communication and sensing (JCAS) system with multiple antennas for object tracking. The native resolution of OFDM sensing, and radar sensing in general, is limited by the observation time and bandwidth. In this work, we improve the resolution through interpolation methods and tracking algorithms. We verify the resolution enhancement by comparing the root mean squared error (RMSE) of the estimated range, velocity and angle and by comparing the mean Euclidean distance between the estimated and true position. We demonstrate how both a Kalman filter for tracking, and interpolation methods using zero-padding and the chirp Z-transform (CZT) improve the estimation error. We discuss the computational complexity of the different methods. We propose the KalmanCZT approach that combines tracking via Kalman filtering and interpolation via the CZT, resulting in a solution with flexible resolution that significantly improves the range RMSE.

Bistatic OFDM-based ISAC with Over-the-Air Synchronization: System Concept and Performance Analysis

Integrated sensing and communication (ISAC) has been defined as one goal for 6G mobile communication systems. In this context, this article introduces a bistatic ISAC system based on orthogonal frequency-division multiplexing (OFDM). While the bistatic architecture brings advantages such as not demanding full duplex operation with respect to the monostatic one, the need for synchronizing transmitter and receiver is imposed. In this context, this article introuces a bistatic ISAC signal processing framework where an incoming OFDM-based ISAC signal undergoes over-the-air synchronization based on preamble symbols and pilots. Afterwards, bistatic radar processing is performed using either only pilot subcarriers or the full OFDM frame. The latter approach requires estimation of the originally transmitted frame based on communication processing and therefore error-free communication, which can be achieved via appropriate channel coding. The performance and limitations of the introduced system based on both aforementioned approaches are assessed via an analysis of the impact of residual synchronization mismatches and data decoding failures on both communication and radar performances. Finally, the performed analyses are validated by proof-of-concept measurement results.

Loss Design for Single-carrier Joint Communication and Neural Network-based Sensing

We evaluate the influence of multi-snapshot sensing and varying signal-to-noise ratio (SNR) on the overall performance of neural network (NN)-based joint communication and sensing (JCAS) systems. To enhance the training behavior, we decouple the loss functions from the respective SNR values and the number of sensing snapshots, using bounds of the sensing performance. Pre-processing is done through conventional sensing signal processing steps on the inputs to the sensing NN. The proposed method outperforms classical algorithms, such as a Neyman-Pearson-based power detector for object detection and ESPRIT for angle of arrival (AoA) estimation for quadrature amplitude modulation (QAM) at low SNRs.

Bistatic OFDM-based Joint Radar-Communication: Synchronization, Data Communication and Sensing

This article introduces a bistatic joint radar-communication (RadCom) system based on orthogonal frequency-division multiplexing (OFDM). In this context, the adopted OFDM frame structure is described and system model encompassing time, frequency, and sampling synchronization mismatches between the transmitter and receiver of the bistatic system is outlined. Next, the signal processing approaches for synchronization and communication are discussed, and radar sensing processing approaches using either only pilots or a reconstructed OFDM frame based on the estimated receive communication data are presented. Finally, proof-of-concept measurement results are presented to validate the investigated system and a trade-off between frame size and the performance of the aforementioned processing steps is observed.

Autoencoder-based Joint Communication and Sensing of Multiple Targets

We investigate the potential of autoencoders (AEs) for building a joint communication and sensing (JCAS) system that enables communication with one user while detecting multiple radar targets and estimating their positions. Foremost, we develop a suitable encoding scheme for the training of the AE and for targeting a fixed false alarm rate of the target detection during training. We compare this encoding with the classification approach using one-hot encoding for radar target detection. Furthermore, we propose a new training method that complies with possible ambiguities in the target locations. We consider different options for training the detection of multiple targets. We can show that our proposed approach based on permuting and sorting can enhance the angle estimation performance so that single snapshot estimations with a low standard deviation become possible. We outperform an ESPRIT benchmark for small numbers of measurement samples.