Integrated Sensing and Communication enabled Doppler Frequency Shift Estimation and Compensation

Despite the millimeter wave technology fulfills the low-latency and high data transmission, it will cause severe Doppler Frequency Shift (DFS) for high-speed vehicular network, which tremendously damages the communication performance. In this paper, we propose an Integrated Sensing and Communication (ISAC) enabled DFS estimation and compensation algorithm. Firstly, the DFS is coarsely estimated and compensated using radar detection. Then, the designed preamble sequence is used to accurately estimate and compensate DFS. In addition, an adaptive DFS estimator is designed to reduce the computational complexity. Compared with the traditional DFS estimation algorithm, the improvement of the proposed algorithm is verified in bit error rate and mean square error performance by simulation results.

ISAC 4D Imaging System Based on 5G Downlink Millimeter Wave Signal

Integrated Sensing and Communication(ISAC) has become a key technology for the 5th generation (5G) and 6th generation (6G) wireless communications due to its high spectrum utilization efficiency. Utilizing infrastructure such as 5G Base Stations (BS) to realize environmental imaging and reconstruction is important for promoting the construction of smart cities. Current 4D imaging methods utilizing Frequency Modulated Continuous Wave (FMCW) based Fast Fourier Transform (FFT) are not suitable for ISAC scenarios due to the higher bandwidth occupation and lower resolution. We propose a 4D (3D-Coordinates, Velocity) imaging method with higher sensing accuracy based on 2D-FFT with 2D-MUSIC utilizing standard 5G Downlink (DL) millimeter wave (mmWave) signals. To improve the sensing precision we also design a transceiver antenna array element arrangement scheme based on MIMO virtual aperture technique. We further propose a target detection algorithm based on multi-dimensional Constant False Alarm (CFAR) detection, which optimizes the ISAC imaging signal processing flow and reduces the computational pressure of signal processing. Simulation results show that our proposed method has better imaging results. The code is publicly available at https://github.com/MrHaobolu/ISAC\_4D\_IMaging.git.