MSE-Based Training and Transmission Optimization for MIMO ISAC Systems
<|reference_start|>MSE-Based Training and Transmission Optimization for MIMO ISAC Systems: In this paper, we investigate a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system under typical block-fading channels. As a non-trivial extension to most existing works on ISAC, both the training and transmission signals sent by the ISAC transmitter are exploited for sensing. Specifically, we develop two training and transmission design schemes to minimize a weighted sum of the mean-squared errors (MSEs) of data transmission and radar target response matrix (TRM) estimation. For the former, we first optimize the training signal for simultaneous communication channel and radar TRM estimation. Then, based on the estimated instantaneous channel state information (CSI), we propose an efficient majorization-minimization (MM)-based robust ISAC transmission design, where a semi-closed form solution is obtained in each iteration. For the second scheme, the ISAC transmitter is assumed to have statistical CSI only for reducing the feedback overhead. With CSI statistics available, we integrate the training and transmission design into one single problem and propose an MM-based alternating algorithm to find a high-quality solution. In addition, we provide alternative structured and low-complexity solutions for both schemes under certain special cases. Finally, simulation results demonstrate that the radar performance is significantly improved compared to the existing scheme that integrates sensing into the transmission stage only. Moreover, it is verified that the investigated two schemes have advantages in terms of communication and sensing performances, respectively.<|reference_end|>
@article{he2024mse-based,
    title={MSE-Based Training and Transmission Optimization for MIMO ISAC Systems},
    author={Zhenyao He, Wei Xu, Hong Shen, Yonina C. Eldar, and Xiaohu You},
    journal={arXiv preprint arXiv:2406.03888},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03888},
    primaryClass={cs.IT eess.SP math.IT}
}