Compressed Sensing Inspired User Acquisition for Downlink Integrated Sensing and Communication Transmissions
<|reference_start|>Compressed Sensing Inspired User Acquisition for Downlink Integrated Sensing and Communication Transmissions: This paper investigates radar-assisted user acquisition for downlink multi-user multiple-input multiple-output (MIMO) transmission using Orthogonal Frequency Division Multiplexing (OFDM) signals. Specifically, we formulate a concise mathematical model for the user acquisition problem, where each user is characterized by its delay and beamspace response. Therefore, we propose a two-stage method for user acquisition, where the Multiple Signal Classification (MUSIC) algorithm is adopted for delay estimation, and then a least absolute shrinkage and selection operator (LASSO) is applied for estimating the user response in the beamspace. Furthermore, we also provide a comprehensive performance analysis of the considered problem based on the pair-wise error probability (PEP). Particularly, we show that the rank and the geometric mean of non-zero eigenvalues of the squared beamspace difference matrix determines the user acquisition performance. More importantly, we reveal that simultaneously probing multiple beams outperforms concentrating power on a specific beam direction in each time slot under the power constraint, when only limited OFDM symbols are transmitted. Our numerical results confirm our conclusions and also demonstrate a promising acquisition performance of the proposed two-stage method.<|reference_end|>
@article{song2024compressed,
    title={Compressed Sensing Inspired User Acquisition for Downlink Integrated
  Sensing and Communication Transmissions},
    author={Yi Song, Fernando Pedraza, Shuangyang Li, Siyao Li, Han Yu, and
  Giuseppe Caire},
    journal={arXiv preprint arXiv:2407.01336},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01336},
    primaryClass={cs.IT eess.SP math.IT}
}