Power Allocation for Cell-Free Massive MIMO ISAC Systems with OTFS Signal
<|reference_start|>Power Allocation for Cell-Free Massive MIMO ISAC Systems with OTFS Signal: Applying integrated sensing and communication (ISAC) to a cell-free massive multiple-input multiple-output (CF mMIMO) architecture has attracted increasing attention. This approach equips CF mMIMO networks with sensing capabilities and resolves the problem of unreliable service at cell edges in conventional cellular networks. However, existing studies on CF-ISAC systems have focused on the application of traditional integrated signals. To address this limitation, this study explores the employment of the orthogonal time frequency space (OTFS) signal as a representative of innovative signals in the CF-ISAC system, and the system's overall performance is optimized and evaluated. A universal downlink spectral efficiency (SE) expression is derived regarding multi-antenna access points (APs) and optional sensing beams. To streamline the analysis and optimization of the CF-ISAC system with the OTFS signal, we introduce a lower bound on the achievable SE that is applicable to OTFS-signal-based systems. Based on this, a power allocation algorithm is proposed to maximize the minimum communication signal-to-interference-plus-noise ratio (SINR) of users while guaranteeing a specified sensing SINR value and meeting the per-AP power constraints. The results demonstrate the tightness of the proposed lower bound and the efficiency of the proposed algorithm. Finally, the superiority of using the OTFS signals is verified by a 13-fold expansion of the SE performance gap over the application of orthogonal frequency division multiplexing signals. These findings could guide the future deployment of the CF-ISAC systems, particularly in the field of millimeter waves with a large bandwidth.<|reference_end|>
@article{fan2024power,
    title={Power Allocation for Cell-Free Massive MIMO ISAC Systems with OTFS
  Signal},
    author={Yifei Fan, Shaochuan Wu, Xixi Bi, Guoyu Li},
    journal={arXiv preprint arXiv:2405.20073},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20073},
    primaryClass={cs.IT eess.SP math.IT}
}