Coverage and Rate Analysis for Integrated Sensing and Communication Networks
<|reference_start|>Coverage and Rate Analysis for Integrated Sensing and Communication Networks: Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from $1$ $\text{km}^{-2}$ to $10$ $\text{km}^{-2}$ can boost the ISAC coverage rate from $1.4\%$ to $39.8\%$. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.<|reference_end|>
@article{gan2024coverage,
    title={Coverage and Rate Analysis for Integrated Sensing and Communication
  Networks},
    author={Xu Gan, Chongwen Huang, Zhaohui Yang, Xiaoming Chen, Jiguang He,
  Zhaoyang Zhang, Chau Yuen, Yong Liang Guan, M'erouane Debbah},
    journal={arXiv preprint arXiv:2403.08343},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08343},
    primaryClass={cs.IT eess.SP math.IT}
}