Impact of Network Geometry on Large Networks with Intelligent Reflecting Surfaces
<|reference_start|>Impact of Network Geometry on Large Networks with Intelligent Reflecting Surfaces: In wireless networks assisted by intelligent reflecting surfaces (IRSs), jointly modeling the signal received over the direct and indirect (reflected) paths is a difficult problem. In this work, we show that the network geometry (locations of serving base station, IRS, and user) can be captured using the so-called triangle parameter $\Delta$. We introduce a decomposition of the effect of the combined link into a signal amplification factor and an effective channel power coefficient $G$. The amplification factor is monotonically increasing with both the number of IRS elements $N$ and $\Delta$. For $G$, since an exact characterization of the distribution seems unfeasible, we propose three approximations depending on the value of the product $N\Delta$ for Nakagami fading and the special case of Rayleigh fading. For two relevant models of IRS placement, we prove that their performance is identical if $\Delta$ is the same given an $N$. We also show that no gains are achieved from IRS deployment if $N$ and $\Delta$ are both small. We further compute bounds on the diversity gain to quantify the channel hardening effect of IRSs. Hence only with a judicious selection of IRS placement and other network parameters, non-trivial gains can be obtained.<|reference_end|>
@article{ali2024impact,
    title={Impact of Network Geometry on Large Networks with Intelligent Reflecting
  Surfaces},
    author={Konpal Shaukat Ali, Martin Haenggi, Arafat Al-Dweik and Marwa Chafii},
    journal={arXiv preprint arXiv:2405.14976},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14976},
    primaryClass={cs.IT math.IT}
}