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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.14976 |
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| _version_ | 1866929357099892736 |
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| author | Ali, Konpal Shaukat Haenggi, Martin Al-Dweik, Arafat Chafii, Marwa |
| author_facet | Ali, Konpal Shaukat Haenggi, Martin Al-Dweik, Arafat Chafii, Marwa |
| contents | 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 $Δ$. 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 $Δ$. For $G$, since an exact characterization of the distribution seems unfeasible, we propose three approximations depending on the value of the product $NΔ$ 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 $Δ$ is the same given an $N$. We also show that no gains are achieved from IRS deployment if $N$ and $Δ$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14976 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Impact of Network Geometry on Large Networks with Intelligent Reflecting Surfaces Ali, Konpal Shaukat Haenggi, Martin Al-Dweik, Arafat Chafii, Marwa Information Theory 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 $Δ$. 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 $Δ$. For $G$, since an exact characterization of the distribution seems unfeasible, we propose three approximations depending on the value of the product $NΔ$ 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 $Δ$ is the same given an $N$. We also show that no gains are achieved from IRS deployment if $N$ and $Δ$ 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. |
| title | Impact of Network Geometry on Large Networks with Intelligent Reflecting Surfaces |
| topic | Information Theory |
| url | https://arxiv.org/abs/2405.14976 |