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Near-Optimal LOS and Orientation Aware Intelligent Reflecting Surface Placement
Signal Processing · Submitted 5 May 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We prove the submodularity of the objective function which establishes near-optimal performance bounds for the algorithm.
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The abstract, sentence by sentence
Due to their passive nature and thus low energy consumption, intelligent reflecting surfaces (IRSs) have shown promise as means of extending coverage as a proxy for connection reliability. The relative locations of the base station (BS), IRS, and user equipment (UE) determine the extent of the coverage that the IRS provides which demonstrates the importance of the IRS placement problem. More specifically, locations, which determine whether BS-IRS and IRS-UE line of sight (LOS) links exist, and surface orientation, which determines whether the BS and UE are within the field of view (FoV) of the surface, play crucial roles in the quality of provided coverage. Moreover, another challenge is high computational complexity, since the IRS placement problem is a combinatorial optimization, and is NP-hard. Identifying the orientation of the surface and LOS channel as two crucial factors, we propose an efficient IRS placement algorithm that takes these two characteristics into account in order to maximize the network coverage. We prove the submodularity of the objective function which establishes near-optimal performance bounds for the algorithm. Simulation results demonstrate the performance of the proposed algorithm in a real environment.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Due to their passive nature and thus low energy consumption, intelligent reflecting surfaces (IRSs) have shown promise as means of extending coverage as a proxy for connection reliability. | background 1.00 | 0.06 | 0.03 | asserted 0.78 |
| s1 | The relative locations of the base station (BS), IRS, and user equipment (UE) determine the extent of the coverage that the IRS provides which demonstrates the importance of the IRS placement problem. | background 1.00 | 0.13 | 0.04 | asserted 1.00 |
| s2 | More specifically, locations, which determine whether BS-IRS and IRS-UE line of sight (LOS) links exist, and surface orientation, which determines whether the BS and UE are within the field of view (FoV) of the surface, play crucial roles in the quality of provided coverage. | background 1.00 | 0.12 | 0.04 | asserted 1.00 |
| s3 | Moreover, another challenge is high computational complexity, since the IRS placement problem is a combinatorial optimization, and is NP-hard. | background 0.85 | 0.13 | 0.02 | asserted 1.00 |
| s4 | Identifying the orientation of the surface and LOS channel as two crucial factors, we propose an efficient IRS placement algorithm that takes these two characteristics into account in order to maximize the network coverage. | contribution 0.85 | 0.95 | 0.35 | asserted 1.00 |
| s5 | We prove the submodularity of the objective function which establishes near-optimal performance bounds for the algorithm. | contribution 0.69 | 0.95 | 0.39 | asserted 1.00 |
| s6 | Simulation results demonstrate the performance of the proposed algorithm in a real environment. | result 0.96 | 0.94 | 0.18 | asserted 1.00 |
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The paper
Original abstract
Due to their passive nature and thus low energy consumption, intelligent reflecting surfaces (IRSs) have shown promise as means of extending coverage as a proxy for connection reliability. The relative locations of the base station (BS), IRS, and user equipment (UE) determine the extent of the coverage that the IRS provides which demonstrates the importance of the IRS placement problem. More specifically, locations, which determine whether BS-IRS and IRS-UE line of sight (LOS) links exist, and surface orientation, which determines whether the BS and UE are within the field of view (FoV) of the surface, play crucial roles in the quality of provided coverage. Moreover, another challenge is high computational complexity, since the IRS placement problem is a combinatorial optimization, and is NP-hard. Identifying the orientation of the surface and LOS channel as two crucial factors, we propose an efficient IRS placement algorithm that takes these two characteristics into account in order to maximize the network coverage. We prove the submodularity of the objective function which establishes near-optimal performance bounds for the algorithm. Simulation results demonstrate the performance of the proposed algorithm in a real environment.
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How the literature uses it
3 arXiv papers cite this in 3 sentences
What it cites
Cites as background 1 paper
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The IRS placement problem has recently become of high interest, e.g., [ref] .
Reconfigurable Intelligent Surface Optimal Placement in Millimeter-Wave Networks · Related Works · 0.96
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