Sparse Channel Estimation for IRS-Aided Systems Exploiting 2-D Sparse Arrays | IEEE Conference Publication | IEEE Xplore

Sparse Channel Estimation for IRS-Aided Systems Exploiting 2-D Sparse Arrays

Publisher: IEEE

Abstract:

Intelligent reflecting surface (IRS) is a promising next-generation technology for increasing channel capacity and reducing power consumption. In this paper, we present a...View more

Abstract:

Intelligent reflecting surface (IRS) is a promising next-generation technology for increasing channel capacity and reducing power consumption. In this paper, we present a novel IRS configuration consisting of a small number of active elements in an optimized L-shaped sparse array to separately estimate the channels between the base station and the IRS, and the channel between multiple user equipment and the IRS. Structured matrix completion techniques are used to attain superior direction-of-arrival estimation performance with an increased number of degrees of freedom. The training overhead is minimized in the proposed system and is not directly related to the number of IRS reflecting elements. The proposed sparse array strategy simultaneously resolves multiple sources with a high accuracy and outperforms the L-shaped uniform array counterpart using the same number of active elements. The effectiveness of the proposed strategy is confirmed using simulation results.
Date of Conference: 20-23 June 2022
Date Added to IEEE Xplore: 22 July 2022
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Publisher: IEEE
Conference Location: Trondheim, Norway

References

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