Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
By Peilan Wang, Jun Fang, Huiping Duan, Hongbin Li. IEEE Signal Processing Letters, 2020
489 citations recorded by the source corpus.
In this letter, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/1aaaa3cb90662480b96e872ff1f23a684b854fdb
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In Electrical and Electronic Engineering
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Citations in the corpus
- Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial (2020, background): “To tackle this difficulty, certain IRS channel properties (such as low-rank, sparsity, and spatial correlation) can be exploited to facilitate the cascaded channel decomposition as well as reduce the training overhead [91]–[95].”
- Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications (2019, methodology): “8, we can see that the NMSE decreases as SNR becomes large except for the CSbased channel estimation [17].”
- An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems (2021, unknown): “In order to exploit the inherent relationship between the columns of the cascaded array response matrix, the authors of [57] and [58] adopted the CS technique, according to which each array response matrix A ( ω ) is replaced by a dictionary matrix composed of potential steering vectors.”
- Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces (2020, background): “Considering the sparse representation of the cascaded channel, the authors in [9] proposed a compressive sensing (CS) based channel estimation method, which can estimate the cascaded channel simultaneously with substantial training overhead reduction.”