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- W4306154356 abstract "Intelligent reflecting surface (IRS) has been materialized as a new physical layer technology to reconfigure and intelligently control the wireless propagation environment. However, for improved system performance in the presence of IRS, knowledge of proper channel state information (CSI) is essential. In this paper, we present a novel and efficient channel estimation technique based on sparse Kalman filter (SKF) in intelligent reflecting surface-supported (IRS) multiple input multiple output (MIMO) millimeter wave (mmWave) wireless communication systems. In addition, we mitigate the training overhead by taking advantage of the sparse attributes and low-rank behavior of the IRS-mmWave MIMO-cascaded channel. Channel effect is evaluated with Rayleigh fading and QPSK as the modulation technique. Simulation results demonstrate that our proposed sparse Kalman filter-based channel estimation method outperforms the conventional methods in the matter of channel estimation accuracy, bit error rate performance, and reduced training overhead." @default.
- W4306154356 created "2022-10-14" @default.
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- W4306154356 date "2022-10-14" @default.
- W4306154356 modified "2023-10-15" @default.
- W4306154356 title "A Novel Channel Estimation Technique for Intelligent Reflecting Surface (IRS)-Aided MmWave Systems Based on Sparse Kalman Filter (SKF)" @default.
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- W4306154356 doi "https://doi.org/10.1007/978-981-19-3035-5_10" @default.
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