Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
arXiv:1911.07202 · doi:10.1109/LSP.2020.2998357
Abstract
In this paper, 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.
arXiv commentAccepted by IEEE Signal Processing Letters
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- Intelligent Reflecting Surface Assisted Multi-User OFDMA: Channel Estimation and Training Design
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- IRS-Aided SWIPT: Joint Waveform, Active and Passive Beamforming Design Under Nonlinear Harvester Model
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- Leveraging Location Information for RIS-aided mmWave MIMO Communications
- Bayesian Channel Estimation for Intelligent Reflecting Surface-Aided mmWave Massive MIMO Systems With Semi-Passive Elements
- Robust Transmission Design for RIS-Assisted Integrated Sensing and Communication Systems
- Plug-In RIS: A Novel Approach to Fully Passive Reconfigurable Intelligent Surfaces
- One-Bit Channel Estimation for IRS-aided Millimeter-Wave Massive MU-MISO System
- Cluster Index Modulation for Reconfigurable Intelligent Surface-Assisted mmWave Massive MIMO
- Beamforming Design with Partial Channel Estimation and Feedback for FDD RIS-Assisted Systems
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