-
Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave MIMO Systems
Intelligent reflecting surfaces (IRSs) are regarded as promising enabler...
read it
-
Training Signal Design for Sparse Channel Estimation in Intelligent Reflecting Surface-Assisted Millimeter-Wave Communication
In this paper, the problem of training signal design for intelligent ref...
read it
-
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces
Integrating large intelligent reflecting surfaces (IRS) into millimeter-...
read it
-
Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO
Reconfigurable intelligent surface (RIS) is envisioned to be an essentia...
read it
-
An Efficient CSI Acquisition Method for Intelligent Reflecting Surface-assisted mmWave Networks
Millimeter-wave (mmWave) communication is one of the key enablers of the...
read it
-
Intelligent Reflecting Surface Assisted Multi-User MISO Communication
The recently completed global standard for 5G new radio air interface fo...
read it
-
Dictionary Learning for Channel Estimation in Hybrid Frequency-Selective mmWave MIMO Systems
Exploiting channel sparsity at millimeter wave (mmWave) frequencies redu...
read it
Cascaded Channel Estimation for IRS-assisted Mmwave Multi-antenna with Quantized Beamforming
In this letter, we optimize the channel estimator of the cascaded channel in an intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multi-antenna system. In this system, the receiver is equipped with a hybrid architecture adopting quantized beamforming. Different from traditional multiple-input multiple-output (MIMO) systems, the design of channel estimation is challenging since the IRS is usually a passive array with limited signal processing capability. We derive the optimized channel estimator in a closed form by reformulating the problem of cascaded channel estimation in this system, leveraging the typical mean-squared error (MSE) criterion. Considering the presence of possible channel sparsity in mmWave channels, we generalize the proposed method by exploiting the channel sparsity for further performance enhancement and computational complexity reduction. Simulation results verify that the proposed estimator significantly outperforms the existing ones.
READ FULL TEXT
Comments
There are no comments yet.