Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces

By Shicong Liu, Zhen Gao, Jun Zhang, M. Di Renzo, Mohamed-Slim Alouini. IEEE Transactions on Vehicular Technology, 2020

299 citations recorded by the source corpus.

Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput. Most existing work assumes the ideal channel estimation, which can be challenging due to the high-dimensional cascaded MIMO channels and passive reflecting elements. Therefore, this paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead. Specifically, we first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. At the channel training stage, only a small proportion of elements will be successively activated to sound the partial channels. Moreover, the complete channel matrix can be reconstructed from the limited measurements based on compressive sensing, whereby the common sparsity of angular domain mmWave MIMO channels among different subcarriers is leveraged for improved accuracy. Besides, a complex-valued denoising convolution neural network (CV-DnCNN) is further proposed for enhanced performance. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art solutions.

Subfields: Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/32ca63934dfba41794a1e785ba98514f3e2e8c57

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Yuanwei Liu
2. Xidong Mu
3. M. Di Renzo
4. Naofal Al-Dhahir
5. Jiaqi Xu
6. Tianwei Hou
7. Jun Fang
8. Hongbin Li

In Electronic, Optical and Magnetic Materials

1. M. Di Renzo

Citations in the corpus

• Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design (2019, background): “[33]–[36].”
• Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, background): “[111] proposed a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems with a low training overhead.”