Joint Channel Estimation and Data Rate Maximization for Intelligent Reflecting Surface Assisted Terahertz MIMO Communication Systems

Xinying Ma; Zhi Chen; Wenjie Chen; Zhuoxun Li; Yaojia Chi; Chong Han; Shaoqian Li

doi:10.1109/ACCESS.2020.2994100

IEEE Access (Jan 2020)

Joint Channel Estimation and Data Rate Maximization for Intelligent Reflecting Surface Assisted Terahertz MIMO Communication Systems

Xinying Ma,
Zhi Chen,
Wenjie Chen,
Zhuoxun Li,
Yaojia Chi,
Chong Han,
Shaoqian Li

Affiliations

Xinying Ma: ORCiD; National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Zhi Chen: ORCiD; National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Wenjie Chen: National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Zhuoxun Li: National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Yaojia Chi: National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Chong Han: ORCiD; University of Michigan-Shanghai Jiao Tong University (UM-SJTU) Joint Institute, Shanghai Jiao Tong University, Shanghai, China
Shaoqian Li: National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China

DOI: https://doi.org/10.1109/ACCESS.2020.2994100
Journal volume & issue: Vol. 8
pp. 99565 – 99581

Abstract

Read online

Terahertz (THz) communications recently attract significant attention and become an emerging technology pillar for sixth generation (6G) wireless systems. Due to the serious path attenuation of THz signals, THz communication is applicable for the short-distance indoor scenarios. However, the THz waves are easily blocked by obstacles, leading to a communication interruption. To this end, an intelligent reflecting surface (IRS), which interacts with incident THz waves in a controlled manner by adjusting the discrete phase shifts of the IRS elements, is considered as a promising technology to mitigate blockage vulnerability and enhance coverage capability for indoor scenarios. In light of graphene-enabled hardware structure of an IRS, the IRS-assisted THz multiple-input multiple-output (MIMO) system model is developed. Moreover, an iterative atom pruning based subspace pursuit (IAP-SP) scheme is developed for channel estimation. Compared to the classical subspace pursuit (SP) scheme, the proposed IAP-SP algorithm can substantially reduce the computational complexity while maintaining accurate channel recovery. With the estimated channel, a data rate maximization problem is formulated, which can be converted to a discrete phase shift search problem. The exhaustive search method is firstly proposed to obtain the optimal transmission rate but endure extremely high computational burden. Then, a local search method is proposed to decrease the number of possible discrete phase candidates of IRS while undergoes obvious performance loss. Interestingly, a novel feedforward fully connected structure based deep neural network (DNN) scheme is put forward, which has the ability to learn how to output the optimal phase shift configurations by inputting the features of estimated channel. Simulation results demonstrate that, in contrast with the exhaustive search scheme and the local search scheme, the proposed DNN-based scheme achieves a near-optimal communication rate performance. Meanwhile, the DNN-based scheme enormously alleviates the computational complexity and allows for dynamic parameter adaption in rapid-varying channel conditions.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

About the journal

Abstract

Keywords