Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems
Abstract
:1. Introduction
- We leverage the superiority in lowering the pilot overhead of the semi-blind CE method. In contrast to conventional pilot-assisted methods, our receiver involves symbol matrix estimation which could lighten the reliance on pilot training.
- We prove that the signal received at the BS adheres to a double parallel factor (PARAFAC) tensor model. The dimension of the received signals can be reduced by leveraging tensor decomposition, resulting in accelerating the estimation speed.
- We propose a novel ALS-based semi-blind receiver for the RIS-aided MIMO communication system. Based on the unfolded forms generated by tensor decomposition, we formulate the joint CE and symbol estimation problem into LS problems and then solve them by ALS.
2. Preliminaries on the Parallel Factor Tensor Decomposition
3. System Model
4. Proposed Semi-Blind Receiver
4.1. Symbol Estimation Stage
4.2. Channel Estimation Stage
Algorithm 1: Proposed double-PARAFAC-based Alternating Least Squares. |
Input: Initialize . 1: Symbol Estimation Stage 1.1: ; 1.2: While do 1.3: ; 1.4: Calculate by (16); 1.5: Calculate by (17); 1.6: End While 2: Channel Estimation Stage 2.1: ; Reconstruct receive signals by (18); 2.2: While do 2.3: ; 2.4: Calculate by (25); 2.5: Calculate by (26); 2.6: End While . |
4.3. Computational Complexity
4.4. Feasibility Analysis
5. Simulations and Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Notation | Meaning |
---|---|
M | The number of BS antennas |
L | The number of UT antennas |
N | The number of reflecting elements |
P | The number of time blocks |
T | The number of time slots |
K | Code length |
BS-RIS channel | |
RIS-UT channel | |
Transmitted symbol matrix | |
RIS’s phase shift matrix | |
Encoding matrix | |
Additive white Gaussian noise matrix |
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Li, N.; Deng, H.; Xu, F.; Zheng, Y.; Qu, M.; Fu, W.; Zhou, N. Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems. Sensors 2024, 24, 6625. https://doi.org/10.3390/s24206625
Li N, Deng H, Xu F, Zheng Y, Qu M, Fu W, Zhou N. Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems. Sensors. 2024; 24(20):6625. https://doi.org/10.3390/s24206625
Chicago/Turabian StyleLi, Ni, Honggui Deng, Fuxin Xu, Yitao Zheng, Mingkang Qu, Wanqing Fu, and Nanqing Zhou. 2024. "Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems" Sensors 24, no. 20: 6625. https://doi.org/10.3390/s24206625
APA StyleLi, N., Deng, H., Xu, F., Zheng, Y., Qu, M., Fu, W., & Zhou, N. (2024). Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems. Sensors, 24(20), 6625. https://doi.org/10.3390/s24206625