Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication

LIU Ting; WANG Yuan; XIN Yuanxue

doi:10.11999/JEIT240584

Volume 46 Issue 10

Oct. 2024

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LIU Ting, WANG Yuan, XIN Yuanxue. Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4002-4008. doi: 10.11999/JEIT240584

Citation:

LIU Ting, WANG Yuan, XIN Yuanxue. Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4002-4008. doi: 10.11999/JEIT240584

Citation:

LIU Ting, WANG Yuan, XIN Yuanxue. Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4002-4008. doi: 10.11999/JEIT240584

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Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication

doi: 10.11999/JEIT240584

1.
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
3.
College of Information Science and Engineering, Hohai University, Changzhou 213200, China

Funds: The National Natural Science Foundation of China (62101274), The Natural Science Foundation of Jiangsu Province (BK20210640)

Received Date: 2024-07-09
Rev Recd Date: 2024-09-14

Available Online: 2024-09-24

Publish Date: 2024-10-30

Abstract

Abstract

Massive Machine-Type Communication (mMTC) is one of the typical scenarios of the fifth-generation mobile communications systems, and nearly one million devices per square kilometer can be connected under this circumstance. The Reconfigurable Intelligent Surface (RIS) is applied for the grant-free uplink transmission due to the complexity of the propagation environment in the scenario of massive connectivity. Then, the cascaded channel, i.e., the channel link between devices and the RIS, as well as the channel link between the RIS and the Base Station (BS), is formed. Consequently, the quality of the wireless signal transmission can be controlled effectively. On this basis, a denoising learning system is designed using the principle of turbo decoding message passing. The RIS-aided cascaded CSI is learned and estimated through a large number of training data. In addition, the statistical analysis of the RIS-assisted mMTC channel estimation is performed to verify the accuracy of the proposed scheme. Numerical simulation results and theoretical analyses show that the proposed technique is superior to other compressed-sensing-type methods.
- Massive Machine-Type Communication (mMTC),
- Grant-free access,
- Reconfigurable Intelligent Surface (RIS),
- Deep Learning (DL),
- Channel estimation

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References(19)

References

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