Full Channel Estimation for IRS-assisted Millimeter-wave Mobile Communication Systems Based on Fixed Point Deep Learning

CHU Hongyun; PAN Xue; HUANG Hang; ZHENG Ling; YANG Mengyao; XIAO Ge

doi:10.11999/JEIT230692

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CHU Hongyun, PAN Xue, HUANG Hang, ZHENG Ling, YANG Mengyao, XIAO Ge. Full Channel Estimation for IRS-assisted Millimeter-wave Mobile Communication Systems Based on Fixed Point Deep Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230692

Citation:

CHU Hongyun, PAN Xue, HUANG Hang, ZHENG Ling, YANG Mengyao, XIAO Ge. Full Channel Estimation for IRS-assisted Millimeter-wave Mobile Communication Systems Based on Fixed Point Deep Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230692

Citation:

CHU Hongyun, PAN Xue, HUANG Hang, ZHENG Ling, YANG Mengyao, XIAO Ge. Full Channel Estimation for IRS-assisted Millimeter-wave Mobile Communication Systems Based on Fixed Point Deep Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230692

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Full Channel Estimation for IRS-assisted Millimeter-wave Mobile Communication Systems Based on Fixed Point Deep Learning

doi: 10.11999/JEIT230692

1.
Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Nanjing Research Institute of Electronic Equipment, Nanjing 210013, China

Funds: The National Natural Science Foundation of China (62102314), The 173 Program for Technology (2022-JCJQ-JJ-0730), The Natural Science Foundation of Shaanxi Province (2022JQ-635)

Received Date: 2023-07-12
Rev Recd Date: 2023-12-27

Available Online: 2024-02-21

Abstract

Abstract

Combining Intelligent Reflective Surface (IRS) with massive MIMO can guarantee and improve the performance of millimeter-wave communication systems. An adaptive full-channel estimation method is proposed for the Base Station (BS)-user direct-connect channel and user-IRS-BS reflective channel mixing scenario. First, auxiliary variables are introduced and atomic paradigms are used to correlate the sparse angle-domain subspaces of the direct-connect and reflective channels; then, the full-channel estimation problem is modeled as a continuous angle-domain sparse matrix reconstruction planning by using atomic paradigm minimization; and finally, a low-complexity problem solving algorithm based on the immovable-point deep learning network is designed. The algorithm can not only overcome the dependence of the nonlinear estimation operator on a priori knowledge in the traditional model-based solution method but also adaptively adjust the complexity of the algorithm according to the changes of the mobile scene. Simulation results show that the proposed algorithm can avoid the error propagation effect caused by the traditional time-division estimation strategy, and has higher estimation accuracy and lower complexity.

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References(21)

References

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