Bidirectional position attention lightweight network for massive MIMO CSI feedback

doi:10.19682/j.cnki.1005-8885.2024.0018

The Journal of China Universities of Posts and Telecommunications ›› 2024, Vol. 31 ›› Issue (5): 1-11.doi: 10.19682/j.cnki.1005-8885.2024.0018

• Wireless • Next Articles

Bidirectional position attention lightweight network for massive MIMO CSI feedback

Received:2023-04-29 Revised:2024-01-07 Online:2024-10-31 Published:2024-10-31
Contact: LI Jun E-mail:rogerjunli@sdu.edu.cn

Abstract

Abstract:

In frequency division duplex ( FDD) massive multiple-input multiple-output ( MIMO) systems, a bidirectional positional attention network ( BPANet) was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information ( CSI) feedback methods. Specifically, a bidirectional position attention module ( BPAM) was designed in the BPANet to improve the network performance. The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information, thereby enhancing the feature representation of the CSI matrix. Furthermore, channel attention is decomposed into two one-dimensional (1D) feature encoding processes effectively reducing computational costs. Simulation results demonstrate that, compared with the existing representative method complex input lightweight neural network ( CLNet), BPANet reduces computational complexity by an average of 19. 4% and improves accuracy by an average of 7. 1% . Additionally, it performs better in terms of running time delay and cosine similarity.

Key words: massive multiple-input multiple-output ( MIMO), channel state information ( CSI) feedback, deep learning, lightweight neural network, bidirectional position attention module ( BPAM)

CLC Number:

TN393

References

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Bidirectional position attention lightweight network for massive MIMO CSI feedback

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