Papers
Topics
Authors
Recent
Search
2000 character limit reached

Swin Transformer-Based CSI Feedback for Massive MIMO

Published 12 Jan 2024 in cs.IT, eess.SP, and math.IT | (2401.06435v1)

Abstract: For massive multiple-input multiple-output systems in the frequency division duplex (FDD) mode, accurate downlink channel state information (CSI) is required at the base station (BS). However, the increasing number of transmit antennas aggravates the feedback overhead of CSI. Recently, deep learning (DL) has shown considerable potential to reduce CSI feedback overhead. In this paper, we propose a Swin Transformer-based autoencoder network called SwinCFNet for the CSI feedback task. In particular, the proposed method can effectively capture the long-range dependence information of CSI. Moreover, we explore the impact of the number of Swin Transformer blocks and the dimension of feature channels on the performance of SwinCFNet. Experimental results show that SwinCFNet significantly outperforms other DL-based methods with comparable model sizes, especially for the outdoor scenario.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (22)
  1. L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,” IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 742–758, Jun. 2014.
  2. Y. Ma, Z. Yuan, W. Li, and Z. Li, “Truly grant-free technologies and protocols for 6G,” ZTE Commun., vol. 19, no. 4, pp. 105–110, Dec. 2022.
  3. F. Zhu, “Next generation semantic and spatial joint perception—neural metric-semantic understanding,” ZTE Commun., vol. 19, no. 1, pp. 61–71, Mar. 2021.
  4. Z. Qin, J. Fan, Y. Liu, Y. Gao, and G. Y. Li, “Sparse representation for wireless communications: A compressive sensing approach,” IEEE Signal Process. Mag., vol. 35, no. 3, pp. 40–58, May 2018.
  5. Z. Qin, H. Ye, G. Y. Li, and B.-H. F. Juang, “Deep learning in physical layer communications,” IEEE Wireless Commun., vol. 26, no. 2, pp. 93–99, Mar. 2019.
  6. J. Xu, T.-Y. Tung, B. Ai, W. Chen, Y. Sun, and D. Gündüz, “Deep Joint Source-Channel Coding for Semantic Communications,” IEEE Wireless Commun., 2023.
  7. C.-K. Wen, W.-T. Shih, and S. Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 748–751, Oct. 2018.
  8. Q. Cai, C. Dong, and K. Niu, “Attention model for massive MIMO CSI compression feedback and recovery,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Apr. 2019, pp. 1–5.
  9. Q. Li, A. Zhang, P. Liu, J. Li, and C. Li, “A novel CSI feedback approach for massive MIMO using LSTM-attention CNN,” IEEE Access, vol. 8, pp. 7295–7302, 2020.
  10. Z. Lu, J. Wang, and J. Song, “Multi-resolution CSI feedback with deep learning in massive MIMO system,” in Proc. IEEE Int. Conf. Commun. (ICC), Jun. 2020, pp. 1–6.
  11. X. Yu, X. Li, H. Wu, and Y. Bai, “DS-NLCsiNet: Exploiting non-local neural networks for massive MIMO CSI feedback,” IEEE Commun. Lett., vol. 24, no. 12, pp. 2790–2794, Dec. 2020.
  12. S. Ji and M. Li, “CLNet: Complex input lightweight neural network designed for massive MIMO CSI feedback,” IEEE Wireless Commun. Lett., vol. 10, no. 10, pp. 2318–2322, Oct. 2021.
  13. Y. Xu, M. Zhao, S. Zhang, and H. Jin, “DFECsiNet: Exploiting diverse channel features for massive MIMO CSI feedback,” in Proc. 13th Int. Conf. Wireless Commun. Signal Process. (WCSP), Oct. 2021, pp. 1–5.
  14. J. Xu, B. Ai, N. Wang, and W. Chen, “Deep joint source-channel coding for CSI feedback: An end-to-end approach,” IEEE J. Sel. Areas Commun., vol. 41, no. 1, pp. 260–273, Nov. 2022.
  15. W. Chen, W. Wan, S. Wang, P. Sun, and B. Ai, “CSI-PPPNet: A One-Sided Deep Learning Framework for Massive MIMO CSI Feedback,” arXiv preprint arXiv:2211.15851, 2022.
  16. W. Wan, W. Chen, S. Wang, G. Y. Li, and B. Ai, “Deep Plug-and-Play Prior for Massive MIMO Systems,” arXiv preprint arXiv:2308.04728, 2023.
  17. Y. Cui, A. Guo, and C. Song, “TransNet: Full attention network for CSI feedback in FDD massive MIMO system,” IEEE Wireless Commun. Lett., vol. 11, no. 5, pp. 903–907, May 2022.
  18. A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv:2010.11929, 2020.
  19. Z. Liu et al., “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2021, pp. 10 012–10 022.
  20. K. Yang, S. Wang, J. Dai, K. Tan, K. Niu, and P. Zhang, “WITT: A wireless image transmission transformer for semantic communications,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), Jun. 2023, pp. 1–5.
  21. J. Xu, B. Ai, W. Chen, A. Yang, P. Sun, and M. Rodrigues, “Wireless image transmission using deep source channel coding with attention modules,” IEEE Trans. Circuits Syst. Video Technol., vol. 32, no. 4, pp. 2315–2328, Apr. 2021.
  22. L. Liu et al., “The COST 2100 MIMO channel model,” IEEE Wireless Commun., vol. 19, no. 6, pp. 92–99, Dec. 2012.
Citations (3)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.

Sign Up for Free