Learning-based Block-wise Planar Channel Estimation for Time-Varying MIMO OFDM (2405.11218v1)
Abstract: In this paper, we propose a learning-based block-wise planar channel estimator (LBPCE) with high accuracy and low complexity to estimate the time-varying frequency-selective channel of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. First, we establish a block-wise planar channel model (BPCM) to characterize the correlation of the channel across subcarriers and OFDM symbols. Specifically, adjacent subcarriers and OFDM symbols are divided into several sub-blocks, and an affine function (i.e., a plane) with only three variables (namely, mean, time-domain slope, and frequency-domain slope) is used to approximate the channel in each sub-block, which significantly reduces the number of variables to be determined in channel estimation. Second, we design a 3D dilated residual convolutional network (3D-DRCN) that leverages the time-frequency-space-domain correlations of the channel to further improve the channel estimates of each user. Numerical results demonstrate that the proposed significantly outperforms the state-of-the-art estimators and maintains a relatively low computational complexity.
- C.-X. Wang et al., “On the road to 6G: Visions, requirements, key technologies and testbeds,” IEEE Commun. Surveys Tuts., 2023.
- W. Chen et al., “5G-advanced toward 6G: Past, present, and future,” IEEE J. Sel. Areas Commun., vol. 41, no. 6, pp. 1592–1619, 2023.
- R. Chataut and R. Akl, “Massive MIMO systems for 5G and beyond networks—overview, recent trends, challenges, and future research direction,” Sensors, vol. 20, no. 10, p. 2753, 2020.
- H. J. Damsgaard, A. Ometov, M. M. Mowla, A. Flizikowski, and J. Nurmi, “Approximate computing in B5G and 6G wireless systems: A survey and future outlook,” Comput. Networks, p. 109872, 2023.
- X. Ma and Z. Gao, “Data-driven deep learning to design pilot and channel estimator for massive MIMO,” IEEE Trans. Veh. Technol., vol. 69, no. 5, pp. 5677–5682, 2020.
- M. B. Mashhadi and D. Gündüz, “Pruning the pilots: Deep learning-based pilot design and channel estimation for MIMO-OFDM systems,” IEEE Trans. Wireless Commun., vol. 20, no. 10, pp. 6315–6328, 2021.
- P. Dong, H. Zhang, G. Y. Li, I. S. Gaspar, and N. NaderiAlizadeh, “Deep CNN-based channel estimation for mmWave massive MIMO systems,” IEEE J. Sel. Topics Signal Process., vol. 13, no. 5, pp. 989–1000, 2019.
- Y. Liao, Y. Hua, and Y. Cai, “Deep learning based channel estimation algorithm for fast time-varying MIMO-OFDM systems,” IEEE Commun. Lett., vol. 24, no. 3, pp. 572–576, 2019.
- M. Soltani, V. Pourahmadi et al., “Deep learning-based channel estimation,” IEEE Commun. Lett., vol. 23, no. 4, pp. 652–655, 2019.
- A. Melgar et al., “Deep neural network: an alternative to traditional channel estimators in massive MIMO systems,” IEEE Trans. Cognit. Commun. Networking, vol. 8, no. 2, pp. 657–671, 2022.
- Y. Sun, H. Shen, Z. Du, L. Peng, and C. Zhao, “ICINet: ICI-aware neural network based channel estimation for rapidly time-varying OFDM systems,” IEEE Commun. Lett., vol. 25, no. 9, pp. 2973–2977, 2021.
- J. Li and Q. Peng, “Lightweight channel estimation networks for OFDM systems,” IEEE Wireless Commun. Lett., vol. 11, no. 10, pp. 2066–2070, 2022.
- H. He, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmWave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, 2018.
- Z. Huang, W. Jiang, X. Yuan, L. Wang, and Y. Zuo, “Learning-based turbo message passing for channel estimation in rich-scattering MIMO-OFDM,” China Commun., 2023, accepted.
- W. U. Bajwa, J. Haupt, A. M. Sayeed, and R. Nowak, “Compressed channel sensing: A new approach to estimating sparse multipath channels,” Proc. IEEE, vol. 98, no. 6, pp. 1058–1076, 2010.
- 3rd Generation Partnership Project (3GPP), “Study on channel model for frequencies from 0.5 to 100 GHz,” 3GPP, Tech. Rep. 3GPP TR 38.901 V17.0.0, 2023.
- W. Jiang, M. Yue, X. Yuan, and Y. Zuo, “Massive connectivity over MIMO-OFDM: Joint activity detection and channel estimation with frequency selectivity compensation,” IEEE Trans. Wireless Commun., vol. 21, no. 9, pp. 6920–6934, 2022.
- B. Marinberg, A. Cohen, E. Ben-Dror et al., “A study on MIMO channel estimation by 2D and 3D convolutional neural networks,” in IEEE Int. Conf. Adv. Netw. Telecommun. Syst. (ANTS), 2020, pp. 1–6.
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