Area Restoration of Channel Impulse Response With Time Decomposition Based Super-Resolution Method
Authors: 
Shuchen Wang, Suixiang Gao, Wenguo Yang, Tian-Hong Loh, Yang Yang, Fei QinAuthors Info & Claims
Pages 9687 - 9700
Abstract
With the application and development of the fifth-generation (5G) communications, it is essential to gain insight understanding of the multi-antenna wireless channel characterizations. In particular, their relevant Channel Impulse Response (CIR) so to ensure the effective design of algorithms and systems. However, both the traditional channel measurement and modelling are essentially based on assessment at discretely sampled spatial points without the capability to obtain the relevant channel information over a given surrounding area. To overcome this limitation, this paper proposes to re-assemble the discretely sampled CIRs into equalized video streams. With this basis, a deep learning based video super-resolution method, namely, the Time Decomposition Video Super-Resolution (TDVSR), has been proposed to restore the area channel information for the first time. Moreover, a time decomposition module based on Bidirectional Long Short-Term Memory (BiLSTM) has been designed to decompose the re-assembled CIRs into video form in the time dimension. A retrained video super-resolution model will then process the composited data and output high-resolution frames, which will be reversed to the CIRs at the dense density target area. A data set with various typical fading scenarios has been constructed by Ray Tracing (RT) method. Extensive experiments demonstrate that the proposed TDVSR model successfully learned the nonlinear propagation laws through the data-driven method, which shows satisfied restoration accuracy with significantly increased computation efficiency.
References
[1]
E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, “Massive MIMO for next generation wireless systems,” IEEE Commun. Mag., vol. 52, no. 2, pp. 186–195, Feb. 2014.
[2]
T. H. Loh, Metrology for 5G and Emerging Wireless Technologies. London, U.K.: IET (The Institution of Engineering and Technology), 2022.
[3]
C.-X. Wang, X. Hong, X. Ge, X. Cheng, G. Zhang, and J. Thompson, “Cooperative MIMO channel models: A survey,” IEEE Commun. Mag., vol. 48, no. 2, pp. 80–87, Feb. 2010.
[4]
C. Wen, W. Shih, and S. Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 748–751, Oct. 2018.
[5]
M. Yang, C. Bian, and H. Kim, “OFDM-guided deep joint source channel coding for wireless multipath fading channels,” IEEE Trans. Cognit. Commun. Netw., vol. 8, no. 2, pp. 584–599, Jun. 2022.
[6]
Y. Yanget al., “Parallel channel sounder for MIMO channel measurements,” IEEE Wireless Commun., vol. 25, no. 5, pp. 16–22, Oct. 2018.
[7]
D. R. Smith and D. Schurig, “Electromagnetic wave propagation in media with indefinite permittivity and permeability tensors,” Phys. Rev. Lett., vol. 90, no. 7, Feb. 2003, Art. no.
[8]
Q. Zhang, T. H. Loh, Z. Huang, and F. Qin, “An investigation into the spatial heterogeneity of MIMO device with 3D deterministic channel modelling using single-probe anechoic chamber method,” in Proc. 17th Eur. Conf. Antennas Propag. (EuCAP), Mar. 2023, pp. 1–5.
[9]
L. Arnaut, T. Loh, and B. Peters, “Statistical experimental characterisation of indoor radio wave propagation in an office environment,” Eur. Cooperation Sci. Technol. Assoc. (COST), Brussels, Belgium, Tech. Rep. COST 2100 TD(08)403, 2008.
[10]
S. Wang, S. Gao, F. Qin, and W. Yang, “A decomposition-based analysis of wireless multipath channel with full-wave simulation,” IEEE Antennas Wireless Propag. Lett., vol. 21, pp. 1615–1619, 2022.
[11]
A. M. Al-Samman, M. H. Azmi, T. A. Rahman, I. Khan, M. N. Hindia, and A. Fattouh, “Window-based channel impulse response prediction for time-varying ultra-wideband channels,” PLoS ONE, vol. 11, no. 12, Dec. 2016, Art. no.
[12]
S. R. Pérezet al., “Reflection model for calculation of the impulse response on IR-wireless indoor channels using ray-tracing algorithm,” Microw. Opt. Technol. Lett., vol. 32, no. 4, pp. 296–300, Feb. 2002.
[13]
H. Liuet al., “Video super-resolution based on deep learning: A comprehensive survey,” Artif. Intell. Rev., vol. 55, no. 8, pp. 5981–6035, Dec. 2022.
[14]
Y. Zeng and T. S. Ng, “Pilot cyclic prefixed single carrier communication: Channel estimation and equalization,” IEEE Signal Process. Lett., vol. 12, no. 1, pp. 56–59, Jan. 2005.
[15]
Y. Y. Jin, W.-S. Soh, and W.-C. Wong, “Indoor localization with channel impulse response based fingerprint and nonparametric regression,” IEEE Trans. Wireless Commun., vol. 9, no. 3, pp. 1120–1127, Mar. 2010.
[16]
A. Niitsoo, T. Edelhäußer, E. Eberlein, N. Hadaschik, and C. Mutschler, “A deep learning approach to position estimation from channel impulse responses,” Sensors, vol. 19, no. 5, p. 1064, Mar. 2019.
[17]
S. Wang, S. Gao, and W. Yang, “Pilot pattern design with branch and bound in PSA-OFDM system,” in Proc. Algorithmic Aspects Inf. Manag., 16th Int. Conf. AAIM, Guangzhou, China, Cham, Switzerland: Springer, Aug. 2022, pp. 244–254.
[18]
Y. Li, Y. Wang, Y. Chen, Z. Yu, and C. Han, “Channel measurement and analysis in an indoor corridor scenario at 300 GHz,” in Proc. IEEE Int. Conf. Commun., May 2022, pp. 2888–2893.
[19]
F. Hsieh and M. Rybakowski, “Propagation model for high altitude platform systems based on ray tracing simulation,” in Proc. 13th Eur. Conf. Antennas Propag. (EuCAP), Mar. 2019, pp. 1–5.
[20]
S. Kasdorf, B. Troksa, C. Key, J. Harmon, and B. M. Notaroš, “Advancing accuracy of shooting and bouncing rays method for ray-tracing propagation modeling based on novel approaches to ray cone angle calculation,” IEEE Trans. Antennas Propag., vol. 69, no. 8, pp. 4808–4815, Aug. 2021.
[21]
Z. Yun and M. F. Iskander, “Ray tracing for radio propagation modeling: Principles and applications,” IEEE Access, vol. 3, pp. 1089–1100, 2015.
[22]
S. Piltyay, A. Bulashenko, Y. Herhil, and O. Bulashenko, “FDTD and FEM simulation of microwave waveguide polarizers,” in Proc. IEEE 2nd Int. Conf. Adv. Trends Inf. Theory (ATIT), Nov. 2020, pp. 357–363.
[23]
J. Li, L. Zhang, and Q.-H. Qin, “A regularized method of moments for three-dimensional time-harmonic electromagnetic scattering,” Appl. Math. Lett., vol. 112, Feb. 2021, Art. no.
[24]
S. K. Ezzulddin, S. O. Hasan, and M. M. Ameen, “Microstrip patch antenna design, simulation and fabrication for 5G applications,” Simul. Model. Pract. Theory, vol. 116, Apr. 2022, Art. no.
[25]
A. Voulodimos, “Deep learning for computer vision: A brief review,” Comput. Intell. Neurosci., vol. 2018, pp. 1–13, Feb. 2018, Art. no.
[26]
M. Soltani, V. Pourahmadi, A. Mirzaei, and H. Sheikhzadeh, “Deep learning-based channel estimation,” IEEE Commun. Lett., vol. 23, no. 4, pp. 652–655, Apr. 2019.
[27]
M. Soltani, V. Pourahmadi, and H. Sheikhzadeh, “Pilot pattern design for deep learning-based channel estimation in OFDM systems,” IEEE Wireless Commun. Lett., vol. 9, no. 12, pp. 2173–2176, Dec. 2020.
[28]
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295–307, Feb. 2015.
[29]
J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 1646–1654.
[30]
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image restoration,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 7, pp. 2480–2495, Jul. 2020.
[31]
C. Lediget al., “Photo-realistic single image super-resolution using a generative adversarial network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 4681–4690.
[32]
X. Wanget al., “ESRGAN: Enhanced super-resolution generative adversarial networks,” in Proc. Eur. Conf. Comput. Vis. (ECCV) Workshops, pp. 63–79, 2018.
[33]
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), Jul. 2017, pp. 136–144.
[34]
T. Xue, B. Chen, J. Wu, D. Wei, and W. T. Freeman, “Video enhancement with task-oriented flow,” Int. J. Comput. Vis., vol. 127, no. 8, pp. 1106–1125, Aug. 2019.
[35]
Y. Tian, Y. Zhang, Y. Fu, and C. Xu, “TDAN: Temporally-deformable alignment network for video super-resolution,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2020, pp. 3360–3369.
[36]
X. Wang, K. C. K. Chan, K. Yu, C. Dong, and C. C. Loy, “EDVR: Video restoration with enhanced deformable convolutional networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), Jun. 2019, pp. 1954–1963.
[37]
K. C. K. Chan, X. Wang, K. Yu, C. Dong, and C. C. Loy, “BasicVSR: The search for essential components in video super-resolution and beyond,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2021, pp. 4947–4956.
[38]
K. C. K. Chan, S. Zhou, X. Xu, and C. C. Loy, “BasicVSR++: Improving video super-resolution with enhanced propagation and alignment,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2022, pp. 5972–5981.
[39]
K. C. K. Chan, S. Zhou, X. Xu, and C. C. Loy, “Investigating tradeoffs in real-world video super-resolution,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2022, pp. 5952–5961.
[40]
A. Ranjan and M. J. Black, “Optical flow estimation using a spatial pyramid network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jul. 2017, pp. 4161–4170.
[41]
G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, Jan. 2003.
[42]
M. Ali and R. Prasad, “Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition,” Renew. Sustain. Energy Rev., vol. 104, pp. 281–295, Apr. 2019.
[43]
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
[44]
K. Choet al., “Learning phrase representations using RNN encoder–decoder for statistical machine translation,” 2014, arXiv:1406.1078.
[45]
S. Ansari, H. Du, and F. Naghdy, “Driver’s foot trajectory tracking for safe maneuverability using new modified reLU-BiLSTM deep neural network,” in Proc. IEEE Int. Conf. Syst., Man, Cybern. (SMC), Oct. 2020, pp. 4392–4397.
[46]
S. Bai, J. Zico Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” 2018, arXiv:1803.01271.
[47]
Q. Wenet al., “Transformers in time series: A survey,” 2022, arXiv:2202.07125.
[48]
Openmmlab. Accessed: May 6, 2023. [Online]. Available: https://openmmlab.com/
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