Generative Adversarial Networks for Propagation Channel Modeling

Document Type : Research Article

Authors

Amirkabir University of Technology

Abstract

Channel is one of the most important parts of a communication system as the medium of the propagation of electromagnetic waves. Being aware of how the channel affects the propagation waves is essential for the design, optimization, and performance analysis of a communication system. Along with conventional modeling schemes, in this paper, we present a novel propagation channel model. The proposed modeling strategy considers the 2-dimensional time-frequency response of the channel as an image. It models the distribution of these channel images using Deep Convolutional Generative Adversarial Networks (DCGANs). In addition, for the measurements with different user speeds, the user speed is considered as an auxiliary parameter for the model. StarGAN is used as an image-to-image translation technique to change the generated channel images with respect to the desired user speed. The performance of the proposed model is evaluated using a few existing evaluation metrics. Furthermore, as modeling the 2D time- frequency response is more general than the modeling of the channel only in time, the conventional metrics for evaluation of the channel models are not sufficient; therefore, a new metric is introduced in this paper. This metric is based on the Cepstral Distance Measure (CDM) between the mean autocorrelation of the generated samples and measurement data. Using this metric, the generated channels show significant statistical similarity to the measurement data.

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[1] Wang, C.X, Bian, J., Sun, J., et al: `A survey of 5g channel measurements and models', in IEEE Communications Surveys and Tutorials, 2018, 20, pp.~3142--3168.
[2] Degli-Esposti, V., Fuschini, F., Vitucci., V.M., et al, "Speed-Up Techniques for Ray Tracing Field Prediction Models", IEEE Transactions on Antennas and Propagation, 2009, 57, pp.~ 1469-1480.
[3] 3GPP. " Spatial Channel Model for MIMO simulations," 2003, [online] available: https://www.3gpp.org/
[4] Baum, D.S., Hansen, J., Salo, J., et al, "An interim channel model for beyond-3G systems: extending the 3GPP Spatial Channel Model (SCM)," 2005 IEEE 61st Vehicular Technology Conference, Stockholm, 2005, 5, pp.~3132-3136.
[5] Thomas, T.A., Vook, F.W., Mellios, E., et al, "3D Extension of the 3GPP/ITU Channel Model", 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), Dresden, 2013, pp.~1-5.                      
[6] Wen, C., Shih, W., and Jin, S., "Deep Learning for Massive MIMO CSI Feedback," IEEE Wireless Communications Letters, Oct. 2018, 7, (5), pp.~748-751. 
[7] N. Farsad, M. Rao and A. Goldsmith, "Deep Learning for Joint Source-Channel Coding of Text," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, 2018, pp. 2326-2330.
[8] Ye, H., Li, G.Y., and Juang, B., "Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems," IEEE Wireless Communications Letters,  Feb. 2018, 7, (1), pp. 114-117.
[9] Neumann, D., Wiese, T., Utschick, W., "Learning the MMSE channel estimator", IEEE Transactions on Signal Processing, 2018, 66, (11), pp. 2905-2917.
[10] Luo, C., Ji, J., Wang, Q.,et al, "Channel state information prediction for 5G wireless communications: A deep learning approach. IEEE Transactions on Network Science and Engineering., 2018.
[11] Zhang, Y., Wen, J., Yang, G., et al, " Air-to-Air path loss prediction based on Machine Learning methods in urban environments", Wireless Communications and Mobile Computing, 2018, 2018, pp. 1-9.
[12] Lu, T., Sun, J., Wu, K., et al "High-Speed Channel Modeling With Machine Learning Methods for Signal Integrity Analysis," IEEE Transactions on Electromagnetic Compatibility, 2018, 60, (6), pp. 1957-1964.
[13] Lee, C., Yilmaz, H.B., Chae, C., et al, " Machine Learning based channel modeling for molecular MIMO communications," 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Sapporo, 2017, pp. 1-5.
[14] Navabi, S., Wang, C., Bursalioglu, O.Y. and Papadopoulos, H., "Predicting wireless channel features using neural networks." In 2018 IEEE international conference on communications (ICC),  May 2018, pp. 1-6.
[15] Jiang, Z., Chen, S., Molisch, A.F., Vannithamby, R., Zhou, S. and Niu, Z., "Exploiting wireless channel state information structures beyond linear correlations: A deep learning approach", IEEE Communications Magazine, 57, (3), pp. 28-34.
[16] Ye, H., Li, G.Y., Juang, B.H.F. and Sivanesan, K., "Channel agnostic end-to-end learning based communication systems with conditional GAN", In 2018 IEEE Globecom Workshops (GC Wkshps), December 2018, pp. 1-5.
[17] Yang Yang, Yang Li, Wuxiong Zhang, et al, Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities. IEEE Commun. Mag. 57(3): 22-27 (2019)
[18] Qianqian Zhang, Aidin Ferdowsi, Walid Saad, et al, Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks. CoRR abs/2102.01751 (2021)
[19] Timothy J. O'Shea, Tamoghna Roy, Nathan E. West, Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks. ICNC 2019: 681-686
[20] Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, et al, "Generative Adversarial Nets," in Ghahramani, Z., Welling, M., Cortes, C., et al, "Advances in Neural Information Processing Systems 27," (Curran Associates, Inc., 2014), pp. 2672-2680.
[21] Radford, A., Metz, L., Chintala, S., "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks," arXive preprint arXiv:1511.06434, 2015.
[22] Mirza, M., Osindero, S., "conditional generative adversarial nets," arXie preprint arXiv:1411.1784, 2014.
[23] Berthelot, D., Schumm, T., Metz, L., "BEGAN: Boundary Equilibrium Generative Adversarial Networks," arXive preprint arXiv:1703.10717, 2017.
[24] Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, et al, "Improved Training of Wasserstein GANs," in Guyon, I., Luxburg, U.V., Bengio, S., et al, "Advances in Neural Information Processing Systems 30," (Curran Associates, Inc., 2017), pp. 5767-5777.
[25] He, H., Wen, C., Jin, S., et al, "Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems,"IEEE Wireless Communications Letters, 2018, 7, (5), pp. 852-855.
[26] Yates, R.D., Goodman, D.J., "Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 3rd Edition," (Wiley, J., Sons, 2014)
[27] Rupp, M., Schwarz, S., Tarantez, M., "The Vienna LTE-advanced simulators." (Springer, 2016)
[28] Xie, Y., Li, Z., Li, M., "Precise Power Delay Profiling with Commodity Wi-Fi,"IEEE Transactions on Mobile Computing, 2019, 18, (6), pp. 1342-1355.
[29] Choi, Y., Choi, M., Kim, M., et al, "StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 8789-8797.