Modeling Millimeter Wave Channels with Generative Adversarial Networks
DOI:
https://doi.org/10.3103/S0735272724010035Keywords:
millimeter wave, subterahertz, neural networks, channel modellingAbstract
According to this research, modern cellular systems are becoming more and greater reliance on concurrent communication across several discontinuous bands as a result of wider bandwidth and macro-diversity. In millimetre wave (mmWave) and Terahertz (THz) frequencies, which are frequently paired with lower frequencies for resilience, multi-frequency communication is very crucial. Statistical models capable of representing the combined distribution of channel routes over many frequencies are needed to assess these systems. A broad neural network-based training approach for multi-frequency double-directional statistical channel models is presented in this research. The suggested method involves representing every channel as a multiclustered set and training a generative adversarial network (GAN) to generate random multi-cluster profiles. The resulting cluster data consists of vectors distributed at various frequencies that are random received powers, angle, and delay. Urban micro-cellular connections at 28 and 140 GHz are modelled using ray tracing data to demonstrate the methodology. The model is readily adaptable for multi-frequency link or network layer simulation. The model may capture intriguing statistical correlations between frequencies, as studies show, and the technique involves minimal statistical assumptions.
References
W. Xia, S. Rangan, M. Mezzavilla, A. Lozano, G. Geraci, V. Semkin, and G. Loianno, “Millimeter wave channel modeling via generative neural networks,” in Proc. IEEE Globecom Workshops., 2020, pp. 1–6.
V. Degli-Esposti, F. Fuschini, E. M. Vitucci, and G. Falciasecca, “Measurement and modelling of scattering from buildings,” IEEE Transactions on Antennas and Propagation, vol. 55, no. 1, pp. 143–153, 2007.
Y. Zakaria et al., “Propagation measurements and calculation of path loss exponent for outdoor cellular communication systems at 3.5 GHz,” Radioelectronics and Communications Systems, vol. 64, no. 5, pp. 247-254, 2021.
DOI: https://doi.org/10.3103/S0735272721050034
Y. Xing and T. S. Rappaport, “Millimeter wave and terahertz urban microcell propagation measurements and models,” IEEE Communications Letters, vol. 25, no. 12, pp. 3755–3759, 2021.
J. Huang, C.-X. Wang, H. Chang, J. Sun, and X. Gao, “Multi-frequency multi-scenario millimeter wave MIMO channel measurements and modeling for B5G wireless communication systems,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 9, pp. 2010–2025, 2020.
Y. Zakaria, I. Glesk, “Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment,” the KSII Transactions on Internet and Information Systems Journal, vol. 11, no. 5, pp. 2453-2467, (2017).
DOI: 10.3837/tiis.2017.05.008
W. Khawaja, O. Ozdemir, and I. Guvenc, “UAV Air-to-Ground Channel Characterization for mmWave Systems,” in Proc. IEEE VTC-Fall, 2017.
A. Alkhateeb, “DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications,” in Proc. of Information Theory and Applications Workshop (ITA), San Diego, CA, Feb 2019, pp. 1–8.
“Remcom,” available on-line at https://www.remcom.com/, 2021-11-11.
R. He, B. Ai, A. F. Molisch, G. L. Stuber, Q. Li, Z. Zhong, and J. Yu, “Clustering enabled wireless channel modeling using big data
algorithms,” IEEE Communications Magazine, vol. 56, no. 5, pp. 177– 183, 2018.
M. Shafi, A. F. Molisch, P. J. Smith, T. Haustein, P. Zhu, P. De Silva, F. Tufvesson, A. Benjebbour, and G. Wunder, “5G: A Tutorial Overview
of Standards, Trials, Challenges, Deployment, and Practice,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 6, pp. 1201–
, 2017.
M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Toward 6g networks: Use cases and technologies,” IEEE Communications Magazine, vol. 58, no. 3, pp. 55–61, 2020.
T. S. Rappaport, Y. Xing, O. Kanhere, S. Ju, A. Madanayake, S. Mandal, A. Alkhateeb, and G. C. Trichopoulos, “Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond,” IEEE access, vol. 7, pp. 78 729–78 757, 2019.
P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” Journal of computational and applied mathematics, vol. 20, pp. 53–65, 1987.
“mmWave channel modeling git hub repository,” available on-line ,https://github.com/klmyyaqihu/mmwchanmod-GAN, 2021-12-14.