Abstract
The elaborate architectures used in millimeter wave (mmWave) MIMO communication system make its channel characteristics prediction difficult. In this regard, we consider the problem of predicting the path power loss for both line of sight and non line of sight mmWave channels by employing deep learning (DL) based data driven model. The success of a DL technique depends on how well we train the model, which again relies on the training data. It is often hard to get the amount of data needed to build and train a model. The paper proposes a methodology to predict the channel characteristics using the available limited data. For this, the work effectively develops two deep learning models: Model 1: Autoencoder for creation of new data from the limited data and Model 2: Deep Convolutional Neural Network (DCNN) for channel characteristic prediction. Simulation results show that the proposed model 1 is able to generate more data in the desired direction and model 2 can accurately determine the path power loss associated with a mmWave channel, with very minimum error. The paper also demonstrates a method of extracting useful information using the constructed data driven model.
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Dataset and code will be shared upon acceptance of manuscript. Link to dataset and code: https://tinyurl.com/w3n95fjy.
References
Xiao, M., Mumtaz, S., Huang, Y., Dai, L., Li, Y., Matthaiou, M., & Ghosh, A. (2017). Millimeter wave communications for future mobile networks. IEEE Journal on Selected Areas in Communications, 35(9), 1909–1935.
Niu, Y., Li, Y., Jin, D., Su, L., & Vasilakos, A. V. (2015). A survey of millimeter wave communications (mmWave) for 5G: Opportunities and challenges. Wireless Networks, 21(8), 2657–2676.
Rappaport, T. S., Xing, Y., MacCartney, G. R., Molisch, A. F., Mellios, E., & Zhang, J. (2017). Overview of millimeter wave communications for fifth-generation (5G) wireless networks-With a focus on propagation models. IEEE Transactions on Antennas and Propagation, 65(12), 6213–6230.
Rumelhart, D., Hinton, G., & Williams, R. (1987). Learning internal representations by error propagation. In Parallel distributed processing: Explorations in the microstructure of cognition: Foundations (pp. 318–362). Cambridge.
Wan, Z., Zhang, Y., & He, H. (2017). Variational autoencoder based synthetic data generation for imbalanced learning. In 2017 IEEE symposium series on computational intelligence (SSCI) (pp. 1–7).
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press.
Qin, Z., Ye, H., Li, G. Y., & Juang, B. H. F. (2019). Deep learning in physical layer communications. IEEE Wireless Communications, 26(2), 93–99.
Li, R., et al. (2017). Intelligent 5G: When cellular networks meet artificial intelligence. IEEE Wireless Communications, 24(5), 175–83.
Ye, H., Li, G. Y., & Juang, B.-H. (2017). Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Communications Letters, 7(1), 114–117.
Dong, P., Zhang, H., Li, G. Y., NaderiAlizadeh, N., & Gaspar, I. S.. (2019). Deep CNN for wideband mmWave massive MIMO channel estimation using frequency correlation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4529–4533).
Soltani, M., Pourahmadi, V., Mirzaei, A., & Sheikhzadeh, H. (2019). Deep learning based channel estimation. IEEE Communications Letters, 23(4), 652–655.
He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018). Deep learning-based channel estimation for beamspace mmWave massive MIMO systems. IEEE Wireless Communications Letters, 7(5), 852–855.
Jiang, W., & Schotten, H. D. (2020). Deep learning for fading channel prediction. IEEE Open Journal of the Communications Society, 1, 320–332.
Wang, J., Ding, Y., Bian, S., Peng, Y., Liu, M., & Gui, G. (2019). UL-CSI data driven deep learning for predicting DL-CSI in cellular FDD systems. IEEE Access, 7, 96105–96112.
Famoriji, O. J., Ogundepo, O. Y., & Qi, X. (2020). An intelligent deep learning-based direction-of-arrival estimation scheme using spherical antenna array with unknown mutual coupling. IEEE Access, 8, 179259–179271.
Huang, H., Yang, J., Huang, H., Song, Y., & Gui, G. (2018). Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Transactions on Vehicular Technology, 67(9), 8549–8560.
Nie, S., MacCartney, G. R., Sun, S., & Rappaport, T. S. (2013). 72 GHz millimeter wave indoor measurements for wireless and backhaul communications. IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 2429–2433).
MacCartney, G. R., Zhang, J., Nie, S., & Rappaport, T. S. (2013). Path loss models for 5G millimeter wave propagation channels in urban microcells. IEEE Global Communications Conference (GLOBECOM).
Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., Wong, G. N., Schulz, J. K., Samimi, M., & Gutierrez, F. (2013). Millimeter wave mobile communications for 5G cellular: It will work! IEEE Access, 335–349.
Azar, Y., Wong, G. N., Wang, K., Mayzus, R., Schulz, J. K., Zhao, H., Gutierrez, F. Jr, Hwang, D., & Rappaport, T. S. (2013). 28 GHz propagation measurements for outdoor cellular communications using steerable beam antennas in New York City. In IEEE International Conference on Communications (ICC) (pp. 5143–5147). Budapest.
Maccartney, G. R., Rappaport, T. S., Samimi, M. K., & Sun, S. (2015). Millimeter wave omnidirectional path loss data for small cell 5G channel modeling. IEEE Access (pp. 1573–1580).
MacCartney, G. R., Rappaport, T. S. (2014). 73 GHz millimeter wave propagation measurements for outdoor urban mobile and backhaul communications in New York City. In IEEE International Conference on Communications (ICC) (4862–4867). Sydney.
Rappaport, T. S., MacCartney, G. R., Samimi, M. K., & Sun, S. (2015). Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design. IEEE Transactions on Communications, 63(9), 3029–3056.
Rappaport, Theodore S. (2002). Wireless communications-principles and practice, (The Book End). Microwave Journal, 45(12), 128–129.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Neema M and E S Gopi. The first draft of the manuscript was written by Neema M and all authors commented on each versions of the manuscript. All authors read and approved the final manuscript.
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Neema, M., Gopi, E.S. Data Driven Approach for mmWave Channel Characteristics Prediction Using Deep Neural Network. Wireless Pers Commun 120, 2161–2177 (2021). https://doi.org/10.1007/s11277-021-08768-7
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DOI: https://doi.org/10.1007/s11277-021-08768-7