Abstract
5G and beyond (B5G) technologies have emerged with the increasing demand for higher data-rate wireless communication with low latency. The B5G network utilizes hybrid beam-forming (HBF) as a promising solution to provide large bandwidths with directional communication. Conventional HBF techniques are computationally complex and unable to fully exploit the spatial & partial channel state information, which results in very low spectral efficiency. Hence, this paper proposes an optimized framework, HYPE, integrating the convolutional neural network resulting in the reduced complexity for the HBF technique. A two-phase analog shifter is used to maximize the spectral efficiency of the system by resolving the constant modulus constraint. Experimental results justify the enhanced performance of the proposed framework than conventional algorithms. Extensive ablation studies on the proposed work were carried out to analyze efficiency more in detail.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Verma, M., Vipparthi, S.K., Singh, G.: Non-linearities improve OrigiNet based on active imaging for micro expression recognition. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (July 2020)
Lin, T., Cong, J., Zhu, Y., Zhang, J., Letaief, K.B.: Hybrid beamforming for millimeter wave systems using the MMSE criterion. IEEE Trans. Commun. 67(5), 3693–3708 (2019)
Lin, T., Zhu, Y.: Beamforming design for large-scale antenna arrays using deep learning. IEEE Wirel. Commun. Lett. 9(1), 103–107 (2019)
Huang, H., Song, Y., Yang, J., Gui, G., Adachi, F.: Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Trans. Veh. Technol. 68(3), 3027–3032 (2019)
Long, Y., Chen, Z., Fang, J., Tellambura, C.: Data-driven-based analog beam selection for hybrid beamforming under mm-wave channels. IEEE J. Sel. Top. Sig. Process. 12(2), 340–352 (2018)
Alkhateeb, A., Leus, G., Heath, R.W.: Limited feedback hybrid precoding for multi-user millimeter wave systems. IEEE Trans. Wirel. Commun. 14(11), 6481–6494 (2015)
Sohrabi, F., Yu, W.: Hybrid digital and analog beamforming design for large-scale antenna arrays. IEEE J. Sel. Top. Sig. Process. 10(3), 501–513 (2016)
Yu, X., Shen, J.C., Zhang, J., Letaief, K.B.: Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE J. Sel. Top. Sig. Process. 10(3), 485–500 (2016)
Alkhateeb, A., El Ayach, O., Leus, G., Heath, R.W.: Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE J. Sel. Top. Sig. Process. 8(5), 831–846 (2014)
El Ayach, O., Rajagopal, S., Abu-Surra, S., Pi, Z., Heath, R.W.: Spatially sparse precoding in millimeter wave MIMO systems. IEEE Trans. Wirel. Commun. 13(3), 1499–1513 (2014)
Ghosh, A., et al.: Millimeter-wave enhanced local area systems: a high-data-rate approach for future wireless networks. IEEE J. Sel. Top. Sig. Process. 32(6), 1152–1163 (2014)
Elbir, A.M.: CNN-based precoder and combiner design in mmWave MIMO systems. IEEE Commun. Lett. 23(7), 1240–1243 (2019)
Ma, W., Qi, C., Zhang, Z., Cheng, J.: Sparse channel estimation and hybrid precoding using deep learning for millimeter wave massive MIMO. IEEE Trans. Commun. 68(5), 2838–2849 (2020)
Lu, Q., Lin, T., Zhu, Y.: Channel estimation and hybrid precoding for millimeter wave communications: a deep learning-based approach. IEEE Access 9, 120924–120939 (2021)
Faragallah, O.S., El-Sayed, H.S., Mohamed, G.: Performance enhancement of MmWave MIMO systems using deep learning framework. IEEE Access 9, 92460–92472 (2021)
Unnisa, N., Tatineni, M.: Adaptive deep learning strategy with red deer algorithm for sparse channel estimation and hybrid precoding in millimeter wave massive MIMO-OFDM systems. Wirel. Pers. Commun. 122, 3019–3051 (2022)
Li, Z., Gao, W., Zhang, M., Xiong, J.: Multi-task deep learning based hybrid precoding for mmWave massive MIMO system. China Commun. 18(10), 96–106 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sharma, D., Biradar, K.M., Vipparthi, S.K., Battula, R.B. (2022). HYPE: CNN Based HYbrid PrEcoding Framework for 5G and Beyond. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-99587-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99586-7
Online ISBN: 978-3-030-99587-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)