Enhanced Beamspace Channel Recovery in mmWave MIMO Using Deep Neural Networks
DOI:
https://doi.org/10.4108/eetiot.10247Keywords:
MIMO, mmwave, Channel Recovery, Beam space, deep neural networkAbstract
Millimeter-wave (mmWave) massive MIMO systems use many antennas. These systems offer high data rates. But using many radio frequency (RF) chains increases cost and power use. To solve this, lens antenna arrays are used. Energy is focused, allowing the use of fewer RF chains. However, this creates a new challenge. With fewer RF chains, it is hard to estimate the wireless channel. Accurate channel estimation is needed for good system performance. In beamspace, the channel is sparse. This shows that only a few values are large. The rest are close to zero. Because of this, the problem is seen as sparse signal recovery. AMP (Approximate Message Passing) is one popular algorithm used for this. A better version named LAMP (Learned AMP) uses deep learning. But it still does not give the best results. This paper proposes a new method GM-LAMP. It improves the channel estimation accuracy. It uses prior knowledge about the channel. It assumes that the beamspace channel follows a Gaussian mixture distribution. First, a new shrinkage function is created based on this distribution. Then, the original function in the LAMP network is replaced with the new one. As a result, a better deep learning model is developed. The final GM-LAMP network estimates the beamspace channel more precisely. It works well with both theoretical models and real-world data. Simulations show that GM-LAMP performs better than earlier methods. This approach combines math knowledge and deep learning. It shows that using prior information helps deep networks make smarter predictions. The proposed method offers better accuracy and is useful for future mmWave systems.
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References
[1] Y. Yang, J. Xu, G. Shi, and C.-X. Wang, “5g wireless systems,” Wireless Networks, 2018.
[2] N. Faruk, A. Ayeni, and Y. A. Adediran, “On the study of empiricalpath loss models for accurate prediction of tv signal for secondary users,” Progress In Electromagnetics Research B, vol. 49, pp. 155–176, 2013.
[3] L. Zhou, F. A. Khan, T. Ratnarajah, and C. B. Papadias, “Achieving arbitrary signals transmission using a single radio frequency chain,” IEEE Transactions on Communications, vol. 63, no. 12, pp. 4865–4878, 2015.
[4] X. Wei, C. Hu, and L. Dai, “Deep learning for beamspace channel estimation in millimeter-wave massive mimo systems,” IEEE Transactions on Communications, vol. 69, no. 1, pp. 182–193, 2020.
[5] A. Ali, N. Gonz´alez-Prelcic, and R. W. Heath, “Millimeter wave beam-selection using out-of-band spatial information,” IEEE Transactions on Wireless Communications, vol. 17, no. 2, pp. 1038–1052, 2017.
[6] H. Yin, D. Gesbert, M. Filippou, and Y. Liu, “A coordinated approach to channel estimation in large-scale multiple-antenna systems,” IEEE Journal on selected areas in communications, vol. 31, no. 2, pp. 264–273, 2013.
[7] L. Stankovi´c, E. Sejdi´c, S. Stankovi´c, M. Dakovi´c, and I. Orovi´c, “A tutorial on sparse signal reconstruction and its applications in signal processing,” Circuits, Systems, and Signal Processing, vol. 38, no. 3, pp. 1206–1263, 2019.
[8] Y. He and G. K. Atia, “Coarse to fine two-stage approach to robust tensor completion of visual data,” IEEE Transactions on Cybernetics, vol. 54, no. 1, pp. 136–149, 2022.
[9] S. F. Ahmed, M. S. B. Alam, M. Hassan, M. R. Rozbu, T. Ishtiak, N. Rafa, M. Mofijur, A. Shawkat Ali, and A. H. Gandomi, “Deep learning modelling techniques: current progress, applications, advantages, and challenges,” Artificial Intelligence Review, vol. 56, no. 11, pp. 13521–13617, 2023.
[10] Z. Zhang, Y. Li, X. Yan, and Z. Ouyang, “A low-complexity amp detection algorithm with deep neural network for massive mimo systems,” Digital Communications and Networks, vol. 10, no. 5, pp. 1375–1386, 2024.
[11] S. Zheng, A. Vishnu, and C. Ding, “Accelerating deep learning with shrinkage and recall,” in 2016 IEEE 22nd International conference on parallel and distributed systems (ICPADS), pp. 963–970, IEEE, 2016.
[12] K.-I. Kim, W.-U. Kwak, and K.-H. Choe, “Closed-form shrinkage function based on mixture of gauss–laplace distributions for dropping ambient noise,” International Journal of Wavelets, Multiresolution and Information Processing, vol. 21, no. 04, p. 2250061, 2023.
[13] M. A. Hasan, M. T. Hassan, and F. Haque, “A deep learning-based efficient beamspace estimation approach in millimeter-wave massive mimo systems,” in 2023 6th International Conference on Electrical Information and Communication Technology (EICT), pp. 1–6, IEEE, 2023.
[14] D. Ferranti, D. Krane, and D. Craft, “The value of prior knowledge in machine learning of complex network systems,” Bioinformatics, vol. 33, no. 22, pp. 3610–3618, 2017.
[15] A. Alkhateeb, O. El Ayach, G. Leus and R. W. Heath, “Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems,” IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 831–846, Oct. 2014.
[16] A. Alkhateeb, J. Mo, N. Gonzalez-Prelcic and R. W. Heath, “MIMO Precoding and Combining Solutions for Millimeter-Wave Systems,” IEEE Commun. Mag., vol. 52, no. 12, pp. 122–131, Dec. 2014.
[17] S. Rangan, “Generalized Approximate Message Passing for Estimation with Random
Linear Mixing,” in Proc. IEEE ISIT, St. Petersburg, Russia, 2011, pp. 2168–2172.
[18] M. F. Borgerding, P. Schniter and S. Rangan, “AMP-Inspired Deep Networks for Sparse Linear Inverse Problems,” IEEE Trans. Signal Process., vol. 65, no. 16, pp. 4293–4308, Aug. 2017.
[19] H. Ye, G. Y. Li and B. Juang, “Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems,” IEEE Wireless Commun. Lett., vol. 7, no. 1, pp. 114–117, Feb. 2018.
[20] H. He, C. Wen, S. Jin and G. Y. Li, “Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, Oct. 2018.
[21] F. Liang, C. Shen, W. Yu and F. Wu, “Towards Optimal Power Control via Ensembling Deep Neural Networks,” IEEE Trans. Commun., vol. 67, no. 6, pp. 4033–4046, Jun. 2019.
[22] V. Monga, Y. Li and Y. C. Eldar, “Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing,” IEEE Signal Process. Mag., vol. 38, no. 2, pp. 18–44, Mar. 2021.
[23] Z. Chen, F. Sohrabi and W. Yu, “Bayesian Sparse Channel Estimation for Massive MIMO with Hybrid Architecture,” IEEE Trans. Signal Process., vol. 66, no. 23, pp. 6164–6178, Dec. 2018.
[24] V. Venkateswaran and A. Sayeed, “Deep Learning for Joint MIMO Channel Estimation and Data Detection with Imperfect Training,” in Proc. IEEE ICC, Montreal, Canada, 2021.
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Copyright (c) 2025 V. Saraswathi, Vatsala Anand, Yaddanapudi Venkata Bhaskara Lakshmi, Samana Vinaya Kumar, Vijaya Babu Burra, U.S.B.K. Mahalaxmi, Suneetha Jalli, V. Vijayasri Bolisetty, Sarala Patchala
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