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Research article

Optimized CNN and adaptive RBFNN for channel estimation and hybrid precoding approaches for multi user millimeter wave massive MIMO

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Received 25 Jul 2023, Accepted 04 May 2024, Published online: 03 Jul 2024

References

  • Abdallah, A., Celik, A., Mansour, M. M., & Eltawil, A. M. (2021). Deep learning-based frequency-selective channel estimation for hybrid mmWave MIMO systems. IEEE Transactions on Wireless Communications, 21(6), 3804–3821.
  • Alouzi, M., Chan, F., & D’Amours, C. (2021). Low complexity hybrid precoding and combining for millimeter wave systems. Institute of Electrical and Electronics Engineers Access, 9, 95911–95924. doi:10.1109/ACCESS.2021.3093880
  • Balevi, E., Doshi, A., & Andrews, J. G. (2020). Massive MIMO channel estimation with an untrained deep neural network. IEEE Transactions on Wireless Communications, 19(3), 2079–2090.
  • Bohan, L., Yang, L.-L., Maunder, R. G., & Sun, S. (2020). Self-interference cancellation and channel estimation in multicarrier-division duplex systems with hybrid beamforming. Institute of Electrical and Electronics Engineers Access, 8, 160653–160669. doi:10.1109/ACCESS.2020.3020910
  • Charrada, A., & Samet, A. (2019). Fast-fading channel environment estimation using linear minimum mean squares error-support vector regression. Wireless Personal Communications, 106(4), 1897–1913.
  • Chen, Z., Tang, J., Yin Zhang, X., Ka Chun so, D., Jin, S., & Wong, K.-K. (2021). Hybrid evolutionary-based sparse channel estimation for IRS-assisted mmWave MIMO systems. IEEE Transactions on Wireless Communications, 21(3), 1586–1601.
  • Dhanasekaran, S., Palanisamy, S., Hajjej, F., Ibrahim Khalaf, O., Muttashar Abdulsahib, G., & Ramalingam, S. (2022). Discrete Fourier transform with denoise model based least square wiener channel estimator for channel estimation in MIMO-OFDM. Entropy, 24(11), 1601. doi:10.3390/e24111601
  • Elbir, A. M. (2019). CNN-based precoder and combiner design in mmWave MIMO systems. IEEE Communications Letters, 23(7), 1240–1243.
  • Elbir, A. M., Papazafeiropoulos, A., Kourtessis, P., & Chatzinotas, S. (2020). Deep channel learning for large intelligent surfaces aided mm-wave massive MIMO systems. IEEE Wireless Communications Letters, 9(9), 1447–1451.
  • Feng, C., Shen, W., Gao, X., An, J., & Hanzo, L. (2020). Dynamic hybrid precoding relying on twin-resolution phase shifters in millimeter-wave communication systems. IEEE Transactions on Wireless Communications, 20(2), 812–826.
  • Hassan, K., Masarra, M., Zwingelstein, M., & Dayoub, I. (2020). Channel estimation techniques for millimeter-wave communication systems: Achievements and challenges. IEEE Open Journal of the Communications Society, 1 , 1336–1363. doi:10.1109/OJCOMS.2020.3015394
  • Hirose, H., Ohtsuki, T., & Gui, G. (2020). Deep learning-based channel estimation for massive MIMO systems with pilot contamination. IEEE Open Journal of Vehicular Technology, 2, 67–77. doi:10.1109/OJVT.2020.3045470
  • Hu, Z., Chen, Y., & Han, C. (2023). PRINCE: A pruned AMP integrated deep CNN method for efficient channel estimation of millimeter-wave and terahertz ultra-massive mimo systems. IEEE Transactions on Wireless Communications, 22. IEEE. doi:10.1109/TWC.2023.3258405
  • Liang, S., Zhao, F., Zhao, F., Huang, Y., & Wang, C. (2020). ANN-based channel estimation algorithm of IM/DD-OFDM/OQAM-PON systems with mobile fronthaul network in 5G. Optical Fiber Technology, 59, 102310. doi:10.1016/j.yofte.2020.102310
  • Lin, Y., Jin, S., Matthaiou, M., & You, X. (2020). Tensor-based channel estimation for millimeter wave MIMO-OFDM with dual-wideband effects. IEEE Transactions on Communications, 68(7), 4218–4232.
  • Liu, F., Zhu, H., Li, C., Li, J., Wang, P., & Orlik, P. V. (2020). Angular-domain channel estimation for one-bit massive MIMO systems: Performance bounds and algorithms. IEEE Transactions on Vehicular Technology, 69(3), 2928–2942.
  • Liu, S., Gao, Z., Zhang, J., DiRenzo, M., & Alouini, M.-S. (2020). Deep denoising neural network assisted compressive channel estimation for mmWave intelligent reflecting surfaces. IEEE Transactions on Vehicular Technology, 69(8), 9223–9228.
  • Liu, S., & Huang, X. (2021). Sparsity-aware channel estimation for mmWave massive MIMO: A deep CNN-based approach. China Communications, 18(6), 162–171.
  • Ma, W., Qi, C., Zhang, Z., & Cheng, J. (2020). Sparse channel estimation and hybrid precoding using deep learning for millimeter wave massive MIMO. IEEE Transactions on Communications, 68(5), 2838–2849.
  • Mao, J., Gao, Z., Wu, Y., & Alouini, M.-S. (2018). Over-sampling codebook-based hybrid minimum sum-mean-square-error precoding for millimeter-wave 3D-MIMO. IEEE Wireless Communications Letters, 7(6), 938–941.
  • Muthukumaran, K., Thayaneshwaran, M., & Natesan, I. (2023). Dual-stage channel estimation using ANN and Hybrid RIS Aided MIMO systems. In 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN) (pp. 1708–1715, Salem, India.
  • Nguyen, L. V., Lee Swindlehurst, A., & Nguyen, D. H. (2021). SVM-based channel estimation and data detection for one-bit massive MIMO systems. IEEE Transactions on Signal Processing, 69, 2086–2099. doi:10.1109/TSP.2021.3068629
  • Pradheep, B., Rajan, T., & Madhan Kumar, K. (2013). A survey on channel estimation schemes for MIMO systems. International Journal of Advanced Research in Electronics and Communication Engineering, 2(1). https://api.semanticscholar.org/CorpusID:212570935
  • Rajan, B. P., & Muthukumaran, N. (2022). Grey neural network channel estimation and RBFNN hybrid precoding schemes for the multi user millimeter wave massive MIMO. Trans Emerging Tel Tech, e4689. https://doi.org/10.1002/ett.4689
  • Rajan, B., Pradheep, T., & Madhan Kumar, K. (2013). Kalman channel estimation by soft decision feedback for MIMO system. International Conference on Engineering and Technology, 243.
  • Rajan, B., Pradheep, T., & Muthukumaran, N. (2023). Two stage deep learning channel estimation scheme for massive MIMO systems. Inventive Computation and Information Technologies: Proceedings of ICICIT 2022 (pp. 779–788). Springer Nature Singapore, Singapore.
  • Rajan, P. T., P, R. R., & R, V. (2023). ANN based quick and accurate estimate of channel availability by using hybrid M1-MMSE equalizer. In 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 434–440, Lalitpur, Nepal. https://doi.org/10.1109/ICICT57646.2023.10134093
  • Salh, A., Audah, L., Abdullah, Q., Aydoğdu, Ö., Alhartomi, M. A., Hamood Alsamhi, S., Almalki, F. A., & Shah, N. S. M. (2021). Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G. Institute of Electrical and Electronics Engineers Access, 10, 4714–4727. doi:10.1109/ACCESS.2021.3139338
  • Singh, D., & Shukla, A. (2022). Manifold optimization with MMSE hybrid precoder for mm-wave massive MIMO communication. Science Technology, 25(1), 36–46.
  • Sun, Y., Gao, Z., Wang, H., Shim, B., Gui, G., Mao, G., & Adachi, F. (2020). Principal component analysis-based broadband hybrid precoding for millimeter-wave massive MIMO systems. IEEE Transactions on Wireless Communications, 19(10), 6331–6346.
  • Thoota, S. S., & Murthy, C. R. (2021). Variational Bayes’ joint channel estimation and soft symbol decoding for uplink massive MIMO systems with low resolution ADCs. IEEE Transactions on Communications, 69(5), 3467–3481.
  • Wang, Y., Chen, X., Cai, Y., Champagne, B., & Hanzo, L. (2022). Channel estimation for hybrid massive MIMO systems with adaptive-resolution ADCs. IEEE Transactions on Communications, 70(3), 2131–2146.
  • Wang, Y., Lu, H., & Sun, H. (2021). Channel estimation in IRS-enhanced mmWave system with super-resolution network. IEEE Communications Letters, 25(8), 2599–2603.
  • Wei, L., Huang, C., Alexandropoulos, G. C., Yuen, C., Zhang, Z., & Debbah, M. (2021). Channel estimation for RIS-empowered multi-user MISO wireless communications. IEEE Transactions on Communications, 69(6), 4144–4157.
  • Wei, X., Hu, C., & Dai, L. (2020). Deep learning for beamspace channel estimation in millimeter-wave massive MIMO systems. IEEE Transactions on Communications, 69(1), 182–193.
  • Xiang, B., & Mu, Q. (2021). Gimbal control of inertially stabilized platform for airborne remote sensing system based on adaptive RBFNN feedback model. IFAC Journal of Systems and Control, 16, 100148. doi:10.1016/j.ifacsc.2021.100148. http://www.sciencedirect.com/science/article/pii/S2468601821000079
  • Zhang, R., Yang, L., Tang, M., Tan, W., & Zhao, J. (2023). Channel estimation for mmwave massive MIMO systems with mixed-ADC architecture. IEEE Open Journal of the Communications Society, 4, 606–613. doi:10.1109/OJCOMS.2023.3242668
  • Zhang, Y., Du, J., Chen, Y., Li, X., Rabie, K. M., & Kharel, R. (2020). Near-optimal design for hybrid beamforming in mmWave massive multi-user MIMO systems. Institute of Electrical and Electronics Engineers Access, 8, 129153–129168. doi:10.1109/ACCESS.2020.3009238
  • Zhang, Y., Huo, Y., Wang, D., Dong, X., & You, X. (2020). Channel estimation and hybrid precoding for distributed phased arrays based MIMO wireless communications. IEEE Transactions on Vehicular Technology, 69(11), 12921–12937.
  • Zhu, Z., Deng, H., Xu, F., Zhang, W., Liu, G., & Zhang, Y. (2022). Hybrid precoding-based millimeter wave massive MIMO-NOMA systems. Symmetry, 14(2), 412.

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