An Optimized Sequence for Sparse Channel Estimation in a 5G MIMO System

References

• Al-Salihi, H., Al-Gharbawi, M., & Said, F. (2021, December). Optimal pilot sequence design for machine learning based channel estimation in FDD massive mimo systems. In 2021 ITU Kaleidoscope: Connecting Physical and Virtual Worlds (ITU K), Geneva, Switzerland (pp. 1–5). IEEE.
   Google Scholar
• 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. https://doi.org/10.1109/TWC.2019.2962474
   Web of Science ®Google Scholar
• Belgiovine, M., Sankhe, K., Bocanegra, C., Roy, D., & Chowdhury, K. R. (2021). Deep learning at the edge for channel estimation in beyond-5G massive MIMO. IEEE Wireless Communications, 28(2), 19–25. https://doi.org/10.1109/MWC.001.2000322
   Web of Science ®Google Scholar
• Chawla, A., & Jagannatham, A. K. (2019). Spectral efficiency of very large multiuser MIMO systems for time-selective fading. In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia (pp. 1–5).
   Google Scholar
• Chitikena, R., & Esther Rani, P. (2023). Deep learning based channel estimation and secure data transmission using IEHO-DLNN and MECC algorithm in mu-MIMO OFDM System. Wireless Personal Communications, 129(4), 2269–2289. https://doi.org/10.1007/s11277-023-10172-2
   Web of Science ®Google Scholar
• Dash, L., & Thampy, A. S. (2023). Channel estimation using hybrid optimizer based recurrent neural network long short term memory for MIMO communications in 5G network. SN Applied Sciences, 5(2), 60. https://doi.org/10.1007/s42452-022-05253-z
   Web of Science ®Google Scholar
• Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169–196. https://doi.org/10.1016/j.knosys.2018.11.024
   Web of Science ®Google Scholar
• Ehsanfar, S., Matthé, M., Zhang, D., & Fettweis, G. (2016). A study of pilot-aided channel estimation in MIMO-GFDM systems. In WSA 2016; 20th International ITG Workshop on Smart Antennas, Munich, Germany (pp. 1–8). VDE.
   Google Scholar
• Elbir, A. M., & Mishra, K. V. (2019). Joint antenna selection and hybrid beamformer design using unquantized and quantized deep learning networks. IEEE Transactions on Wireless Communications, 19(3), 1677–1688. https://doi.org/10.1109/TWC.2019.2956146
   Web of Science ®Google Scholar
• Falahati, A., Karami, M. R., & Zargar, B. S. (2016). Removal of the OFDM segment without affecting signalling BER to provide higher data rate as well as a cost efficient time processing or a MIMO-OFDM relay network. IETE Journal of Research, 62(3), 339–346. https://doi.org/10.1080/03772063.2015.1084896
   Web of Science ®Google Scholar
• Farzamnia, A., Hlaing, N. W., Haldar, M. K., & Rahebi, J. (2017). Channel estimation for sparse channel OFDM systems using least square and minimum mean square error techniques. In 2017 international conference on engineering and technology (ICET), Antalya, Turkey (pp. 1–5). IEEE.
   Google Scholar
• Gao, J., Zhong, C., Li, G. Y., & Zhang, Z. (2021). Deep learning-based channel estimation for massive MIMO with hybrid transceivers. IEEE Transactions on Wireless Communications, 21(7), 5162–5174. https://doi.org/10.1109/TWC.2021.3137354
   Web of Science ®Google Scholar
• Ge, L., Guo, Y., Zhang, Y., Chen, G., Wang, J., Dai, B., Li, M., & Jiang, T. (2021). Deep neural network based channel estimation for massive MIMO-OFDM systems with imperfect channel state information. IEEE Systems Journal, 16(3), 4675–4685. https://doi.org/10.1109/JSYST.2021.3114229
   Web of Science ®Google Scholar
• Ge, X., Sun, Y., Gharavi, H., & Thompson, J. (2018). Joint optimization of computation and communication power in multi-user massive MIMO systems. IEEE Transactions on Wireless Communications, 17(6), 4051–4063. https://doi.org/10.1109/TWC.2018.2819653
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1109/OJVT.2020.3045470
   Google Scholar
• Huang, Y., He, Y., He, W., Shi, L., Cheng, T., & Sui, Y. (2020). Channel estimation in massive MIMO systems based on generalized block adaptive matching pursuit algorithm. IEEE Wireless Communications Letters, 9(12), 2098–2101. https://doi.org/10.1109/LWC.2020.3013689
   Web of Science ®Google Scholar
• Jayaram, M., & Anjaneyulu, B. M. (2023, April). Pilot Compression Analysis for Feedback Based Channel Estimation Model in FDD massive MIMO. In 2023 International Conference on Networking and Communications (ICNWC), Chennai, India (pp. 1–6). IEEE.
   Google Scholar
• Kim, J., Ahn, Y., Kim, S., & Shim, B. (2022, May). Parametric sparse channel estimation using long short-term memory for mmwave massive mimo systems. In ICC 2022-IEEE International Conference on Communications, Seoul, Republic of Korea (pp. 1397–1402). IEEE.
   Google Scholar
• Liu, Q., Li, Y., & Sun, J. (2023). A Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems. Sensors, 23(5), 2638. https://doi.org/10.3390/s23052638
   PubMed Web of Science ®Google Scholar
• Liu, Z., Zhang, L., & Ding, Z. (2020). Overcoming the channel estimation barrier in massive MIMO communication via deep learning. IEEE Wireless Communications, 27(5), 104–111. https://doi.org/10.1109/MWC.001.1900413
   Web of Science ®Google Scholar
• Li, D., Xu, Y., Zhao, M., Zhang, S., & Zhu, J. (2021). Knowledge-driven machine learning-based channel estimation in massive MIMO system. In 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Nanjing, China (pp. 1–6).
   Google Scholar
• Ma, X., Gao, Z., Gao, F., & DiRenzo, M. (2021). Model-driven deep learning based channel estimation and feedback for millimeter-wave massive hybrid MIMO systems. IEEE Journal on Selected Areas in Communications, 39(8), 2388–2406. https://doi.org/10.1109/JSAC.2021.3087269
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1109/TCOMM.2020.2974457
   Web of Science ®Google Scholar
• Mashhadi, M. B., & Gündüz, D. (2021). Pruning the pilots: Deep learning-based pilot design and channel estimation for MIMO-OFDM systems. IEEE Transactions on Wireless Communications, 20(10), 6315–6328. https://doi.org/10.1109/TWC.2021.3073309
   Web of Science ®Google Scholar
• Ma, X., Yang, F., Liu, S., Song, J., & Han, Z. (2017). Design and optimization on training sequence for mmWave communications: A new approach for sparse channel estimation in massive MIMO. IEEE Journal on Selected Areas in Communications, 35(7), 1486–1497. https://doi.org/10.1109/JSAC.2017.2698978
   Web of Science ®Google Scholar
• Melgar, A., de la Fuente, A., Carro-Calvo, L., Barquero-Pérez, Ó., & Morgado, E. (2022). Deep neural network: An alternative to traditional channel estimators in massive MIMO systems. IEEE Transactions on Cognitive Communications and Networking, 8(2), 657–671. https://doi.org/10.1109/TCCN.2022.3164888
   Web of Science ®Google Scholar
• Mohades, Z., & Vakili, V. T. (2021). Deep Neural Network for Compressive Sensing and Application to massive MIMO channel estimation. Circuits, Systems, and Signal Processing, 40(9), 1–16. https://doi.org/10.1007/s00034-021-01675-z
   Web of Science ®Google Scholar
• Nair, A. K., & Menon, V. (2022, January). Joint channel estimation and symbol detection in MIMO-OFDM systems: A deep learning approach using Bi-LSTM. In 2022 14th International Conference on Communication Systems & Networks (COMSNETS), Bangalore, India (pp. 406–411). IEEE.
   Google Scholar
• Özdogan, Ö., Björnson, E., & Zhang, J. (2019). Performance of cell-free massive MIMO with rician fading and phase shifts. IEEE Transactions on Wireless Communications, 18(11), 5299–5315. https://doi.org/10.1109/TWC.2019.2935434
   Web of Science ®Google Scholar
• Pasangi, P., Atashbar, M., & Feghhi, M. M. (2020). Blind downlink channel estimation of multi-user multi-cell massive MIMO system in presence of the pilot contamination. AEU-International Journal of Electronics and Communications, 117, 153099. https://doi.org/10.1016/j.aeue.2020.153099
   Web of Science ®Google Scholar
• Ravikumar, C. V. (2023). Developing novel channel estimation and hybrid precoding in millimeter-wave communication system using heuristic-based deep learning. Energy, 268, 126600. https://doi.org/10.1016/j.energy.2022.126600
   Web of Science ®Google Scholar
• Reddy, G. N., Ravikumar, C. V., & Rajesh, A. (2022). Literature review and research direction towards channel estimation and hybrid pre-coding in mmWave massive MIMO communication systems. Journal of Reliable Intelligent Environments, 9(2), 1–20. https://doi.org/10.1007/s40860-022-00174-5
   Google Scholar
• Riadi, A., Boulouird, M., & Hassani, M. M. R. (2019, April). ZF/MMSE and OSIC detectors for uplink OFDM massive MIMO systems. In 2019 IEEE jordan international joint conference on electrical engineering and information technology (JEEIT), Amman, Jordan (pp. 767–772).
   Google Scholar
• Sadraei, M. H., Fazel, M. S., & Doost‐Hoseini, A. M. (2020). Ergodic spectral efficiency of massive MIMO with correlated Rician channel and MRC detection based on LS and MMSE channel estimation. IET Communications, 14(17), 2962–2971. https://doi.org/10.1049/iet-com.2019.0905
   Web of Science ®Google Scholar
• Sohrabi, F., Attiah, K. M., & Yu, W. (2021). Deep learning for distributed channel feedback and multiuser precoding in FDD massive MIMO. IEEE Transactions on Wireless Communications, 20(7), 4044–4057. https://doi.org/10.1109/TWC.2021.3055202
   Web of Science ®Google Scholar
• Sun, B., Zhou, Y., Yuan, J., & Shi, J. (2020). Interference cancellation based channel estimation for massive MIMO systems with time shifted pilots. IEEE Transactions on Wireless Communications, 19(10), 6826–6843. https://doi.org/10.1109/TWC.2020.3006208
   Web of Science ®Google Scholar
• Tang, R., Qi, C., & Zhang, P. (2023, June). Block Sparse Channel Estimation based on residual difference and deep learning for wideband MmWave massive MIMO. In 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy (pp. 1–6).
   Google Scholar
• Unnisa, N., & Tatineni, M. (2022). Adaptive deep learning strategy with red deer algorithm for sparse channel estimation and hybrid precoding in millimeter wave massive MIMO-OFDM systems. Wireless Personal Communications, 122(4), 3019–3051. https://doi.org/10.1007/s11277-021-09039-1
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1109/TCOMM.2020.3027027
   Web of Science ®Google Scholar
• Wen, C. K., Shih, W. T., & Jin, S. (2018). Deep learning for massive MIMO CSI feedback. IEEE Wireless Communications Letters, 7(5), 748–751. https://doi.org/10.1109/LWC.2018.2818160
   Web of Science ®Google Scholar
• Yuan, X., Qi, S., & Wang, Y. (2020). Stacked enhanced auto-encoder for data-driven soft sensing of quality variable. IEEE Transactions on Instrumentation and Measurement, 69(10), 7953–7961. https://doi.org/10.1109/TIM.2020.2985614
   Web of Science ®Google Scholar
• Zhang, Y., Alrabeiah, M., & Alkhateeb, A. (2020). Deep learning for massive MIMO with 1-bit ADCs: When more antennas need fewer pilots. IEEE Wireless Communications Letters, 9(8), 1273–1277. https://doi.org/10.1109/LWC.2020.2987893
   Web of Science ®Google Scholar
• Zhao, J., Wu, Y., Zhang, Q., & Liao, J. (2023). Two-stage channel estimation for mmwave massive mimo systems based on resnet-unet. IEEE Systems Journal, 17(3), 4291–4300. https://doi.org/10.1109/JSYST.2023.3234048
   Web of Science ®Google Scholar
• Zheng, X., & Lau, V. K. (2021). Online deep neural networks for mmWave massive MIMO channel estimation with arbitrary array geometry. IEEE Transactions on Signal Processing, 69, 2010–2025. https://doi.org/10.1109/TSP.2021.3068568
   Web of Science ®Google Scholar
• Zia, M. U., Xiang, W., Vitetta, G. M., & Huang, T. (2022). Deep learning for Parametric Channel Estimation in massive MIMO systems. IEEE Transactions on Vehicular Technology, 72(4), 4157–4167. https://doi.org/10.1109/TVT.2022.3223896
   Web of Science ®Google Scholar