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RF Chain Selection Using Hybrid Optimization with Precoding in mm-Wave Massive MIMO Systems

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Abstract

Energy efficiency is considered the prominent metric in data communication systems and has a greater impact on designing Millimetre-wave (mm-wave) multiple input multiple output (MIMO) systems. As the recent mm-wave system relies upon the number of radiofrequency (RF) chains, effective RF chains are preferred to maximize overall system performance, energy and spectral efficiency. Degraded system efficiency and increased path loss are the major limitations faced in MIMO systems. This harms the data rate transmission between transmitter and receiver antennas. Hence, an effectual mm-wave with a massive MIMO model is presented in this research by adopting standard techniques. The chief objective of this research is to maximize energy and spectral efficiency with an appropriate selection of RF chains. The optimal RF chains are selected through a hybrid optimization model called the differential evolution firefly selection model (DEA + FOA). After selecting optimal RF chains, an appropriate precoding scheme is used to route the transmitted data over the preferred direction by overcoming the path-loss issues. In the precoding step, the successive interference cancellation based technique is employed to optimize the RF chain's sub-capacity and increase the system efficiency. The performance of a proposed model is analyzed by evaluating the energy and spectral efficiency by varying the number of RF chains at low and high signal-to-noise ratio rates. The proposed optimization and precoding techniques are compared with existing approaches to analyze the efficacy of a proposed MIMO system. The proposed model has proven extremely effective in transforming data from transmitter to receiver with increased system efficiency.

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References

  1. Nouri, M., Behroozi, H., Bastami, H., Moradikia, M., Jafarieh, A., Abdelhadi, A., & Han, Z. (2023). Hybrid precoding based on active learning for mmWave massive MIMO communication systems. IEEE Transactions on Communications., 71, 3043–3058.

    Article  Google Scholar 

  2. Khalid, S., Abbas, W. B., Kim, H. S., & Niaz, M. T. (2020). Evolutionary algorithm based capacity maximization of 5G/B5G hybrid precoding systems. Sensors, 20(18), 5338.

    Article  Google Scholar 

  3. Suneetha, N., & Satyanarayana, P. (2023). Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement. International Journal of Communication Systems, 36(5), e5400.

    Article  Google Scholar 

  4. Ravikumar, C. V. (2023). Developing novel channel estimation and hybrid precoding in millimeter-wave communication system using heuristic-based deep learning. Energy, 268, 126600.

    Article  Google Scholar 

  5. Vlachos, E., & Thompson, J. (2020). Energy-efficiency maximization of hybrid massive MIMO precoding with random-resolution DACs via RF selection. IEEE Transactions on Wireless Communications, 20(2), 1093–1104.

    Article  Google Scholar 

  6. Khalid, S., Mehmood, R., Abbas, W., Khalid, F., & Naeem, M. (2021). Probabilistic distribution learning algorithm based transmit antenna selection and precoding for millimeter wave massive MIMO systems. Telecommunication Systems, 76(3), 449–460.

    Article  Google Scholar 

  7. Li, X., Huang, Y., Heng, W., & Wu, J. (2021). Machine learning-inspired hybrid precoding for mmWave MU-MIMO systems with domestic switch network. Sensors, 21(9), 3019.

    Article  Google Scholar 

  8. Sharifi, S., Shahbazpanahi, S., & Dong, M. (2021). A POMDP-based antenna selection for massive MIMO communication. IEEE Transactions on Communications, 70(3), 2025–2041.

    Article  Google Scholar 

  9. Kumar, S., Mahapatra, R., & Singh, A. (2023). Multi-user mmWave massive-MIMO hybrid beamforming: A quantize deep learning approach. In 2023 National Conference on Communications (NCC) (pp. 1–6). IEEE.

  10. Yang, X., Jin, S., Li, G. Y., & Li, X. (2021). Asymmetrical uplink and downlink transceivers in massive MIMO systems. IEEE Transactions on Vehicular Technology, 70(11), 11632–11647.

    Article  Google Scholar 

  11. Yetis, C. M., Björnson, E., & Giselsson, P. (2021). Joint analog beam selection and digital beamforming in millimeter wave cell-free massive mimo systems. IEEE Open Journal of the Communications Society, 2, 1647–1662.

    Article  Google Scholar 

  12. Salh, A., Shah, N.S.M., Audah, L., Abdullah, Q., Abdullah, N., Hamzah, S.A., & Saif, A. (2021). Trade-off energy and spectral efficiency in 5G massive MIMO system. arXiv preprint arXiv:2105.10722.

  13. Liu, P., Li, Y., Cheng, W., Gao, X., & Huang, X. (2021). Intelligent reflecting surface aided NOMA for millimeter-wave massive MIMO with lens antenna array. IEEE Transactions on Vehicular Technology, 70(5), 4419–4434.

    Article  Google Scholar 

  14. Byreddy, A. R., & Logashanmugam, E. (2023). Energy and spectral efficiency improvement using improved shark smell-coyote optimization for massive MIMO system. International Journal of Communication Systems, 36, e5381.

    Article  Google Scholar 

  15. He, Y., & M.S., Zeng, F., Zheng, H., Wang, R., Zhang, M. and Liu, X. (2021). Energy efficient power allocation for cell-free mmWave massive MIMO with hybrid precoder. IEEE Communications Letters, 26(2), 394–398.

    Article  Google Scholar 

  16. Zhang, Y., Cheng, Y., Zhou, M., Yang, L., & Zhu, H. (2020). Analysis of uplink cell-free massive MIMO system with mixed-ADC/DAC receiver. IEEE Systems Journal, 15, 5162–5173.

    Article  Google Scholar 

  17. Yang, J., Zhang, L., Zhu, C., Guo, X., & Zhang, J. (2021). Energy efficiency optimization of massive MIMO systems based on the particle swarm optimization algorithm. Wireless Communications and Mobile Computing, 2021, 1–11.

    Google Scholar 

  18. Bouchibane, F. Z., & Bensebti, M. (2018). Artificial bee colony algorithm for energy efficiency optimization in massive MIMO system. International Journal of Wireless and Mobile Computing, 15(2), 97–104.

    Article  Google Scholar 

  19. Fountoukidis, K. C., Kalialakis, C., Psannis, K. E., Siakavara, K., Goudos, S. K., Sarigiannidis, P., & Obaidat, M. (2018). MIMO antenna selection using biogeography-based optimization with nonlinear migration models. International Journal of Communication Systems, 31(17), e3813.

    Article  Google Scholar 

  20. Ghosh, J., Zhu, H., & Haci, H. (2021). A novel channel model and optimal beam tracking schemes for mobile millimeter-wave massive MIMO communications. IEEE Transactions on Vehicular Technology, 70(7), 7205–7210.

    Article  Google Scholar 

  21. Sheikh, J. A., Mustafa, F., & Sidiq, S. (2022). New hybrid architecture for energy efficient and low complex massive MIMO system. Arabian Journal for Science and Engineering, 47(3), 3141–3149.

    Article  Google Scholar 

  22. Yu, W., Wang, T., & Wang, S. (2021). Multi-label learning based antenna selection in massive MIMO systems. IEEE Transactions on Vehicular Technology, 70(7), 7255–7260.

    Article  Google Scholar 

  23. Chung, M.K., Liu, L., Johansson, A., Gunnarsson, S., Nilsson, M., Ying, Z., Zander, O. et al. (2021). LuMaMi28: Real-time millimeter-wave massive MIMO systems with antenna selection. arXiv preprint arXiv:2109.03273.

  24. Huang, H., Song, Y., Yang, J., Gui, G., & Adachi, F. (2019). Deep-learning-based millimeter-wave massive MIMO for hybrid precoding. IEEE Transactions on Vehicular Technology, 68(3), 3027–3032.

    Article  Google Scholar 

  25. Zhang, X., & Zhao, F. (2021). Hybrid precoding algorithm for millimeter-wave massive MIMO systems with subconnection structures. Wireless Communications and Mobile Computing, 2021, 1–9.

    Article  Google Scholar 

  26. Elbir, A. M. (2020). A deep learning framework for hybrid beamforming without instantaneous CSI feedback. IEEE Transactions on Vehicular Technology, 69(10), 11743–11755.

    Article  Google Scholar 

  27. Ribeiro, L. N., Schwarz, S., Rupp, M., & de Almeida, A. L. F. (2018). Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs. IEEE Journal of Selected Topics in Signal Processing, 12(2), 298–312.

    Article  Google Scholar 

  28. Sennan, S., Somula, R., Luhach, A. K., Deverajan, G. G., Alnumay, W., Jhanjhi, N. Z., Ghosh, U., & Sharma, P. (2021). Energy efficient optimal parent selection based routing protocol for Internet of Things using firefly optimization algorithm. Transactions on Emerging Telecommunications Technologies, 32(8), e4171.

    Article  Google Scholar 

  29. Gao, S., Wang, K., Tao, S., Jin, T., Dai, H., & Cheng, J. (2021). A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models. Energy Conversion and Management, 230, 113784.

    Article  Google Scholar 

  30. Gao, X., Dai, L., Han, S., Chih-Lin, I., & Heath, R. W. (2016). Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays. IEEE Journal on Selected Areas in Communications, 34(4), 998–1009.

    Article  Google Scholar 

  31. Yu, X., Shen, J.-C., Zhang, J., & Letaief, K. B. (2016). Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 10(3), 485–500.

    Article  Google Scholar 

  32. Khalid, S., Mehmood, R., Abbas, W. B., Khalid, F., & Naeem, M. (2022). Energy efficiency maximization of massive MIMO systems using RF chain selection and hybrid precoding. Telecommunication Systems, 80(2), 251–261.

    Article  Google Scholar 

  33. Li, L., Ren, H., Li, X., Chen, W., & Han, Z. (2019). Machine learning-based spectrum efficiency hybrid precoding with lens array and low-resolution ADCs. IEEE Access, 7, 117986–117996.

    Article  Google Scholar 

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Correspondence to Ch V. V. S. Srinivas.

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Srinivas, C.V.V.S., Borugadda, S. RF Chain Selection Using Hybrid Optimization with Precoding in mm-Wave Massive MIMO Systems. Wireless Pers Commun 131, 1997–2017 (2023). https://doi.org/10.1007/s11277-023-10529-7

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