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An optimization-driven model for a hybrid beam forming for massive multiple-input multiple-output

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Abstract

Generally, hybrid digital and analog beam forming (HBF) has recently attracted much well-deserved attention for use in large-scale antenna systems. The power consumption hardware’s cost and complexity can be greatly reduced when using a partially connected HBF rather than a fully connected HBF. Using partially connected HBF significantly decreases the hardware complexity. Hence, an optimization-driven model is proposed for a HBF for massive multiple-input multiple-output to decrease strengthen robustness and interference against direction-of-arrival mismatch. The proposed model is based on optimizing both analog and digital beam forms. To obtain the optimal digital beam, an Improved Social Ski-Driver Algorithm (ISSDA) is proposed, and to obtain the optimal analog beam, analog phase alignment-linear searching (APA-LS) is introduced (ISSDA-APALS). The proposed ISSDA-APALS is evaluated by using MATLAB simulation and contrasted with that of other optimization models such as improved bat algorithm with analog phase alignment-linear searching, Hybrid cuckoo search with salp swarm optimization, Cuckoo Levy-based SSA, Cuckoo search improved elephant herding optimization and particle swarm optimization. The simulation results of the proposed ISSDA-APALS reveals superior performance in case of signal-to-noise ratio, Convergence characteristics, sum-rate comparison, Spectral efficiency, energy efficiency relationship, power consumption, and beam width. The proposed ISDDA-APALS is compared with existing methods like Piecewise dual joint iterative-Baseband piecewise successive approximation, Multi-user- successive interference cancellation, and hybrid –block diagonal (Hy-SBD).

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References

  1. Blogh, J. S., & Hanzo, L. (2002). Third-generation systems and intelligent wireless networking: smart antennas and adaptive modulation. Wiley.

    Book  Google Scholar 

  2. Biswas, R. N., Saha, A., Mitra, S. K., & Naskar, M. K. (2019). Realization of PSO-based adaptive beamforming algorithm for smart antennas. Advances nature-inspired computing applications (1st ed., pp. 135–136). Springer.

    Chapter  Google Scholar 

  3. Shu, F., Qin, Y., Liu, T., Gui, L., Zhang, Y., Li, J., & Han, Z. (2018). Lowcomplexity and high-resolution DOA estimation for hybrid analog and digital massive MIMO receive array. IEEE Transactions on Communications, 66(6), 2487–2501.

    Article  Google Scholar 

  4. Sheikh, J. A., Mustafa, F., Sidiq, S., Parah, S. A., & Bhat, G. M. (2021). A new optimization technique in massive MIMO and LSAS using hybrid architecture and channel estimation algorithm for 5G networks. Wireless Personal Communications, 120(1), 771–785.

    Article  Google Scholar 

  5. Roh, W., Seol, J. Y., Park, J., et al. (2014). Millimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility and prototype results. IEEE Communications Magazine, 52(2), 106–113.

    Article  Google Scholar 

  6. Niu, Y., Li, Y., Jin, D. P., et al. (2015). A survey ofmillimeter wave communications (mmWave) for 5G: Opportunities and challenges. Wireless Networks, 21(8), 2657–2676.

    Article  Google Scholar 

  7. Kaushik, A., Thompson, J., Vlachos, E., Tsinos, C., & Chatzinotas, S. (2019). Dynamic RF chain selection for energy efficient and low complexity hybrid beamforming in millimeter wave MIMO systems. IEEE Transactions on Green Communications and Networking, 3(4), 886–900.

    Article  Google Scholar 

  8. Kaushik, A., Vlachos, E., Tsinos, C., Thompson, J., & Chatzinotas, S. (2020). Joint bit allocation and hybrid beamforming optimization for energy efficient millimeter wave MIMO systems. IEEE Transactions on Green Communications and Networking, 5(1), 119–132.

    Article  Google Scholar 

  9. Lu, A. Y., Chen, Y. F., & Tseng, S. M. (2022). Reduced complexity hybrid beamforming for time-varying channels in millimeter wave MIMO systems. Wireless Personal Communications, 124(3), 2391–2410.

    Article  Google Scholar 

  10. Ali, E., Ismail, M., Nordin, R., & Abdulah, N. F. (2017). Beamforming techniques for massive MIMO systems in 5G: Overview, classification, and trends for future research. Frontiers of Information Technology & Electronic Engineering, 18(6), 753–772.

    Article  Google Scholar 

  11. Subhashini, R., & Satapathy, J. K. (2017). Development of an enhanced antlion optimization algorithm and its application in antenna array synthesis. Appl. Soft Computing, 59, 153–173.

    Article  Google Scholar 

  12. Luyen, T. V., & Giang, T. V. B. (2017). Interference suppression of ULA antennas by phase-only control using bat algorithm. IEEE Antennas Wireless Propagation Letter, 16, 3038–3042.

    Article  Google Scholar 

  13. Ayach, O. E., Rajagopal, S., Abu-Surra, S., Pi, Z., & Heath, R. W. (2014). Spatially sparse pre-coding in millimeter wave MIMO systems. IEEE Transactions Wireless Communications, 13(3), 1499–1513.

    Article  Google Scholar 

  14. Alkhateeb, A., Leus, G., & Heath, R. W. (2015). Limited feedback hybrid pre-coding for multi-user millimeter wave systems. IEEE Transactions Wireless Communications, 14(11), 6481–6494.

    Article  Google Scholar 

  15. Singh, U., & Salgotra, R. (2018). Synthesis of linear antenna array using flower pollination algorithm. Neural Computing and Applications, 29(2), 435–445.

    Article  Google Scholar 

  16. Saxena, P., & Kothari, A. (2016). Optimal pattern synthesis of linear antenna array using grey wolf optimization algorithm. International Journal of Antennas and Propagation. https://doi.org/10.1155/2016/1205970

    Article  Google Scholar 

  17. Saxena, P., & Kothari, A. (2016). Ant lion optimization algorithm to control side lobe level and null depths in linear antenna arrays. AEU-International Journal of Electronics and, 70(9), 1339–1349.

    Google Scholar 

  18. Khodier, M. M., & Christodoulou, C. G. (2005). Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization. IEEE Transactions Antennas Propagation, 53(8), 2674–2679.

    Article  Google Scholar 

  19. Jin, N., & Rahmat-Samii, Y. (2007). Mar). Advances in particle swarm optimization for antenna designs: Real-number, binary, single-objective and multiobjective implementations. IEEE Transactions Antennas Propagation, 55, 556–567.

    Article  Google Scholar 

  20. Zhang, C., Fu, X., Leo, L., Peng, S., & Xie, M. (2018). Synthesis of broadside linear aperiodic arrays with sidelobe suppression and null steering using whale optimization algorithm. IEEE Antennas Wireless Propagation Letter, 17(2), 347–350.

    Article  Google Scholar 

  21. Asim, F. E., Antreich, F., Cavalcante, C. C., de Almeida, A. L., & Nossek, J. A. (2020). Channel parameter estimation for millimeter-wave cellular systems with hybrid beamforming. Signal Processing, 176, 107715.

    Article  Google Scholar 

  22. Priya, T. S., Manish, K., & Prakasam, P. (2021). Hybrid beamforming for massive MIMO using rectangular antenna array model in 5G wireless networks. Wireless Personal Communications, 120(3), 2061–2083.

    Article  Google Scholar 

  23. Chen, J., Feng, W., Xing, J., Yang, P., Sobelman, G. E., Lin, D., & Li, S. (2020). Hybrid beamforming/combining for millimeter wave MIMO: A machine learning approach. IEEE Transactions on Vehicular, 69(10), 11353–11368.

    Article  Google Scholar 

  24. Dilli, R. (2021). Performance analysis of multi user massive MIMO hybrid beamforming systems at millimeter wave frequency bands. Wireless Networks, 27(3), 1925–1939.

    Article  Google Scholar 

  25. Jiang, J., Li, Y., Chen, L., Du, J., & Li, C. (2020). Multi-task deep learning-based multi-user hybrid beamforming for mm-wave orthogonal frequency division multiple access systems. Science China Information Sciences, 63(8), 1–11.

    Article  Google Scholar 

  26. Islam, M.A., Alexandropoulos, G.C., and Smida, B. (2022). Integrated sensing and communication with millimeter wave full duplex hybrid beamforming. arXiv preprint arXiv, 2201.05240.

  27. Zhang, Y., Du, J., Chen, Y., Li, X., Rabie, K. M., & Khkrel, R. (2020). Dual-iterative hybrid beamforming design for millimeter-wave massive multi-user MIMO systems with sub-connected structure. IEEE Transactions on Vehicular Technology, 69(11), 13482–13496.

    Article  Google Scholar 

  28. Zhang, Y., Du, J., Chen, Y., Han, M., & Li, X. (2019). Optimal hybrid beamforming design for millimeter-wave massive multi-user MIMO relay systems. IEEE Access, 7, 157212–157225.

    Article  Google Scholar 

  29. Wu, X., Liu, D., & Yin, F. (2018). Hybrid beamforming for multi-user massive MIMO systems. IEEE Transactions on Communications, 66(9), 3879–3891.

    Article  Google Scholar 

  30. Osama, A., Ahmed, O.Z., Wang, J., and Zhu, H. (2017). Hybrid digital-to-analog pre-coding design for mm-wave systems. In: 2017 IEEE International Conference on Communications (ICC), 1–6. IEEE.

  31. Almagboul, M. A., Shu, F., Qin, Y., Zhou, X., Wang, J., Qian, Y., Zou, K. J., & Abdelgader, A. M. S. (2019). An efficient hybrid beamforming design for massive MIMO receive systems via SINR maximization based on an improved bat algorithm. IEEE Access, 7, 136545–136558.

    Article  Google Scholar 

  32. Devi, R. P., & Prabakaran, N. (2022). Hybrid cuckoo search with salp swarm optimization for spectral and energy efficiency maximization in NOMA system. Wireless Personal Communications, 124(1), 377–399.

    Article  Google Scholar 

  33. Velusamy, Y., Manickam, R., Chinnaswamy, S., Eanoch, G. J., Yesudhas, H. R., Kumar, R., & Long, H. V. (2021). Adaptive beam formation and channel allocation using substance near multicast protocol and CS-iEHO. Soft Computing, 25(6), 4663–4676.

    Article  Google Scholar 

  34. Chen, Y., Huang, Y., Li, C., Hou, T., & Lou, W. (2022). Turbo-HB: a sub-millisecond hybrid beamforming design for 5G mmwave systems. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/TMC.2022.3152480

    Article  Google Scholar 

  35. Chen, B. Y., Chen, Y. F., & Tseng, S. M. (2022). Hybrid beamforming and data stream allocation algorithms for power minimization in multi-user massive MIMO-OFDM systems. IEEE Access, 10, 101898–101912.

    Article  Google Scholar 

  36. Li, R., Guo, B., Tao, M., Liu, Y. F., & Yu, W. (2022). Joint design of hybrid beamforming and reflection coefficients in RIS-aided mmWave MIMO systems. IEEE Transactions on Communications, 70(4), 2404–2416.

    Article  Google Scholar 

  37. 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 

  38. Elbir, A. M., Mishra, K. V., Shankar, M. B., & Ottersten, B. (2021). A family of deep learning architectures for channel estimation and hybrid beamforming in multi-carrier mm-wave massive MIMO. IEEE Transactions on Cognitive Communications and Networking, 8, 642–656.

    Article  Google Scholar 

  39. Elbir, A.M., & Mishra, K.V. (2019). Deep learning strategies for joint channel estimation and hybrid beamforming in multi-carrier mm-Wave massive MIMO systems. arXiv preprint arXiv:1912.10036.

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Authors and Affiliations

  1. Rayat Institutes Research & Development, Satara, India

    Minal K. Pawar

  2. Department of Technology, Shivaji University, Kolhapur, Maharashtra, 416004, India

    Minal K. Pawar

  3. Department of Electronics Engineering, K.B.P. College of Engineering, Satara, Maharashtra, 415001, India

    Suhas S. Patil

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Pawar, M.K., Patil, S.S. An optimization-driven model for a hybrid beam forming for massive multiple-input multiple-output. Wireless Netw 29, 3367–3382 (2023). https://doi.org/10.1007/s11276-023-03380-y

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