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Intelligent based hybrid precoder for millimetre wave massive MIMO system

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Abstract

The Millimeter-wave and Massive Multiple Input Multiple Output technologies are promising candidates to offer high data rates and system throughputs in the next-generation wireless communication systems. In massive MIMO systems, the precoder plays a leading role to cancel the interference between the data stream and thereby reduce the complexity of the receiver design. In the conventional fully digital precoding scheme, many Radio Frequency chains are essential for every antenna array. Therefore, a hybrid precoder is a feasible solution to reduce the RF chains and improve the antenna array gain by dividing the signal processing into analog and digital precoders. Designing a hybrid precoder is a non-convex optimization problem because the phase shifter in the analog precoder holds hardware constraints. To address this, an intelligent hybrid precoder is designed using deep learning algorithms. In this paper, the Deep Learning framework is incorporated for hybrid precoder as it renovates non-convex problems into a network training process. In this work, the hybrid precoder is based on decomposition techniques such as Uniform Channel Decomposition (UCD) and Generalized Triangular Decomposition method, which is implemented in the training process as it provides equal gain for all subchannels to diminish the inter subchannel interference. The simulation results validate the proposed work is superior in terms of the Bit Error Rate in comparison with other conventional decomposition techniques. The result shows that the deep learning-based hybrid precoder is better than the conventional with a 2 dB improvement between the UCD method and GMD method.

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References

  1. Health, R. W., Gonzalez-Prelcic, N., Rangan, S., Roh, W., & Sayeed, A. (2016). An overview of signal processing techniques for millimeter wave MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 10(3), 436–453.

    Article  Google Scholar 

  2. Ghosh, A., et al. (2014). Millimeter-wave enhanced local area systems: A high data-rate approach for future wireless networks. IEEE Journal of Selection Areas Communication, 32(6), 1152–1163.

    Article  Google Scholar 

  3. Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., Wong, G. N., Schulz, J. K., Samimi, M., & Gutierrez, F. (2013). Millimeter wave mobile communications for 5G cellular: It will work! IEEE Access, 1, 335–349.

    Article  Google Scholar 

  4. Imran Zoha, A., & Abu, A. (2014). Challenges in 5G: How to empower SON with big data for enabling 5G. IEEE Network, 28, 27–33.

    Article  Google Scholar 

  5. Yu, X., et al. (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 

  6. Lu, L., Li, G. Y., Swindlehurst, A. L., Ashikhmin, A., & Zhang, R. (2014). An overview of massive MIMO: Benefits and challenges. IEEE Journal of Selected Topics in Signal Processing, 8, 742–758. https://doi.org/10.1109/JSTSP.2014.2317671

    Article  Google Scholar 

  7. Gao, X., Dai, L., Han, S., I, C. L., & 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. https://doi.org/10.1109/JSAC.2016.2549418

    Article  Google Scholar 

  8. **, J., Zheng, Y. R., Chen, W., & **ao, C. (2018). Hybrid precoding for millimeter wave MIMO systems: A matrix factorization approach. IEEE Transactions on Wireless Communications, 17(5), 3327–3339. https://doi.org/10.1109/TWC.2018.2810072

    Article  Google Scholar 

  9. Chen, C. E., Tsai, Y. C., & Yang, C. H. (2015). An iterative geometric mean decomposition algorithm for MIMO communications systems. IEEE Transactions on Wireless Communications, 14(1), 343–352. https://doi.org/10.1109/TWC.2014.2347051

    Article  Google Scholar 

  10. Jiang, Y., Li, J., & Hager, W. (2005). Joint transceiver design for MIMO communications using geometric mean decomposition. IEEE Transactions on Signal Processing, 53(10), 3791–3803. https://doi.org/10.1109/TSP.2005.855398

    Article  MathSciNet  MATH  Google Scholar 

  11. Jiang, Y., Li, J., & Hager, W. (2005). Uniform channel decomposition for MIMO communications. IEEE Transactions on Signal Processing, 53(11), 4283–4294. https://doi.org/10.1109/TSP.2005.857052

    Article  MathSciNet  MATH  Google Scholar 

  12. Hinton GE, T. Y., & Osindero, S. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527

    Article  MathSciNet  MATH  Google Scholar 

  13. Kato, N., Fadlullah, Z. M., Mao, B., Tang, F., Akashi, O., Inoue, T., & Mizutani, K. (2017). The deep learning vision for heterogeneous networktraffic control: Proposal, challenges, and future perspective. IEEE Wireless Communications, 24(3), 146–153. https://doi.org/10.1109/MWC.2016.1600317WC

    Article  Google Scholar 

  14. Chen, C. H., Tsai, C. R., Liu, Y. H., Hung, W. L., & Wu, A. Y. (2017). Compressive sensing (cs) assisted low-complexity beamspace hybrid precoding for millimeter-wave MIMO systems. IEEE Transactions on Signal Processing, 65(6), 1412–1424. https://doi.org/10.1109/TSP.2016.2641379

    Article  MathSciNet  MATH  Google Scholar 

  15. **e, T., Dai, L., Gao, X., Shakir, M. Z., & Li, J. (2018). Geometric mean decomposition based hybrid precoding for millimeter-wave massive MIMO. China Communications, 15(5), 229–238. https://doi.org/10.1109/CC.2018.8388000

    Article  Google Scholar 

  16. Liu, X., Sun, Q., Lu, W., Wu, C., & Ding, H. (2020). Big-data-based intelligent spectrum sensing for heterogeneous spectrum communications in 5G. IEEE Wireless Communications, 27(5), 67–73. https://doi.org/10.1109/MWC.001.1900493

    Article  Google Scholar 

  17. Liu, X., Sun, C., Zhou, M., Wu, C., Peng, B., & Li, P. (2021). Reinforcement learning-based multislot double-threshold spectrum sensing with Bayesian fusion for industrial big spectrum data. IEEE Transactions on Industrial Informatics, 17(5), 3391–3400. https://doi.org/10.1109/TII.2020.2987421

    Article  Google Scholar 

  18. Liu, X., Ding, H., & Hu, S. (2021). Uplink resource allocation for noma-based hybrid spectrum access in 6G-enabled cognitive internet of things. IEEE Internet of Things Journal, 8(20), 15049–15058. https://doi.org/10.1109/JIOT.2020.3007017

    Article  Google Scholar 

  19. Liu, X., Sun, C., Yu, W., & Zhou, M. (2022). Reinforcement-learning-based dynamic spectrum access for software-defined cognitive industrial Internet of Things. IEEE Transactions on Industrial Informatics, 18(6), 4244–4253. https://doi.org/10.1109/TII.2021.3113949

    Article  Google Scholar 

  20. 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 

  21. Yi Jiang, W. W. H., & Li, J. (2008). The generalized triangular decomposition. Mathematics of Computation, 77, 1037–1056.

    Article  MathSciNet  MATH  Google Scholar 

  22. Huang, Hongji, Song, Yiwei, Yang, Jie, Gui, Guan, & Adachi, Fumiyuki. (2019). Deep-learning-based millimeter-wave massive for hybrid precoding. IEEE Transaction on Vehicular Technology, 68, 3027–3032.

    Article  Google Scholar 

  23. Yang, C.-H., Chou, C.-W., Hsu, C.-S., & Chen, C.-E. (2015). A systolic array based GTD processor with a parallel algorithm. IEEE Transactions on Circuits and Systems I: Regular Papers, 62(4), 1099–1108.

    Article  MathSciNet  MATH  Google Scholar 

  24. Weng, Ching-Chih., Chen, Chun-Yang., & Vaidyanathan, P. P. (2010). Generalized triangular decomposition in transform coding. IEEE Transactions on Signal Processing, 58(2), 566–574.

    Article  MathSciNet  MATH  Google Scholar 

  25. Rajarajeswarie, B., Raj, A., & Sandanalakshmi, R. (2022). Uniform channel decomposition-based hybrid precoding using deep learning. In R. Patgiri, S. Bandyopadhyay, M. D. Borah, & V. Emilia Balas (Eds.), Edge analytics, lecture notes in electrical engineering. (Vol. 869). Springer.

    Google Scholar 

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Rajarajeswarie, B., Sandanalakshmi, R. Intelligent based hybrid precoder for millimetre wave massive MIMO system. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03245-4

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