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Optimized precoding for massive MU-MIMO systems with KLDA dimension reduction and RNN-crossover GBO algorithm

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Abstract

Nowadays the communication of massive multi-user multiple-input multiple-output (MU-MIMO) takes an important role in wireless systems, as they facilitate the ultra-reliable transmission of data and high performance. In order to sustain massive user equipment (UE) with tremendous reliability and spectral efficiency, more antennas are deployed per base station (BS) in the MU-MIMO system. To overcome such problems, the recurrent neural network (RNN) with crossover-gradient based optimizer (GBO) model called RNN-crossover GBO is proposed for precoding in the MU-MIMO system. However, it is essential to diminish the complexity to attain the maximum sum rate for obtaining the optimal solution. Moreover, the kernel linear discriminant analysis (KLDA) dimensionality reduction technique is employed for mapping high dimensional data into a low dimension by considering a linear combination matrix. In order to obtain the best feature the GBO is employed that predict the optimal solution and restrict falling from the local solution. Furthermore, the crossover-GBO algorithm is applied with the RNN to estimate the output precoding matrix with considerable features to obtain the best search space. The experimental results revealed that the proposed method achieves higher performance with a higher sum rate and shows significant improvement in spectral efficiency (SE) values than the existing methods. SE rises due to the selection of closely associated features. This indicates the robustness and stability of the proposed model.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

\(Q\) :

Transmit power on BS

\(r \in Z^{T \times 1}\) :

Signal vector

\(S\) :

Long-term average spectral efficiency

\(\delta^{2}\) :

Noise variance

\(W_{E}\) :

Number of RF chains

\(M_{bY} \times M_{a}\) :

Beam forming matrix

\(B \in R^{n \times q}\) :

High dimensional data

\(W \in R^{n \times t}\) :

Low dimension data

\(U \in R^{n \times t} \left( {t < < q} \right)\) :

Linear combination matrix

\(Tr\left( \cdot \right)\) :

Matrix’s Trace

\(U\) :

Projection vector

\(n_{j}\) :

Number of samples

\(\overline{{b_{ji} }}\) :

Project data

\(\ker \left( {a,\,a_{j} } \right)\) :

Kernel function

\(\mu\) :

Gaussian distribution

\(y_{b}\) and \(y_{\Re 1}^{n}\) :

Phase scale

References

  1. Song, Z., & Ma, J. (2022). Deep learning-driven MIMO: Data encoding and processing mechanism. Physical Communication, 57, 101976.

    Article  Google Scholar 

  2. Hasan, M. K., Hosain, M. S., Saha, T., Islam, S., Paul, L. C., Khatak, S., Alkhassawneh, H. M., Kariri, E., Ahmed, E., & Hassan, R. (2022). Energy efficient data detection with low complexity for an uplink multi-user massive MIMO system. Computers and Electrical Engineering, 101, 108045.

    Article  Google Scholar 

  3. Sarajlić, M., Rusek, F., Sánchez, J. R., Liu, L., & Edfors, O. (2019). Fully decentralized approximate zero-forcing precoding for massive MIMO systems. IEEE Wireless Communications Letters, 8(3), 773–776.

    Article  Google Scholar 

  4. Li, X., & Alkhateeb, A. (2019). November. Deep learning for direct hybrid precoding in millimeter wave massive MIMO systems. In 2019 53rd Asilomar conference on signals, systems, and computers (pp. 800–805). IEEE.

  5. Zu, K., de Lamare, R. C., & Haardt, M. (2013). Generalized design of low-complexity block diagonalization type precoding algorithms for multiuser MIMO systems. IEEE Transactions on Communications, 61(10), 4232–4242.

    Article  Google Scholar 

  6. Hu, Q., Cai, Y., Shi, Q., Xu, K., Yu, G., & Ding, Z. (2020). Iterative algorithm induced deep-unfolding neural networks: Precoding design for multiuser MIMO systems. IEEE Transactions on Wireless Communications, 20(2), 1394–1410.

    Article  Google Scholar 

  7. Zhu, X., Zhang, X., Zeng, W., & Xie, J. (2020). Deep learning-based precoder design in MIMO systems with finite-alphabet inputs. IEEE Communications Letters, 24(11), 2518–2521.

    Article  Google Scholar 

  8. Bo, Z., Liu, R., Guo, Y., Li, M., & Liu, Q. (2020). December. Deep learning based low-resolution hybrid precoding design for mmWave MISO systems. In 2020 IEEE Globecom Workshops, GC Wkshps (pp. 1–6), IEEE.

  9. 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 

  10. Albreem, M. A., Al Habbash, A. H., Abu-Hudrouss, A. M., & Ikki, S. S. (2021). Overview of precoding techniques for massive MIMO. IEEE Access, 9, 60764–60801.

    Article  Google Scholar 

  11. Zhang, M., Gao, J., & Zhong, C. (2022). A deep learning-based framework for low complexity multiuser MIMO precoding design. IEEE Transactions on Wireless Communications, 21(12), 11193–11206.

    Article  Google Scholar 

  12. Liang, L., Xu, W., & Dong, X. (2014). Low-complexity hybrid precoding in massive multiuser MIMO systems. IEEE Wireless Communications Letters, 3(6), 653–656.

    Article  Google Scholar 

  13. Chen, J. C., Wang, C. J., Wong, K. K., & Wen, C. K. (2015). Low-complexity precoding design for massive multiuser MIMO systems using approximate message passing. IEEE Transactions on Vehicular Technology, 65(7), 5707–5714.

    Article  Google Scholar 

  14. Pavia, J. P., Velez, V., Ferreira, R., Souto, N., Ribeiro, M., Silva, J., & Dinis, R. (2021). Low complexity hybrid precoding designs for multiuser mmWave/THz ultra massive MIMO Systems. Sensors, 21(18), 6054.

    Article  Google Scholar 

  15. Shi, J., Wang, W., Yi, X., Gao, X., & Li, G. Y. (2021). Deep learning-based robust precoding for massive MIMO. IEEE Transactions on Communications, 69(11), 7429–7443.

    Article  Google Scholar 

  16. Liu, X., Li, X., Cao, S., Deng, Q., Ran, R., Nguyen, K., & Tingrui, P. (2019). Hybrid precoding for massive mmWave MIMO systems. IEEE Access, 7, 33577–33586.

    Article  Google Scholar 

  17. Dinh, V. K., Le, M. T., Ngo, V. D., & Ta, C. H. (2020). PCA-aided linear precoding in massive MIMO systems with imperfect CSI. Wireless Communications and Mobile Computing, 2020, 1–9.

    Article  Google Scholar 

  18. Ding, T., Zhao, Y., & Zhang, L. (2021). Hybrid precoding for mmWave massive MU-MIMO systems with overlapped subarray: A modified GLRAM approach. ICT Express, 7(4), 460–467.

    Article  Google Scholar 

  19. Zhang, Y., Lian, Y., Liu, Y., Zhang, Q., Jin, M., & Qiu, T. (2021). Energy-efficient multi-antenna hybrid block diagonalization precoding and combining for Mmwave massive multi-user MIMO systems. IEEE Transactions on Vehicular Technology, 70(10), 10461–10476.

    Article  Google Scholar 

  20. Dong, Y., Gong, C., Zhang, Z., Li, H., Wang, X., & Dai, X. (2022). A low-complexity precoding scheme for downlink massive MU-MIMO systems with low-resolution DACs. Wireless Personal Communications, 125, 3627–3640.

    Article  Google Scholar 

  21. Singh, J., & Kedia, D. (2020). spectral efficient precoding design for multi-cell large MU-MIMO system. IETE Journal of Research, 68(6), 4310–4325.

    Article  Google Scholar 

  22. 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 

  23. Bao, X., Jiang, M., Fang, W., & Zhao, C. (2022). PCQNet: A trainable feedback scheme of precoder for the uplink multi-user MIMO systems. Entropy, 24(8), 1066.

    Article  Google Scholar 

  24. Hu, Q., Liu, Y., Cai, Y., Yu, G., & Ding, Z. (2021). Joint deep reinforcement learning and unfolding: Beam selection and precoding for mmWave multiuser MIMO with lens arrays. IEEE Journal on Selected Areas in Communications, 39(8), 2289–2304.

    Article  Google Scholar 

  25. Ayesha, S., Hanif, M. K., & Talib, R. (2020). Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion, 59, 44–58.

    Article  Google Scholar 

  26. Peng, F., Peng, W., Zhang, C., & Zhong, D. (2019). Iot assisted kernel linear discriminant analysis based gait phase detection algorithm for walking with cognitive tasks. IEEE Access, 7, 68240–68249.

    Article  Google Scholar 

  27. Bucher, S., & Waldschmidt, C. (2020) Advanced noncoherent detection in massive mimo systems via digital beamspace preprocessing. In Telecom (vol. 1, (3), pp. 211–227). MDPI.

  28. Said, M., Houssein, E. H., Deb, S., Alhussan, A. A., & Ghoniem, R. M. (2022). A novel gradient based optimizer for solving unit commitment problem. IEEE Access, 10, 18081–18092.

    Article  Google Scholar 

  29. Deb, S., Abdelminaam, D. S., Said, M., & Houssein, E. H. (2021). Recent methodology-based gradient-based optimizer for economic load dispatch problem. IEEE Access, 9, 44322–44338.

    Article  Google Scholar 

  30. Jiang, Y., Luo, Q., Wei, Y., Abualigah, L., & Zhou, Y. (2021). An efficient binary Gradient-based optimizer for feature selection. Mathematical Biosciences and Engineering, 18(4), 3813–3854.

    Article  Google Scholar 

  31. Ma, G., & Yue, X. (2022). An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method. Engineering Applications of Artificial Intelligence, 113, 104960.

    Article  Google Scholar 

  32. Jiang, W., Strufe, M., & Schotten, H.D. (2020). Long-range MIMO channel prediction using recurrent neural networks. In 2020 IEEE 17th annual consumer communications & networking conference (CCNC) 1–6, IEEE.

  33. Alkhateeb, A. (2019). DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications, 1902.06435.

  34. Kumari, P.R., Chaturvedi, A., Juyal, A., Pant, B., Mydhili, S.K. & Yadav, R. (2022). December. Deep Learning-Based Hybrid System for Multiuser MIMO Systems. In 2022 5th international conference on contemporary computing and informatics (IC3I) (pp. 1596–1601). IEEE.

  35. Singh, R., Khurana, V., Reddy, M.S., Yadav, R., Jangir, R. & Kapila, D. (2022) Wireless communication design using neural networks and deep learning. In 2022 International conference on innovative computing, intelligent communication and smart electrical systems (ICSES) (pp. 1–6). IEEE.

  36. Singh, J., & Kedia, D. (2022). Spectral efficient precoding design for multi-cell large MU-MIMO system. IETE Journal of Research, 68(6), 4310–4325.

    Article  Google Scholar 

  37. Jin, W., Zhang, J., Wen, C. K., & Jin, S. (2023). Model-driven deep learning for hybrid precoding in millimeter wave MU-MIMO system. IEEE Transactions on Communications., 71, 5862–5876.

    Article  Google Scholar 

  38. Zhao, X., Li, M., Liu, Y., Chang, T. H., & Shi, Q. (2023). Communication-efficient decentralized linear precoding for massive MU-MIMO systems. IEEE Transactions on Signal Processing., 71, 4045–4059.

    Article  Google Scholar 

  39. Bobrov, E., Chinyaev, B., Kuznetsov, V., Minenkov, D., & Yudakov, D. (2023). Power allocation algorithms for massive MIMO systems with multi-antenna users. Wireless Networks, 29(8), 3747–3768.

    Article  Google Scholar 

  40. Misso, A., & Kissaka, M. (2024). Pilot contamination mitigation by pilot assignment and adaptive linear precoding for massive MIMO multi-cell systems. Telecommunication Systems, 85, 389–400.

    Article  Google Scholar 

  41. Srinivas, C. V., & Borugadda, S. (2023). RF chain selection using hybrid optimization with precoding in mm-wave massive MIMO systems. Wireless Personal Communications, 131, 1997–2017.

    Article  Google Scholar 

  42. Paranthaman, R. N., Sonker, A., Varalakshmi, S., Madiajagan, M., Daya Sagar, K. V., & Malathi, M. (2024). Reinforcement learning-based model for the prevention of beam-forming vector attacks on massive MIMO system. Optical and Quantum Electronics, 56(1), 44.

    Article  Google Scholar 

  43. Liang, H., Liu, C., Song, Y., Gao, T., & Zou, Y. (2024). Neighbor-based joint spatial division and multiplexing in massive MIMO: User scheduling and dynamic beam allocation. EURASIP Journal on Advances in Signal Processing, 2024(1), 1.

    Article  Google Scholar 

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  1. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

    Srividhya Ramanathan & M. Anto Bennet

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  1. Srividhya Ramanathan
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All agreed on the content of the study. SR and ABM collected all the data for analysis. SR and ABM agreed on the methodology. SR and ABM completed the analysis based on agreed steps. Results and conclusions are discussed and written together. All author read and approved the final manuscript.

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Correspondence to Srividhya Ramanathan.

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Ramanathan, S., Bennet, M.A. Optimized precoding for massive MU-MIMO systems with KLDA dimension reduction and RNN-crossover GBO algorithm. Telecommun Syst 86, 363–381 (2024). https://doi.org/10.1007/s11235-024-01135-4

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