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AI Based Power Allocation for NOMA

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Abstract

Novel methods using artificial intelligence for downlink power allocation problem in non-orthogonal multiple access networks are presented. The proposed machine learning and deep learning based methods achieved performance close to the optimum in terms of sum capacity with significantly lower computational costs. The numerical results also demonstrated up to 120 times a boost in computation time as compared to the conventional exhaustive search approach. Furthermore, the training and testing accuracy of the deep learning model reached 0.92 and 0.93 with the loss value dropping up to 0.002.

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Availability of data and material

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The source code developed during the current study is available from the corresponding author on reasonable request.

References

  1. Andrews, J. G., & Meng, T. H. (2003). Optimum power control for successive interference cancellation with imperfect channel estimation. IEEE Transactions on Wireless Communications, 2(2), 375–383.

    Article  Google Scholar 

  2. Benjebbovu, A., Li, A., Saito, Y., Kishiyama, Y., Harada, A., & Nakamura, T. (2013). System-level performance of downlink NOMA for future LTE enhancements. In 2013 IEEE globecom workshops (GC Wkshps) (pp. 66–70). IEEE.

  3. Wang, K., Liang, W., Yuan, Y., Liu, Y., Ma, Z., & Ding, Z. (2019). User clustering and power allocation for hybrid non-orthogonal multiple access systems. IEEE Transactions on Vehicular Technology, 68(12), 12052–12065.

    Article  Google Scholar 

  4. He, J., Tang, Z., & Che, Z. (2016). Fast and efficient user pairing and power allocation algorithm for non-orthogonal multiple access in cellular networks. Electronics Letters, 52(25), 2065–2067.

    Article  Google Scholar 

  5. Ali, M. S., Tabassum, H., & Hossain, E. (2016). Dynamic user clustering and power allocation for uplink and downlink non-orthogonal multiple access (NOMA) systems. IEEE Access, 4, 6325–6343.

    Google Scholar 

  6. Zhang, Y., Wang, X., & Xu, Y. (2019). Energy-efficient resource allocation in uplink noma systems with deep reinforcement learning. In 2019 11th international conference on wireless communications and signal processing (WCSP) (pp. 1–6). IEEE.

  7. He, C., Hu, Y., Chen, Y., & Zeng, B. (2019). Joint power allocation and channel assignment for NOMA with deep reinforcement learning. IEEE Journal on Selected Areas in Communications, 37(10), 2200–2210.

    Article  Google Scholar 

  8. Luo, J., Tang, J., So, D. K., Chen, G., Cumanan, K., & Chambers, J. A. (2019). A deep learning-based approach to power minimization in multi-carrier NOMA with SWIPT. IEEE Access, 7, 17450–17460.

    Article  Google Scholar 

  9. Rezwan, S., & Choi, W. (2021). Priority-based joint resource allocation with deep q-learning for heterogeneous NOMA systems. IEEE Access, 9, 41468–41481.

    Article  Google Scholar 

  10. Zhang, H., Zhang, H., & Karagiannidis, G. K. (2020). Deep learning based radio resource management in NOMA networks: User association, subchannel and power allocation. IEEE Transactions on Network Science and Engineering, 7(4), 2406–2415.

    Article  MathSciNet  Google Scholar 

  11. de Sena, A. S., Lima, F. R. M., da Costa, D. B., Ding, Z., Nardelli, P. H., Dias, U. S., & Papadias, C. B. (2020). Massive MIMO-NOMA networks with imperfect SIC: Design and fairness enhancement. IEEE Transactions on Wireless Communications, 19(9), 6100–6115.

    Article  Google Scholar 

  12. Manglayev, T., Kizilirmak, R. C., & Kho, Y. H. (2016). Optimum power allocation for non-orthogonal multiple access (NOMA). In 2016 IEEE 10th international conference on application of information and communication technologies (AICT) (pp. 1–4). IEEE.

  13. Hanif, M. F., Ding, Z., Ratnarajah, T., & Karagiannidis, G. K. (2015). A minorization–maximization method for optimizing sum rate in the downlink of non-orthogonal multiple access systems. IEEE Transactions on Signal Processing, 64(1), 76–88.

    Article  MathSciNet  Google Scholar 

Download references

Funding

Funding was partly provided by Nazarbayev University School of Engineering.

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Authors and Affiliations

  1. School of Engineering and Digital Sciences, Nazarbayev University, Z05H0P9, Nur-Sultan, Kazakhstan

    Talgat Manglayev & Refik Caglar Kizilirmak

  2. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, 6140, New Zealand

    Yau Hee Kho

  3. Laboratory of Computational Sciences and Mathematical Physics, Institute for Mathematical Research, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia

    Nor Asilah Wati Abdul Hamid

  4. Fujian Key Laboratory of Communication, Network and Information Processing, Xiamen University of Technology, Xiamen, China

    Yue Tian

Authors
  1. Talgat Manglayev
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  2. Refik Caglar Kizilirmak
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  3. Yau Hee Kho
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  4. Nor Asilah Wati Abdul Hamid
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  5. Yue Tian
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Corresponding author

Correspondence to Talgat Manglayev.

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Conflict of interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Manglayev, T., Kizilirmak, R.C., Kho, Y.H. et al. AI Based Power Allocation for NOMA. Wireless Pers Commun 124, 3253–3261 (2022). https://doi.org/10.1007/s11277-022-09511-6

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  • DOI: https://doi.org/10.1007/s11277-022-09511-6

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