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Volume 13 | Issue 10 | Year 2026 | Article Id. IJECE-V13I10P104 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I10P104

Joint Clustering, Association, and Power Allocation in Cell-Free Massive MIMO-NOMA

Gurudevi A. Hiremath, Shankar D. Nawale

Received Revised Accepted Published
06 Aug 2026 16 Sep 2026 23 Sep 2026 28 Oct 2026

Citation :

Gurudevi A. Hiremath, Shankar D. Nawale, "Joint Clustering, Association, and Power Allocation in Cell-Free Massive MIMO-NOMA," International Journal of Electronics and Communication Engineering, vol. 13, no. 10, pp. 54-67, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I10P104

Abstract

This study revisits the joint design of user clustering, Access-Point (AP) association, transmit-antenna selection, and power allocation in downlink cell-free massive MIMO with Non-Orthogonal Multiple Access (NOMA). A rigorous Monte-Carlo evaluator with pilot contamination, Minimum Mean-Square-Error (MMSE) channel estimation, maximum-ratio transmission, and Successive Interference Cancellation (SIC) scores every candidate configuration consistently under common random numbers. A central methodological point is emphasized: prior clustering-centric comparisons hold AP association and power fixed, which conflates the clustering effect with an unoptimized association budget. When all schemes are granted an identical association/antenna/power search-a matched-scope comparison over 40 independent topologies—a clear and statistically significant ordering of resource levers emerges: AP association is dominant (+20.2 bps/Hz), clustering optimization is a significant second lever (+13.0 bps/Hz), the clustering algorithm itself contributes a small but significant increment (+4.1 bps/Hz), and transmit-antenna selection is spectrally neutral, all under the parameter regime investigated (Table 3); the ordering is not asserted to hold universally across every cell-free massive MIMO-NOMA configuration. A block-coordinate joint optimizer is proposed and shown, by exhaustive enumeration on small instances (K=9), to attain a mean optimality gap of only 0.64%, a result that is evidence for, but not proof of, near-optimality at the larger K=30 scale used elsewhere in the evaluation; it improves the sum Spectral Efficiency (SE) by 16.0% over the best matched-scope heuristic (and by 54.3% over the conventionally evaluated base method) while converging monotonically in about five iterations. Its discrete power grid is shown to attain 99.3% of a continuous power optimum. Transmit-antenna selection is characterized as an energy-efficiency lever, the energy-optimal configuration activating four of eight antennas for a 14.3% energy-efficiency gain at a 28.2% SE cost.

Keywords

Cell-free massive MIMO, NOMA, User clustering, AP association, Antenna selection, Power allocation, Resource-lever attribution.

References

  1. Hien Quoc Ngo et al., “Cell-Free Massive MIMO versus Small Cells,” IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Emil Björnson, and Luca Sanguinetti, “Scalable Cell-Free Massive MIMO Systems,” IEEE Transactions on Communications, vol. 68, no. 7, pp. 4247-4261, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. Giovanni Interdonato et al., “Ubiquitous Cell-Free Massive MIMO Communications,” EURASIP Journal on Wireless Communications and Networking, vol. 2019, pp. 1-13, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. Elina Nayebi et al., “Cell-Free Massive MIMO Systems,” 2015 49th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, pp. 695-699, 2015.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Yuya Saito et al., “Non-Orthogonal Multiple Access (NOMA) for Cellular Future Radio Access,” 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), Dresden, Germany, pp. 1-5, 2013.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Zhiguo Ding et al., “A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 10, pp. 2181-2195, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. Yuanwei Liu et al., “Nonorthogonal Multiple Access for 5G and Beyond,” Proceedings of the IEEE, vol. 105, no. 12, pp. 2347-2381, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Zhiguo Ding et al., “Application of Non-Orthogonal Multiple Access in LTE and 5G Networks,” IEEE Communications Magazine, vol. 55, no. 2, pp. 185-191, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Manijeh Bashar et al., “On the Performance of Cell-Free Massive MIMO Relying on Adaptive NOMA/OMA Mode-Switching,” IEEE Transactions on Communications, vol. 68, no. 2, pp. 792-810, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. Rabia Arshad, Sobia Baig, and Saad Aslam, “User Clustering in Cell-Free Massive MIMO NOMA System: A Learning Based and User Centric Approach,” Alexandria Engineering Journal, vol. 90, pp. 183-196, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. A.F. Molisch et al., “Capacity of MIMO Systems with Antenna Selection,” IEEE Transactions on Wireless Communications, vol. 4, no. 4, pp. 1759-1772, 2005.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Yuehua Yu et al., “Antenna Selection for MIMO Nonorthogonal Multiple Access Systems,” IEEE Transactions on Vehicular Technology, vol. 67, no. 4, pp. 3158-3171, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Qingjiang Shi et al., “An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel,” IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4331-4340, 2011.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. Kaiming Shen, and Wei Yu, “Fractional Programming for Communication Systems—Part I: Power Control and Beamforming,” IEEE Transactions on Signal Processing, vol. 66, no. 10, pp. 2616-2630, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  15. Haoran Sun et al., “Learning to Optimize: Training Deep Neural Networks for Interference Management,” IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5438-5453, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Yifei Shen et al., “Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 101-115, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. Mark Eisen, and Alejandro Ribeiro, “Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks,” IEEE Transactions on Signal Processing, vol. 68, pp. 2977-2991, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. David Arthur, and Sergei Vassilvitskii, “K-Means++: The Advantages of Careful Seeding,” Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA, pp. 1027-1035, 2007.
    [    Google Scholar] [Publisher Link]
  19. R. Jain, D. Chiu, and W. Hawe, “A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems,” arXiv preprint, pp. 1-38, 1984.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. Haijun Zhang et al., “Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 9, pp. 2074-2085, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. Emil Björnson, Jakob Hoydis, and Luca Sanguinetti, “Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency,” Foundations and Trends in Signal Processing, vol. 11, no. 3-4, pp. 154-655, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]