Q-MOHO: Q-Machine Learning Guided Multi- Objective power optimization for RIS-assisted MIMO-NOMA systems

Autor

  • Suprith P G PESITM, Shivamogga, and Karnataka, India
  • Marulasiddappa H B G M University, Davangere, and Karnataka, India

DOI:

https://doi.org/10.24425/ijet.2026.157929

Keywords

Q: Automated Instruction, Optimization with Various Goals, Distribution of Power, Reconfigurable Intelligent Surface (RIS), MIMO-NOMA, Optimization of Efficiency

Abstrakt

In this study, we introduce Q-MOHO (Q-Machine
Learning Guided Multi-Objective Hybrid Optimization) for power
allocation in MIMO-NOMA communication systems aided by RIS.
To optimize throughput, improve equalization, and reduce power
variation, the algorithm adjusts power distribution among three
users facilitated by 64 RIS components. Dynamically adjusting
with SNR, the optimal power distribution spans from 0.2 at
elevated SNR levels to 0.8 at reduced SNRs. The Q-MOHO
performance of 5.40 bps/Hz at an SNR of 25 dB is substantial when
contrasted with equal power allocation (EPA) and difference of
convex optimization (DCO). These results confirm that, in realworld
wireless conditions, the Q-MOHO algorithm can effectively
acquire optimal strategies and significantly enhance system
performance.

Pobrania

Opublikowane

2026-06-02

Jak cytować

P G, Suprith, i Marulasiddappa H B. „Q-MOHO: Q-Machine Learning Guided Multi- Objective Power Optimization for RIS-Assisted MIMO-NOMA Systems”. International Journal of Electronics and Telecommunications, t. 72, nr 2, czerwiec 2026, s. 1-8, doi:10.24425/ijet.2026.157929.

Numer

Tom 72 Nr 2 (2026)

Dział

Artykuły

Licencja

Prawa autorskie (c) 2026 International Journal of Electronics and Telecommunications

Creative Commons License

Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa 4.0 Międzynarodowe.


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