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The rapid growth of wireless services demands schemes that can simultaneously support high capacity, low latency, and massive connectivity. Non-Orthogonal Multiple Access (NOMA) has emerged as a key enabler for next-generation networks, however its practical performance depends critically on efficient power and subchannel allocation. Two population-based metaheuristics the Genetic Algorithm (GA) and the Grey Wolf Optimizer (GWO) to solve the joint allocation problem in a realistic downlink NOMA setting with explicit multi-cell interference. Candidate solutions encoded user subchannel assignments and power splits, and their fitness was evaluated by a penalized objective that incorporates system throughput, fairness, and power constraints. Simulation experiments under different user densities and interference conditions compared GA and GWO against Orthogonal Multiple Access (OMA). The results showed that GWO consistently outperformed GA, and improved sum throughput by up to 13% over GA and more than 150% over OMA in interference-limited scenarios. GWO also converged nearly twice as fast (28 vs. 50 iterations on average), reduced per-iteration runtime by about 40%, and delivered higher fairness (JFI = 0.89 vs. 0.84) and energy efficiency (87.1 vs. 79.5 Mbps/W). These findings highlight the novelty of our study: a comprehensive comparative evaluation of GA and GWO under an interference-aware, multi-cell NOMA framework. The results confirmed that GWO provides a practical and computationally efficient solution for dense NOMA deployments, while also highlighted directions for future work in mobility-aware and MIMO-enabled networks.

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Efficient Power Allocation in NOMA Networks Using Grey Wolf Optimizer and Genetic Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Hussein Ali Mohammed,
  • Firas Abedi,
  • Zubaidah Saleem Hasan,
  • Ahmed Hussain

摘要

The rapid growth of wireless services demands schemes that can simultaneously support high capacity, low latency, and massive connectivity. Non-Orthogonal Multiple Access (NOMA) has emerged as a key enabler for next-generation networks, however its practical performance depends critically on efficient power and subchannel allocation. Two population-based metaheuristics the Genetic Algorithm (GA) and the Grey Wolf Optimizer (GWO) to solve the joint allocation problem in a realistic downlink NOMA setting with explicit multi-cell interference. Candidate solutions encoded user subchannel assignments and power splits, and their fitness was evaluated by a penalized objective that incorporates system throughput, fairness, and power constraints. Simulation experiments under different user densities and interference conditions compared GA and GWO against Orthogonal Multiple Access (OMA). The results showed that GWO consistently outperformed GA, and improved sum throughput by up to 13% over GA and more than 150% over OMA in interference-limited scenarios. GWO also converged nearly twice as fast (28 vs. 50 iterations on average), reduced per-iteration runtime by about 40%, and delivered higher fairness (JFI = 0.89 vs. 0.84) and energy efficiency (87.1 vs. 79.5 Mbps/W). These findings highlight the novelty of our study: a comprehensive comparative evaluation of GA and GWO under an interference-aware, multi-cell NOMA framework. The results confirmed that GWO provides a practical and computationally efficient solution for dense NOMA deployments, while also highlighted directions for future work in mobility-aware and MIMO-enabled networks.