2025/11/01 by Gopal K Sharma, Gopal K sharma, Vineeta Saxena Nigam +2
Engineering · #Advanced Wireless Communication Technologies #PAPR reduction in OFDM #Telecommunications and Broadcasting Technologies
paper · doi:10.1088/1402-4896/ae1c75
Abstract With the emergence of Fifth-generation (5G) wireless communication systems, the demand for advanced multiple access techniques capable of supporting high data rates and massive connectivity has become increasingly critical. Power Domain Non-Orthogonal Multiple Access (PD-NOMA), when integrated with universal Filtered Multi-Carrier (UFMC), offers a promising solution by combining spectral efficiency, user fairness, and robustness against inter-subband interference. However, existing PD-NOMA and UFMC studies often rely on static or heuristic power allocation strategies that fail to adapt to dynamic channel conditions and fairness constraints, limiting their effectiveness in real-time 5G deployments.To address this gap, this paper proposes a technologically enhanced, adaptive power allocation framework based on intelligent meta-heuristic optimization, specifically the Genetic algorithm (GA) and the Moth Flame Optimization (MFO) algorithm. These algorithms are tailored to the PD-NOMA-UFMC context by incorporating user-specific channel state information and bounded gain equalization constraints, enabling dynamic and fair power distribution. The objective is to maximize system throughput and minimize Bit Error Rate (BER) by dynamically allocating power to users based on their instantaneous channel state information. Comprehensive simulation results validate the effectiveness of the proposed algorithms, demonstrating significant improvements in throughput and BER performance.GA exhibits faster convergence, while MFO achieves superior solution quality. A comparative analysis highlights the trade-offs between GA and MFO in terms of convergence behaviour, fairness optimization, and computational efficiency.