Time Reduction for SLM OFDM PAPR Based on Adaptive Genetic Algorithm in 5G IoT Networks

Area of Science:

• Electrical Engineering
• Signal Processing
• Wireless Communications

Background:

• Orthogonal Frequency Division Multiplexing (OFDM) systems suffer from high Peak-to-Average Power Ratio (PAPR).
• Reducing PAPR is crucial for improving the power efficiency and performance of OFDM systems.
• Existing methods like conventional genetic algorithm (GA) based Selective Mapping (SLM) offer improvements but have limitations in time efficiency and PAPR reduction.

Purpose of the Study:

• To introduce a novel adaptive genetic algorithm (AGA) strategy for PAPR and time reduction in SLM-OFDM.
• To evaluate the effectiveness of the proposed AGA-based SLM-OFDM technique compared to conventional SLM-OFDM and GA-SLM-OFDM.
• To demonstrate significant improvements in both PAPR reduction and computational time.

Main Methods:

• Implementation of a new Peak Average Power and Time Reduction (PAPTR) strategy utilizing an adaptive genetic algorithm (AGA).
• Comparative simulations of the proposed AGA-SLM-OFDM against standard SLM-OFDM and conventional GA-SLM-OFDM.
• Performance evaluation based on PAPR reduction and learning time metrics.

Main Results:

• The AGA technique reduced PAPR by approximately 3.87 dB compared to standard SLM-OFDM.
• A substantial learning time reduction of about 95.56% was achieved with AGA-SLM-OFDM versus traditional GA-SLM-OFDM.
• The proposed AGA-SLM-OFDM showed a PAPR enhancement of around 3.87 dB over traditional OFDM.

Conclusions:

• The proposed AGA strategy effectively enhances PAPR reduction in SLM-OFDM systems.
• AGA significantly accelerates the learning time, making it more computationally efficient than GA-SLM-OFDM.
• The AGA-based approach offers a promising solution for improving the efficiency of OFDM communication systems.