April 26, 2024
Mehdi Hosseinzadeh Aghdam

Mehdi Hosseinzadeh Aghdam

Academic rank: Associate professor
Address: Velayat Highway, Bonab, Iran
Education: Ph.D in Computer Engineering-Artificial Intelligence
Phone: 041-37741636
Faculty: Faculty of Engineering
Department: Computer Engineering

Research

Title
A novel hybrid genetic algorithm to reduce the peak-to-average power ratio of OFDM signals
Type Article
Keywords
PTS, PAPR, Hybrid genetic algorithm (HGA), OFDM
Researchers Abbas Ali Sharifi، Mehdi Hosseinzadeh Aghdam

Abstract

Orthogonal frequency division multiplexing (OFDM) is an effective approach for high rate data transmission in telecommunication systems. The high peak-to-average power ratio (PAPR) is one of the main drawbacks of the OFDM systems. Partial transmit sequence (PTS) is an efficient method to decrease the PAPR of OFDM signals. But, the PTS technique needs an exhaustive search over all combinations of phase rotation factors and the com- putational complexity grows exponentially with the number of sub-blocks. To reduce the search complexity, we introduce a novel hybrid genetic algorithm (HGA) in the PTS tech- nique. The important aspect of the proposed method is the choice of salient phase factors to reduce the high PAPR. The proposed HGA-PTS approach incorporates novel local search operations that are designed and embedded in the genetic algorithm to explore the opti- mum phase factors. Numerical simulations are performed for 16-QAM modulated symbols and the obtained results show that the HGA-PTS significantly reduces both the PAPR and search complexity.