Investigators at University of Electronic Science and Technology Detail Findings in Algorithms (Optimal phase searching of PTS using modified genetic algorithm for PAPR reduction in OFDM systems)
By a News Reporter-Staff News Editor at Journal of Mathematics -- A new study on Algorithms is now available. According to news reporting out of Chengdu, People's Republic of China, by VerticalNews editors, research stated, "In this paper, a novel genetic algorithm assisted partial transmit sequence (NGA-PTS) is proposed to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM). However, the search complexity of the optimum PTS (OPTS) scheme is too large for the typical number of sub-blocks."
Our news journalists obtained a quote from the research from the University of Electronic Science and Technology, "Therefore, some artificial intelligence methods, such as genetic algorithm technique, and particle swarm optimization, are introduced to reduce the complexity. As traditional GA-PTS (TGA-PTS) technique risks finding a suboptimal solution, how to avoid this disadvantage of TGA-PTS is an interest topic. In order to obtain a better suboptimal solution, a phase factor optimal pair technique and an abandon/introduction new chromosome technique are proposed in GA here. Simulation results show that the proposed scheme achieves a significant improvement over the TGA-PTS scheme in PAPR. Furthermore, by use of the inherent diversity of constellation for each OFDM candidate, in the receiver part, the proposed scheme enables data recovery without any side information."
According to the news editors, the research concluded: "Simulation results show the efficiency of the proposed scheme."
For more information on this research see: Optimal phase searching of PTS using modified genetic algorithm for PAPR reduction in OFDM systems. Science China-Information Sciences, 2014;57(6):54-64. Science China-Information Sciences can be contacted at: Science Press, 16 Donghuangchenggen North St, Beijing 100717, Peoples R China.
Our news journalists report that additional information may be obtained by contacting Z. Chen, Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 610054, People's Republic of China. Additional authors for this research include S. Zhang, L. Yang, Y.Y. Jia and S.Q. Li.
Keywords for this news article include: Asia, Chengdu, Genetics, Algorithms, People's Republic of China
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