Similarity-Reduced Intelligent Algorithms for OFDM PAPR Reduction

• Main Authors: Xiao-Qing Su, 蘇筱晴
• Other Authors: Dah-Chung Chang
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/57630580917000397396

碩士 === 國立中央大學 === 通訊工程學系 === 104 === Orthogonal Frequency Division Multiplexing (OFDM) has been widely used in communication fields. One of the major drawbacks of OFDM is high peak-to-average power ratio (PAPR) for the transmitted signals. Therefore, the issue about how to remedy the PAPR in an OFDM system is important. Nowadays, many techniques have been developed to reduce the problem of high PAPR. Partial Transmit Sequence (PTS) is one of the attractive techniques to reduce the PAPR, but PTS requires an exhaustive search for all combinations of allowed phase factors such that the computational complexity is quite high. The sub-optimal algo- rithms that combine the PTS technique and intelligent algorithms have been frequently discussed in the past few years. It can effectively reduce PAPR and significantly decrease computational complexity. As a result, iiithis type of sub-optimal algorithms become a subject that draws a lot of interest. However, there is still some performance gap between the sub-optimal algorithms and the traditional PTS solutions. In this thesis, we propose a new method based on the intelligent algorithms which reduces the sim- ilarity during searching for the phase factors for PTS in order to avoid the solutions falling in local minimum. The proposed method can ef- fectively reduce PAPR though it may increase computational complexity more than the original intelligent algorithms.