The Research of Companding Transform for Peak-to-Average Power Ratio Reduction of OFDM Signals

2011 ◽  
Vol 128-129 ◽  
pp. 1484-1488
Author(s):  
Xiu Yan Zhang ◽  
Wei Dong Wang ◽  
Lei Lei Pei ◽  
Yu Bo Duan
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Average Power ◽  
Original System ◽  
Power Ratio ◽  
Transform Method ◽  
Ofdm Signal ◽  
Power Ratio Reduction ◽  
Papr Performance ◽  
Ofdm Signals ◽  
Ratio Reduction

One of the main drawbacks of orthogonal frequency division multiplexing (OFDM) is the high peak-to-average power ratio (PAPR) of the OFDM signal. How to reduce the PAPR is the key to applying the technology of OFDM efficiently. In this paper, companding transform method is presented. The performance of the systerm BER is analysised, then PAPR is reduced by using the method through simulation. Simulation results have shown that the proposed methods are very effective and flexible to improve PAPR performance in the OFDM based on the performance of the system BER is not changed very much compare to the original system.

scholarly journals A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

2016 ◽  
Vol 14 (1) ◽  
pp. 705-722 ◽  
Author(s):  
Sotirios K. Goudos
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Average Power ◽  
Papr Reduction ◽  
Power Ratio ◽  
Ofdm Systems ◽  
Major Drawback ◽  
A New Technique ◽  
Power Ratio Reduction ◽  
Ofdm Signals ◽  
Ratio Reduction

AbstractA major drawback of orthogonal frequency division multiplexing (OFDM) signals is the high value of peak to average power ratio (PAPR). Partial transmit sequences (PTS) is a popular PAPR reduction method with good PAPR reduction performance, but its search complexity is high. In this paper, in order to reduce PTS search complexity we propose a new technique based on biogeography-based optimization (BBO). More specifically, we present a new Generalized Oppositional Biogeography Based Optimization (GOBBO) algorithm which is enhanced with Oppositional Based Learning (OBL) techniques. We apply both the original BBO and the new Generalized Oppositional BBO (GOBBO) to the PTS problem. The GOBBO-PTS method is compared with other PTS schemes for PAPR reduction found in the literature. The simulation results show that GOBBO and BBO are in general highly efficient in producing significant PAPR reduction and reducing the PTS search complexity.

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