Orthogonal frequency division multiplexing(OFDM) is an attractive technology to provide immense improvement in wireless transmission capacity but high peak-to-average power ratio(PAPR) is a major drawback of OFDM syst...Orthogonal frequency division multiplexing(OFDM) is an attractive technology to provide immense improvement in wireless transmission capacity but high peak-to-average power ratio(PAPR) is a major drawback of OFDM system.Selected mapping(SLM) scheme has good performance for PAPR reduction.It requires the transmitting data to be multiplied by random phase sequences.However,the sequences are pseudo-random which will decrease the method effectiveness.Exhaustive entropy is introduced in this paper which can identify the strength of random phase sequences property.Then an exhaustive entropy based on SLM method is proposed.The scheme improves the effectiveness of random phase sequences by selecting the larger exhaustive entropy of them.The simulation results show that the PAPR reduction performance is better than that of conventional SLM through this method.展开更多
基金Sponsored by the National Natural Science Foundation of China (Grant No. 61101126)the Postdoctoral Science Foundation of China (Grant No.2011M500664)
文摘Orthogonal frequency division multiplexing(OFDM) is an attractive technology to provide immense improvement in wireless transmission capacity but high peak-to-average power ratio(PAPR) is a major drawback of OFDM system.Selected mapping(SLM) scheme has good performance for PAPR reduction.It requires the transmitting data to be multiplied by random phase sequences.However,the sequences are pseudo-random which will decrease the method effectiveness.Exhaustive entropy is introduced in this paper which can identify the strength of random phase sequences property.Then an exhaustive entropy based on SLM method is proposed.The scheme improves the effectiveness of random phase sequences by selecting the larger exhaustive entropy of them.The simulation results show that the PAPR reduction performance is better than that of conventional SLM through this method.
