Side Information を不要とする Neural Network によるOFDM信号のPAPR抑圧法とそのハードウェア化 [in Japanese] PAPR Reduction of OFDM Signal by Neural Networks without Side Information and its FPGA Implementation [in Japanese]
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A major drawback of orthogonal frequency division multiplexing (OFDM) is the high peak-to-average power ratio (PAPR) of the transmitted signal. PAPR reduction techniques by using neural networks have been proposed to reduce the PAPR problem in OFDM transmitter. These techniques require side information to be transmitted from the transmitter to the receiver in order to recover the original data symbol from the received signal. In this paper, we propose a novel technique to reduce PAPR of OFDM signal. Proposed technique is based on Tone Injection(TI) and dose not use any side information to be transmitted from the transmitter to the receiver. Moreover, the proposed model is designed with VHDL for a FPGA device, and evaluated the performance.
- IEEJ Transactions on Electronics, Information and Systems
IEEJ Transactions on Electronics, Information and Systems 126(11), 1296-1303, 2006-11-01
The Institute of Electrical Engineers of Japan