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

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タイトル別名
  • PAPR Reduction of OFDM Signal by Neural Networks without Side Information and its FPGA Implementation
  • Side Information オ フヨウ ト スル Neural Network ニ ヨル OFDM シンゴウ ノ PAPR ヨクアツホウ ト ソノ ハードウェアカ

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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.

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