Compensation for Nonlinear Distortion in OFDM Systems Using a Digital Predistorter Based on the Canonical PWL Model

Canonical PWL 모델 기반의 디지털 사전왜곡기를 이용한 OFDM 시스템의 비선형 왜곡 보상

  • 서만중 (숭실대학교 정보통신공학과 전송보상 연구실) ;
  • 심희성 (숭실대학교 정보통신공학과 전송보상 연구실) ;
  • 임성빈 (숭실대학교 정보통신공학과 전송보상 연구실) ;
  • 정재호 (한국전자통신연구원) ;
  • 이광천 (한국전자통신연구원)
  • Published : 2010.01.31

Abstract

Orthogonal frequency division multiplexing (OFDM) is an attractive technique for achieving high-bit-rate wireless data transmission. However, multicarrier systems such as OFDM show great sensitivity to nonlinear distortion. The OFDM structure requires a summation of a large number of subcarriers for multicarrier modulation, and as a result of this summation large signal envelope fluctuations occur. These fluctuations make OFDM systems to be very sensitive to nonlinear distortion introduced by the high power amplifier (HPA) at the transmitter. In this paper, we propose a canonical piecewise-linear (CPWL) model based digital predistorter to compensate for nonlinear distortion introduced by the high peak-to-average power ratio (PAPR) and the HPA in OFDM systems. The performance of the new predistortion scheme for OFDM systems is evaluated in terms of total degradation (TD) and bit error rate (BER). The simulation results demonstrated that the proposed predistorter achieves significant performance improvement by effectively compensating for the nonlinear distortion introduced by the HPA.

OFDM (Orthogonal Frequency Division Multiplexing) 시스템은 고속의 무선 데이터 전송이 가능한 기술이다. 하지만 OFDM과 같은 다중 반송파 시스템은 비선형 왜곡에 매우 민감하다. OFDM 시스템에서는 심볼들을 다중 반송파를 이용하여 변조 후 더하여 전송함으로써 전송 신호 진폭의 변화가 매우 심하다. 이러한 신호 진폭의 변화와 송신기 내에 사용되는 고출력 증폭기 (High Power Amplifier; HPA)의 비선형 특성으로 인해 심각한 비선형 왜곡을 겪게 된다. 본 논문에서는 OFDM 시스템의 높은 PAPR (Peak-to-Average Power Ratio)과 HPA에 의한 비선형 왜곡을 보상하기 위해 CPWL (Canonical Piecewise-Linear) 모델 기반의 새로운 디지털 사전왜곡기를 제안한다. 모의실험을 통하여 새로운 사전왜곡 기법의 성능을 TD (Total Degradation)와 비트오율 (Bit Error Ratio; BER) 측면에서 평가한 결과, HPA에 의해 발생하는 비선형 왜곡을 효과적으로 보상함으로써, 우수한 성능 향상이 있음을 확인하였다.

Keywords

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