A joint approach for PAPR reduction and predistortion by adding signal in cognitive radio

Multi-carrier and multi-standard systems are prone to high Peak-to-Average Power Ratio (PAPR). Due to the nonlinearity of the high Power Amplifier (PA), this results in interferences and/or low power efficiency. In this paper, we propose to use a joint approach for PAPR reduction and memoryless predistortion by adding signal in order to improve PA linearity and efficiency performances depending on the radio environment. First, the radio environment is sensed and informations such as signal´s PAPR, channel estimation, Signal-to-Noise Ratio (SNR) and battery level are collected. Accordingly, a decision engine updates additional signals for PAPR reduction and predistortion in order to meet targeted linearity and power efficiency requirements. Ideally suited for Cognitive Radio (CR) systems, this dynamic joint approach by adding signal is simulated and validated through two scenarios represented on two examples of radio environment. The PAPR reduction performance is evaluated by the Complementary Cumulative Density Function (CCDF) and the PA linearity by Error Vector Magnitude (EVM) criteria.