Orthogonal frequency division multiplexing (OFDM) signals have high peak-to-average power ratio (PAPR) and cause large nonlinear distortions in power amplifiers (PAs). Memory effects in PAs also become no longer ignorable for the wide bandwidth of OFDM signals. Digital baseband predistorter is a highly efficient technique to compensate the nonlinear distortions. But it usually has many parameters and takes long time to converge. This paper presents a novel predistorter design using a set of orthogonal polynomials to increase the convergence speed and the compensation quality. Because OFDM signals are approximately complex Gaussian distributed, the complex Hermite polynomials which have a closed-form expression can be used as a set of orthogonal polynomials for OFDM signals. A differential envelope model is adopted in the predistorter design to compensate nonlinear PAs with memory effects. This model is superior to other predistorter models in parameter number to calculate. We inspect the proposed predistorter performance by using an OFDM signal referred to the IEEE 802.11a WLAN standard. Simulation results show that the proposed predistorter is efficient in compensating memory PAs. It is also demonstrated that the proposal acquires a faster convergence speed and a better compensation effect than conventional predistorters.

Nonlinear distortions in power amplifiers (PAs) generate spectral regrowth at the output, which causes interference to adjacent channels and errors in digitally modulated signals. This paper presents a novel method to evaluate adjacent channel leakage power ratio (ACPR) and error vector magnitude (EVM) from the amplitude-to-amplitude (AM/AM) and amplitude-to-phase (AM/PM) characteristics. The transmitted signal is considered to be complex Gaussian distributed in orthogonal frequency-division multiplexing (OFDM) systems. We use the Mehler formula to derive closed-form expressions of the PAs output power spectral density (PSD), ACPR and EVM for memoryless PA and memory PA respectively. We inspect the derived relationships using an OFDM signal in the IEEE 802.11a WLAN standard. Simulation results show that the proposed method is appropriate to predict the ACPR and EVM values of the nonlinear PA output in OFDM systems, when the AM/AM and AM/PM characteristics are known.

The adaptive predistorter and the negative feedback system are known as methods to compensate for the nonlinear distortion of a power amplifier. Although the feedback method is a simple technique, its instability impedes the capability of high-feedback gain to achieve a high-compensation effect. On the other hand, the predistorter requires a long time for convergence of the adaptive predistorters. In this paper, we propose a nonlinear distortion compensation method for a narrow-band signal. In this method, an adaptive predistorter and negative feedback are combined. In addition, to shorten the convergence time to minimize nonlinear distortion, a variable step-size (VS) method is also applied to the algorithm to determine the parameters of the adaptive predistorter. Using computer simulations, we show that the proposed scheme achieves both five times faster convergence speed than that of the predistorter and three times higher permissible delay time in the feedback amplifier than that of a negative feedback only amplifier.

The quality of edge detection is related to detection angle, scale, and threshold. There have been many algorithms to promote edge detection quality by some rules about detection angles. However these algorithm did not form rules to detect edges at an arbitrary angle, therefore they just used different number of angles and did not indicate optimized number of angles. In this paper, a novel edge detection algorithm is proposed to detect edges at arbitrary angles and optimized number of angles in the algorithm is introduced. The algorithm combines singularity detection with Gaussian wavelet transform and edge detection at arbitrary directions and contain five steps: 1) An image is divided into some pixel lines at certain angle in the range from 45° to 90° according to decomposition rules of this paper. 2) Singularities of pixel lines are detected and form an edge image at the certain angle. 3) Many edge images at different angles form a final edge images. 4) Detection angles in the range from 45° to 90° are extended to range from 0° to 360°. 5) Optimized number of angles for the algorithm is proposed. Then the algorithm with optimized number of angles shows better performances.