Orthogonal frequency division multiplexing (OFDM) is an important technology that has been widely used in broadband wireless communications. However OFDM has some drawbacks, one of which is the high peak-to-average power ratio (PAPR) problem. Many techniques for reducing PAPR thus have been proposed in the literatures, where clipping and companding are effective techniques and have gained a lot of research interest. Based on the study of clipping and companding, this paper proposes a hybrid approach to reduce the PAPR of OFDM signals. An iterative clipping and filtering (ICF) technique and an enhanced nonlinear companding (ENC) scheme are used as the typical clipping and companding techniques in this paper. The results of the analysis show that the computational complexity of the proposed method is slightly higher than that of ENC, but much lower than that of ICF. And the simulation results show that the proposed method has better performance on PAPR reduction and bit error rate (BER) in contrast to ICF. On the other hand, compared to ENC with equal PAPR reduction, the proposed method still achieves better performance on BER.

Orthogonal frequency division multiplexing (OFDM) is a widely used technology for wireless broadband communications. However, it also suffers from some drawbacks. One of the critical limitations is the problem of high peak-to-average power ratio (PAPR), which causes distortions of some nonlinear components such as power amplifiers. A number of techniques have been proposed to reduce the PAPR of OFDM signals, among which the clipping-based methods have gained a lot of attention due to the effective PAPR reduction and simplicity of implementation. This paper proposes a novel clipping-based method to reduce the PAPR of OFDM signals. Based on the recently proposed clipping noise compression (CNC) method, the proposed scheme introduces a preset normalization factor to replace the calculation of average amplitude of clipping noise in the original CNC method during compression processing. Comparative simulations were carried out, and the results exhibit that the proposed method achieves better bit-error-ratio performance with equal level of PAPR reduction compared to the original CNC method.

The application of orthogonal frequency division multiplexing (OFDM) has become more and more widespread in recent years. However, the high peak-to-average power ratio (PAPR) problem is still one of the critical issues in the implementation of OFDM systems. Among the techniques of PAPR reduction, iterative clipping and filtering (ICF) is widely used due to its easy implement, and many ICF-based methods have been proposed recently. The optimized ICF (OICF) further has been designed with an optimized filter and achieves faster convergence rate. And the simplified OICF (SOICF) applies Lagrange multiplier method to simplify the problem solving as compared to OICF. The approach saves SOICF from using complicated convex optimization methods. Most recently the clipping-noise compression (CNC) method is proposed to reduce the required number of Fourier transforms by compressing the clipping noise in the time domain. Based on SOICF, this paper adopts clipping and noise compression to replace the calculation of PAPR-reduction vector in each iteration. Analysis shows that the computational complexity of the proposed scheme is lower than SOICF. In addition, we have improved the CNC technique and introduced a compression threshold in the proposed scheme. In each iteration, only the components whose amplitude exceeds the compression threshold are compressed, while the others remain unchanged. To this end, this reduces the amount of information lost, resulting in better BER performance. The simulation results show that the proposed scheme has better BER performance than SOICF, and better PAPR reduction than CNC. When equal amounts of PAPR reduction by the evaluated methods are actually achieved, the BER performance of the proposed scheme is better than SOICF and CNC, especially with high-order modulation or lower clipping ratio.

In this paper, a one-step model extraction method is proposed for parameter identification of two-box nonlinear model of power amplifiers. In order to simplify the extraction complexity, the Wiener and Hammerstein-based serial structure is replaced by a parallel structure. The dynamic nonlinear filter curve and the static piecewise function formed by linear interpolating LUT entry are jointly curve-fitted to identify the parameters of the two-box model in one step. Compared with the traditional two-box model extraction method, the resultant data shows a comparable linearization performance of the proposed model in suppressing the adjacent channel error power with simplified procedures.

This letter proposes a low-complexity suboptimal two-dimensional crest factor reduction (2D-CFR) scheme for dual-band signals based on noise segment cancellation, which is approximated by combining the pulse kernel signals of two bands with the dual-band combining effect being taken into account. The proposed method reduces the calculation of fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) due to requiring no filtering. At the end of the letter, the computational complexity and performance of the algorithm are analysed and compared. The numerical results show that the computational complexity of the proposed solution is significantly reduced with similar error vector magnitude(EVM) performance as compared to traditional methods.

Currently, peak-to-average power ratio (PAPR) degrades the performance of the wireless system and has been widely studied in the orthogonal frequency division multiplexing (OFDM) systems. However, there is less work concerning about this issue in the dual-band signal wireless communication system, which still hinders the wide applications of the system. This paper aims to develop a new twodimensional sector crest factor reduction (2D-SCFR) method to solve this problem in the dual-band signal system by reducing a certain amount of dual-band signal amplitude. In practice, we formulate a combined PAPR of the dual-band signals with the independent Gaussian distribution to evaluate the PAPR value of the dual-band signals. Further, as to the peak detection process, this proposed method introduces a continuous piecewise function, which determines the reduction amount based on signal amplitude to avoid overreduction. To evaluate the performance of the proposed clipping technique, crest factor reduction (CFR) algorithms are applied as the benchmark solution with different dual-band input signal combinations. The numerical results show that the proposed method significantly improves the signal quality of error vector magnitude (EVM), as compared to other CFR algorithms.