An Enhanced Iterative Clipping and Filtering Method Using Time-Domain Kernel Matrix for PAPR Reduction in OFDM Systems

Abstract

Iterative clipping and filtering (ICF) is a straightforward method for reducing the peak-toaverage power ratio (PAPR) of signals in orthogonal frequency-division multiplexing (OFDM) system. Recently, convex optimization has been used to find the optimal filter coefficients that minimize the error vector magnitude (EVM) and meet the PAPR constraint. However, high-computation complexity may be incurred when solving the convex optimization problem. Therefore, we develop an efficient PAPR reduction method that uses the time-domain kernel matrix to generate the PAPR-reduction signal. Besides, we relax the assumption that the clipping noise is a series of uncorrelated parabolic pulses and apply the proposed method to more general cases. Based on the instantaneous observation of clipping noise, the proposed method constructs a simple time-domain kernel matrix and employs the curve fitting approach to optimize the corresponding scaling factors. The simulation results show that the proposed method can achieve very close performance to that using convex optimization in terms of both the PAPR reduction and EVM while the computational cost is reduced greatly. In addition, due to the decrease of iteration numbers and computational complexity, the proposed method is more efficient than some existing clipping and filtering methods. INDEX TERMS OFDM, PAPR reduction, clipping and filtering, time-domain kernel, curve fitting.

A Novel Clipping-Based Method to Reduce Peak-To-Average Power Ratio of OFDM Signals

“…The power of the OFDM symbol is also normalized to 1 too as described in Section 2. At However, according to (13) we can find that there isĉ(n) → α when µ → ∞ , which means that α could not be arbitrarily large, otherwise it cannot be compressed to the desired amplitude. Specifically, there is…”

“…According to (12) and (13), it can be seen that  is a scale factor that causes the signal to be linearly scaled, while  is a compression factor that affects the shape of the compression curve, causing the signal to be nonlinearly scaled. Therefore,  and  together determine the characteristics of the compression function, as shown in Figure 1.…”

“…(3) Clip the amplitude of x(n) according to (8) to get the clipped signal x(n), then calculate the clipping noise c(n) according to (9). (4) Compress the amplitude of c(n) to get the compressed noiseĉ(n) according to (13). (5) Modify the clipped OFDM signal x(n) according to (11) to get the transmission signal x(n).…”

“…Further, they will also have similar level of PAPR reductions, according to the definition of PAPR. However, according to (13) we can find that there is ˆ() cn   when  , which means that  could not be arbitrarily large, otherwise it cannot be compressed to the desired amplitude.…”

“…A number of PAPR reduction techniques have been proposed in the literature. Depending on whether the signal is distorted, these techniques can be classified into two categories: distortion-based techniques and distortionless-based techniques [11][12][13]. The commonly used distortionless-based techniques include selective mapping (SLM), partial transmit sequence (PTS) and so on.…”