A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems

© 2015 IEEE. Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms

A Low Complexity Partial Transmit Sequence for Peak to Average Power Ratio Reduction in OFDM Systems

Partial transmit sequence (PTS) is one of the most important techniques for reducing the peak to average power ratio (PAPR) in OFDM systems. This paper presents a low complexity PTS scheme by applying a new phase sequence. Unlike the conventional PTS which needs several inverse fast Fourier transform (IFFT) operations, the proposed technique requires half IFFT operations only at the expense of slight PAPR degradation. Simulation and results are examined with QPSK modulation and OFDM signal and power amplifier with memory effects

Efficient PAPR reduction of OFDM signal using PTS technique with hybrid partitioning method

The high peak-to-average power ratio (PAPR) is one of the major problems of orthogonal frequency division multiplexing (OFDM) systems in wireless transmission.Therefore, partial transmit sequence (PTS), a promising scheme that can provide good PAPR reduction performance, has been proposed for OFDM transmission to eliminate distortion. The PTS method divides the input data block into disjoint sub-blocks, computes Inverse Fourier Transform of the subblocks, rotates the sub-blocks with appropriate phase factors and combines them to form the transmitted signal.This paper presents an enhanced PTS approach that combines two PTS partitioning schemes (adjacent and interleaved) to effectively reduce the PAPR of the OFDM systems. The influence of the proposed approach on performance is investigated by varying the size of the disjoint sub-blocks.The PAPR reduction performance of the proposed PTS method is compared with two well known sub-blocks partitioning schemes, namely, Adjacent Partitioning (AP), Interleaved Partitioning (IP).The various computer simulation results for the various sub-blocks confirmed that the proposed method provides better PAPR reduction performance compared with AP and IP partitioning based PTS scheme. In addition, these PTS schemes largely depend on the chosen size of the partitions

EFFICIENT PAPR REDUCTION OF OFDM SIGNAL USING PTS TECHNIQUE WITH HYBRID PARTITIONING METHOD

ABSTRACT The high peak-to-average power ratio (PAPR) is one of the major problems of orthogonal frequency division multiplexing (OFDM) systems in wireless transmission. Therefore, partial transmit sequence (PTS), a promising scheme that can provide good PAPR reduction performance, has been proposed for OFDM transmission to eliminate distortion. The PTS method divides the input data block into disjoint sub-blocks, computes Inverse Fourier Transform of the subblocks, rotates the sub-blocks with appropriate phase factors and combines them to form the transmitted signal. This paper presents an enhanced PTS approach that combines two PTS partitioning schemes (adjacent and interleaved) to effectively reduce the PAPR of the OFDM systems. The influence of the proposed approach on performance is investigated by varying the size of the disjoint sub-blocks. The PAPR reduction performance of the proposed PTS method is compared with two well known sub-blocks partitioning schemes, namely, Adjacent Partitioning (AP), Interleaved Partitioning (IP). The various computer simulation results for the various sub-blocks confirmed that the proposed method provides better PAPR reduction performance compared with AP and IP partitioning based PTS scheme. In addition, these PTS schemes largely depend on the chosen size of the partitions

Blind nonlinearity equalization by machine learning based clustering for single- and multi-channel coherent optical OFDM

Fiber-induced intra- and inter-channel nonlinearities are experimentally tackled using blind nonlinear equalization (NLE) by unsupervised machine learning based clustering (MLC) in ∼46-Gb/s single-channel and ∼20-Gb/s (middle-channel) multi-channel coherent multi-carrier signals (OFDM-based). To that end we introduce, for the first time, Hierarchical and Fuzzy-Logic C-means (FLC) based clustering in optical communications. It is shown that among the two proposed MLC algorithms, FLC reveals the highest performance at optimum launched optical powers (LOPs), while at very high LOPs Hierarchical can compensate more effectively nonlinearities only for low-level modulation formats. FLC also outperforms K-means, Fast-Newton support vector machines, supervised artificial neural networks and a NLE with deterministic Volterra analysis, when employing BPSK and QPSK. In particular, for the middle channel of a QPSK WDM coherent optical OFDM system at optimum -5 dBm of LOP and 3200 km of transmission, FLC outperforms Volterra-NLE by 2.5 dB in Q-factor. However, for a 16-quadrature amplitude modulated single-channel system at 2000 km, the performance benefit of FLC over IVSTF reduces to ∼0.4 dB at a LOP of 2 dBm (optimum). Even when using novel sophisticated clustering designs in 16 clusters, no more than additional ∼0.3 dB Q-factor enhancement is observed. Finally, in contrast to the deterministic Volterra-NLE, MLC algorithms can partially tackle the stochastic parametric noise amplification