Low-complexity PTS scheme based on phase factor sequences optimization

In this paper, a new partial transmit sequence（PTS）scheme with low computational complexity is proposed for the problems of high computational complexity in the conventional PTS method. By analyzing the relationship of candidate sequences in the PTS method under the interleaved partition method, it has been discovered that some candidate sequences generated by phase factor sequences have the same peak average power ratio（PAPR）. Hence, phase factor sequences can be optimized to reduce their searching times. Then, the computational process of generating candidate sequences can be simplified by improving the utilization of data and minimizing the calculations of complex multiplication. The performance analysis shows that, compared with the conventional PTS scheme, the proposed approach significantly decreases the computational complexity and has no loss of PAPR performance.

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.

A KIND OF PAPR REDUCTION METHOD BASED ON PRUNING WPM AND PTS TECHNOLOGY

Wavelet packet multicarrier system gains widespread concern because of its better resistance performance to Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI), as well as the higher spectrum efficiency. However, multicarrier system has a high Peak to Average Power Ratio (PAPR), which will lead to many problems such as lower system performance. In order to solve the problem, a kind of PAPR reduction method based on pruning Wavelet Packet Modulation (WPM) and Partial Transmit Sequences (PTS) technology is proposed in this paper, through proper pruning of the full-tree structure of wavelet packet modulation in the PTS technology to reduce the number of nodes in the system, and finally improve the reduction effect of PAPR. Simulation results show that when Complementary Cumulative Distribution Function (CCDF) is 10 -3 , PTS based on pruning WPM compared with PTS technique and pruning technique has improved about 1 dB and 1.5 dB, which will not affect the system's Bit Error Rate (BER) performance in the wavelet packet multicarrier system.

A correlation OPTS algorithm for reducing peak to average power ratio of FBMC-OQAM systems

A correlation overlapping partial transmit sequence(C-OPTS) algorithm is proposed to solve the issue of high complexity of overlapping partial transmit sequence(OPTS) algorithm in suppressing the peak to average power ratio(PAPR) of filter bank multicarrier-offset quadrature amplitude modulation(FBMC-OQAM) signals.The V subblocks in partial transmit sequence(PTS) are regrouped into U combinations according to the correlation coefficient p,and overlapping subblocks are allowed between adjacent groups.The search starts from the first group and sets the phase factors of the subsequent groups to 1.When the phase factors of the non-overlapping subblocks in the first group are determined,the subsequent groups are searched in turn to determine their respective phase factors.Starting from the second data block,the data overlapped with it should be taken into account when determining its optimal phase factor vector.Theoretical analysis and simulation results indicate that compared with the OPTS algorithm,the proposed algorithm can significantly reduce the computational complexity at the cost of slight deterioration of PAPR performance.Meanwhile,compared with the even-odd iterative double-layers OPTS(ID-OPTS) algorithm,it can further reduce the complexity and obtain a better PAPR suppression effect.