| Orthogonal frequency division multiplexing(OFDM)technology has the advantages of high spectrum utilization,strong ability to resist frequency selective fading.It is one of the mainstream technologies of modern wireless communication systems.At the same time,it is also one of the most important alternative technologies in the fifth generation digital mobile communication system.However,its high peak-to-average power ratio(PAPR)damages system performance,makes greater demands on the power amplifier and other devices,and largely restricts the widespread use of OFDM in the field of wireless communications.Therefore,the peak-to-average ratio suppression technique in OFDM systems is one of the main research directions in this field.In this paper,we first introduce the definition,the causes and the distribution of the peak-toaverage ratio in the OFDM system,and make a summary of the typical pre-distortion technology,coding technology and probabilistic technology.Then,two kinds of popular probabilistic technology are discussed: Partial Transmit Sequence(PTS)and Active Constellation Extension(ACE).Traditional PTS uses the exhaustive method to search the optimal phase factor combination,and the computational complexity is very high.To solve this problem,in this paper intelligent algorithms are applied to search the phase factor of each block with low computational complexity to obtain good PAPR suppression performance.How to choose the optimal constellation to be adjusted and how to adjust the location of these constellation points is the technique difficulty in ACE.In order to solve this problem,this paper proposes improved ACE algorithms based on the metric,selecting the appropriate set of constellation points to be expanded and the corresponding amplitude expansion factors based on the metric.When ACO is used to search the phase factors in PTS,the algorithm will converge to the local suboptimal solution.In view of this shortcoming,this paper introduces an improved ACO combining with intelligent ants.After the traditional ants complete search in one iteration,the intelligent ants continue to search around the local suboptimal phase solution found by the traditional ants,improving the chance of obtaining better solution while avoiding premature convergence,and reducing the computational complexity.When TS is applied in PTS,the algorithm will converge to the local optimal solution when the tabu list is short.Aiming at this shortcoming,in this paper an improved TS with dynamic neighborhood and phase factor variogram is designed to avoid the over-reliance on the length of tabu list and reduce the computational complexity.The simulation results show that compared with the existing PTS based on genetic algorithm and particle swarm algorithm,the improved algorithms improve the PAPR performance by 0.2 to 0.3 d B.Considering the metric definition is not accurate enough and the constellation point amplitude amplification factor adaptability is poor in the existing ACE algorithm,we propose new methods to calculate the value of the metric and the amplitude magnification factor,which are called ACE-PT and ACE-RI.The simulation results show that in comparison with existing SAP,the improved non-distorted ACE-PT improves the PAPR performance by about 0.1 to 0.2 d B,distorted ACE-PT improves the PAPR performance by about 0.7 to 1 d B at the expense of a small amount of bit error rate;compared with existing MPCP,the improved ACE-RI improves the PAPR performance by about 0.3 to 0.5 d B. |
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