论文标题
基于遗传算法的几乎最佳的峰值还原音调选择自适应振幅降低PAPR降低
Genetic Algorithm Based Nearly Optimal Peak Reduction Tone Set Selection for Adaptive Amplitude Clipping PAPR Reduction
论文作者
论文摘要
在基于音调的OFDM系统中，峰值与平均功率比（PAPR）降低性能主要取决于峰值还原音（PRT）集的选择和最佳目标剪辑水平。找到最佳的PRT集需要详尽地搜索所有可能的PRT集的组合，这是一个非确定的多项式时间（NP-HARD）问题，并且对于实用系统中使用的音调数量，此搜索是不可行的。与最佳PRT集合或产生高计算复杂性相比，现有的选择方法（例如连续的PRT集）相同间隔的PRT集和随机PRT集。在本文中，提出了基于遗传算法（GA）具有较低计算复杂性的有效方案，以搜索几乎最佳的PRT集。虽然基于TR的剪辑对于实际实施非常有吸引力，但确定最佳目标剪辑水平很难。为了克服这个问题，我们提出了一种自适应剪辑控制算法。表明我们提出的算法有效地获得了几乎最佳的PRT集合和良好的PAPR减少。
In tone reservation (TR) based OFDM systems, the peak to average power ratio (PAPR) reduction performance mainly depends on the selection of the peak reduction tone (PRT) set and the optimal target clipping level. Finding the optimal PRT set requires an exhaustive search of all combinations of possible PRT sets, which is a nondeterministic polynomial-time (NP-hard) problem, and this search is infeasible for the number of tones used in practical systems. The existing selection methods, such as the consecutive PRT set, equally spaced PRT set and random PRT set, perform poorly compared to the optimal PRT set or incur high computational complexity. In this paper, an efficient scheme based on genetic algorithm (GA) with lower computational complexity is proposed for searching a nearly optimal PRT set. While TR-based clipping is simple and attractive for practical implementation, determining the optimal target clipping level is difficult. To overcome this problem, we propose an adaptive clipping control algorithm.Simulation results show that our proposed algorithms efficiently obtain a nearly optimal PRT set and good PAPR reductions.
