基于最大似然序列检测的PTS相位系数优化

Optimizing PTS Phase Coefficients with Maximum Likelihood Sequence Detecting

建立了搜索最优PTS相位系数的树形网格模型,提出了一种基于最大似然序列检测的PTS算法(ML-PTS)。该算法采用改进的最大似然序列检测算法搜索得到降低正交频分复用(OFDM)峰均比的最优PTS相位系数。仿真结果表明,在可控复杂度条件下,选择适当的译码深度,该算法可获得最接近相位系数最优解的可行解,最大程度地改善了OFDM信号峰均功率比统计特性。

A trellis model was built up to search the optimal phase coefficients of partial transmit sequence. Then a new PTS algorithm with maximum likelihood sequence detecting named ML-PTS was proposed A modified maximum likelihood sequence detecting algorithm which could get the optimal phase coefficients of PTS was applied to the proposed algorithm in order to reduce the PAPR of OFDM. Simulation results show that the proposed algorithm obtains the inferior phase coefficients closest to optimal ones, the complexity of which can be controlled by decoding depth. The statistical characteristic of the PAPR of OFDM is improved greatly by the proposed algorithm.