一种基于最大似然的SOQPSK联合估计优化算法

An optimized maximum likelihood-based joint estimation algorithm for SOQPSK

摘要:

为解决整形偏移正交相移键控（shaped-offset quadrature phase-shift keying,SOQPSK）信号符号定时同步算法在高精度条件下实现复杂度较高的问题,提出了一种基于最大似然的优化SOQPSK符号定时与相位同步联合估计算法。首先给出了SOQPSK-MIL信号模型和该信号的相位变化规律,以及基于最大似然算法的SOQPSK信号的符号定时与相位同步联合估计算法的原理与估计结果,详细分析了该算法中不同h函数的能量差异以及其所带来的在不同简化程度下该算法的性能差距,并论证了在只采用Z+₁和Z+_－1参数进行估计时算法的有效性,同时针对该算法实现过程中卷积运算量大的问题,提出了一种h函数的优化处理方式,在保证h函数能量不变的前提下,对h函数进行量化,使其变为一个简单的只有0、A和-A三种幅度的三值函数,从而有效减小卷积过程的计算量,使卷积部分的计算复杂度由O(N²L₀)下降为O(NL₀)。仿真结果表明:该算法具有估计精度高、计算复杂度低的特点,在大幅降低了算法实现复杂度的同时,维持了先前的高估计精度,改进后算法的定时误差和相位误差的估计结果的最小均方误差几乎与改进前没有差别。

Abstract:

In view of the problem of high complexity of the shaped-offset quadrature phase-shift keying (SOQPSK) symbol timing algorithm under high-precision conditions, an optimized maximum likelihood (ML)-based SOQPSK joint symbol timing and phase recovery algorithm was proposed. The SOQPSK-MIL signal model and the phase changing regulation of the signal were presented. The principle and estimation results of the ML-based algorithm for SOQPSK signal were given. The energy difference between different h functions in the algorithm was analyzed in detail, and the performance of the algorithm under different simplification degrees was discussed. The effectiveness of the algorithm when it only used Z+₁ and Z+_－1 parameters for estimation was analyzed. Considering the problem of the large amount of calculation during the convolution process in the implementation of the algorithm, an optimized processing method of h function was proposed. On the premise of ensuring that the energy of the h function remained unchanged, an optimization method was proposed, which quantizes the h function to make it a simple three-valued function with only three amplitudes of 0, A, and -A, thereby effectively reducing the calculation amount of the convolution process and reducing the computational complexity of the convolution part from O(N²L₀) to O(NL₀). Simulation results show that the algorithm had the characteristics of high estimation accuracy and low computational complexity. The implementation complexity of the algorithm was greatly reduced, and the previous high estimation accuracy was maintained. The minimum mean square errors of the timing error and phase error estimation results of the improved algorithm were almost the same as those before the improvement.