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Burst-Mode Synchronization for SOQPSK

机译：SOQPSK的突发模式同步

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摘要

We consider a comprehensive synchronization strategy for burst-mode transmission of shaped-offset quadrature phase-shift keying (SOQPSK) signals over the additive white Gaussian noise channel. Due to the similarities between SOQPSK and continuous phase modulation (CPM), we make use of recent results for synchronization of burst-mode CPMs. We first derive a training sequence that is optimal in the sense that it jointly minimizes the Cramer-Rao bounds (CRBs) for frequency offset, phase offset, and timing offset estimation. Additionally, we develop a maximum likelihood data-aided algorithm for joint estimation of the synchronization parameters for SOQPSK signals. We show that the proposed algorithm for the optimal training sequence can be adapted to work with the suboptimal training sequence that is being considered for use in burst-mode integrated network enhanced telemetry. This demonstrates an immediate practical application for our approach. We present numerical results on the mean-squared error performance of the proposed algorithm for both training sequences and for different versions of SOQPSK. The numerical results show that our joint estimation algorithm yields performance that is very close to the CRBs for all three synchronization variables. Finally, we compare the overall performance of our proposed training sequence and synchronization algorithm for different sequence lengths by simulating a burst-mode SOQPSK receiver. This allows us to employ the right training sequence length based on the desired complexity and bit error rate performance.
机译：我们考虑了一种全面的同步策略，用于成形偏移正交相移键控（SOQPSK）信号的突发模式传输通过添加剂白色高斯噪声通道。由于SOQPSK和连续相位调制（CPM）之间的相似性，我们利用最近的突发模式CPMS同步结果。我们首先导出了一种训练序列，其意义上是最佳的，即它共同最小化了频率偏移，相位偏移和定时偏移估计的Cramer-Rao界限（CRB）。此外，我们开发了一种最大似然数据辅助算法，用于联合估计SOQPSK信号的同步参数。我们表明，所提出的最佳训练序列的算法可以适用于正在考虑用于突发模式集成网络增强遥测的次优训练序列。这证明了我们的方法即时实际应用。我们在训练序列和不同版本的SOQPSK中提出了数值结果对所提出的算法的平均平均误差性能。数值结果表明，我们的联合估计算法产生了对所有三个同步变量非常接近CRB的性能。最后，我们通过模拟突发模式SOQPSK接收器来比较我们提出的训练序列和同步算法的整体性能。这允许我们基于所需的复杂性和误码率性能来使用正确的训练序列长度。

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