针对传统的偏移正交相移键控(OQPSK)解调方法中载波和定时同步进行、二者相互制约、调试难的问题,提出一种基于前馈定时的解调方法。采用频域非线性定时估计算法提取定时误差进行基于内插的定时恢复,利用FFT对数据进行载波频差估计,利用...针对传统的偏移正交相移键控(OQPSK)解调方法中载波和定时同步进行、二者相互制约、调试难的问题,提出一种基于前馈定时的解调方法。采用频域非线性定时估计算法提取定时误差进行基于内插的定时恢复,利用FFT对数据进行载波频差估计,利用2倍数据的Costas环进行载波相位的恢复。该方法实现简单,避免调试中环套环时参数调试的困难。实验结果表明,解调信噪比恶化小于0.5 d B,可以用于指导工程实践。展开更多
Based on an orthogonal frequency division multiplexing(OFDM) training symbol with L identical parts, a novel carrier frequency offset (CFO) estimator is proposed for OFDM systems. The CFO is estimated in two steps, fi...Based on an orthogonal frequency division multiplexing(OFDM) training symbol with L identical parts, a novel carrier frequency offset (CFO) estimator is proposed for OFDM systems. The CFO is estimated in two steps, fine estimate and coarse estimate. In the first step, the fine estimation is performed based on the principle of minimum variance. However, the fine estimation has ambiguity since its estimate range is limited. In the second step, the coarse estimation is obtained, which results in a larger estimate range but less precision. Using the coarse estimation, the ambiguity of fine estimation is resolved. To fully use the correlation among L identical parts, the fine estimation resolved the ambiguity and the coarse estimation are optimally combined to obtain the final estimation. Furthermore, the estimation variance of the proposed method is derived. Simulation results demonstrate that the novel two-step estimator outperforms the conventional two-step estimator in terms of estimate performance and computational complexity.展开更多
文摘针对传统的偏移正交相移键控(OQPSK)解调方法中载波和定时同步进行、二者相互制约、调试难的问题,提出一种基于前馈定时的解调方法。采用频域非线性定时估计算法提取定时误差进行基于内插的定时恢复,利用FFT对数据进行载波频差估计,利用2倍数据的Costas环进行载波相位的恢复。该方法实现简单,避免调试中环套环时参数调试的困难。实验结果表明,解调信噪比恶化小于0.5 d B,可以用于指导工程实践。
基金Foundation of Donghua University,China (No.104100044027)
文摘Based on an orthogonal frequency division multiplexing(OFDM) training symbol with L identical parts, a novel carrier frequency offset (CFO) estimator is proposed for OFDM systems. The CFO is estimated in two steps, fine estimate and coarse estimate. In the first step, the fine estimation is performed based on the principle of minimum variance. However, the fine estimation has ambiguity since its estimate range is limited. In the second step, the coarse estimation is obtained, which results in a larger estimate range but less precision. Using the coarse estimation, the ambiguity of fine estimation is resolved. To fully use the correlation among L identical parts, the fine estimation resolved the ambiguity and the coarse estimation are optimally combined to obtain the final estimation. Furthermore, the estimation variance of the proposed method is derived. Simulation results demonstrate that the novel two-step estimator outperforms the conventional two-step estimator in terms of estimate performance and computational complexity.
