LTE上行链路高速铁路应用场景中的频偏估计方法

提出了一种长期演进(LTE)上行链路频偏最大似然(ML)估计算法,并通过定量分析该算法与基于2个训练序列相差的频偏估计算法(相差法)的估计性能,提出了一种联合频偏估计算法.仿真结果表明:与相差法相比,所出提的ML估计算法与联合估计算法的频偏估计范围均能够覆盖高速铁路应用场景中的最大频偏,且ML估计算法不受LTE上行链路跳频传输的影响;在信噪比10 dB且少于4个用户的情况下,2种算法均能够提供10-4或更小的归一化频偏估计均方误差;在单用户情况下,联合估计算法比ML估计算法的均方误差在信噪比上提高了近5 dB.

Frequency Offset Estimation in LTE Uplink High Speed Train Scenarios

Abstract： A maximum likelihood (ML) frequency estimation algorithm for the long term evolution (LTE) uplink is proposed. By the quantitative analysis on the performances of the ML algorithm and the algorithm based on the phase difference between two training sequences (phase difference algorithm), a combined frequency offset algorithm is also proposed. The simulation results show that the proposed ML algorithm and combined algorithm cover a sufficient estimation range for the high speed train (HST) scenario compared with the phase difference algorithm. Moreover, the proposed ML algorithm is not vulnerable to the frequency hopping in the LTE uplink. At SNR=10 dB, when the number of users is smaller than 4, the two proposed algorithms can both provide 10-4, or even smaller, mean square error (MSE) of normalized frequency offset estimation. At the environment of only one user, the combined algorithm provides 5 dB improvement over the ML algorithm in performance.