为了在上行链路支持频率选择性调度,长期演进(LTE)系统定义了探测参考信号(SRS)用于信道质量估计。该文主要研究SRS的信噪比估计方法,针对Boumard方法和传统DFT方法的缺点,提出一种改进的基于DFT的估计方法。该方法通过在时域修正噪声的估计区间,减小高信噪比时有用信号能量泄露对噪声估计的影响,从而获得更准确的信噪比估计。仿真结果表明,所提方法的估计性能优于Boumard方法和传统的DFT方法,提高了高信噪比时的估计精度,在高信噪比区域,平均估计性能提高了约6 dB以上。

To support frequency selective scheduling in uplink, Long Term Evolution (LTE) system defines the Sounding Reference Signal (SRS) for channel quality estimation. This paper focuses on the Signal-to-Noise Ratio (SNR) estimation of the SRS. In order to deal with the shortcomings of Boumard’s method and the traditional DFT method, an improved estimation method based on DFT is proposed. This method reduces the energy leakage’s influence of useful signal on high SNR by correcting the noise estimated interval in time domain, thus more accurate SNR estimation can be obtained. Simulation results show that the estimated performance of the proposed method is better than Boumard’s method and traditional DFT method, and the average performance achieves an improvement of over 6 dB in high SNR area.

为解决频偏估计中经典的MM算法在频偏增大时信噪比门限变差的问题,提出一种改进的频偏估计算法。首先对自相关函数做预平均处理来降低噪声,然后利用预平均值做频偏粗估计,并利用粗估计值纠正相位来减轻相位模糊的问题,最后推导更加合理的窗函数并给出最终频偏估计表达式。仿真表明该算法的信噪比门限比MM算法至少低-1 dB,且在频偏加大时仍然能保持较低的信噪比门限。在保证-3.5 dB的信噪比门限的前提下该算法的估计范围达到了理论值的90%,另外在最大自相关阶数较小时,估计精度门限优于MM算法。该算法在MM算法基础上的改进达到了预期效果,能同时满足无线传感网频偏估计中对低信噪比门限和大估计范围的要求。

M&M method is a typical frequency offset estimator,but its SNR threshold raises when frequency becomes larger, to solve this problem,an improved frequency offset estimator based on M&M method is proposed. Firstly the average treatment of the autocorrelation function is utilized to reduce the noise. Then frequency offset is roughly obtained by using average value, and the problem of phase ambiguity is solved. At last a more reasonable window function is derived and the final expression of estimation frequency offset is given. Simulation results show that the SNR threshold of the proposed method is at least-1 dB low-er than M&M method,and the SNR threshold can keep low when the frequency offset increases. The estimation range of the pro-posed method can reach 90% of the theory value in condition of keeping the SNR threshold -3.5 dB. Additionally,the estima-tion accuracy is better than M&M method when max autocorrelation order is small. The proposed method meets the requirements both in S

在信噪比估计算法中,频域估计法是一种经典算法。为了降低算法复杂度并在低信噪比条件下准确估计,在频域估计法的基础上,结合平均周期图的思想,通过分段运算、累加合并的方法,给出一种改进的频域信噪比估计法。仿真结果表明,在实际信噪比为[-16dB,-6dB]时,估计均方误差不超过0.4dB；通过与最大似然法仿真对比可知,该算法不受多普勒频移影响,应用范围更广。同时,该算法运算量少,复杂度低,易于在工程中实现。

In Signal-to-Noise ratio ( SNR ) estimation algorithms, the frequency-domain estimation is a classical algorithm. To reduce complexity and estimate accurately in the condition of low SNR,this paper proposes an improved frequency-domain SNR estimation algorithm based on the Averaged periodogram by data striping operation and accumulation. The simulation results show that the estimated Mean Square Error ( MSE) of this method is less than 0. 4 dB when the actual SNR ranges from -16 dB to -6 dB. Compared with the maximum likelihood method, the proposed method is not affected by Doppler-shift and can be widely used. Also,it has less complexity,and can be implemented easily in practical projects.

信噪比(SNR)估计对散射通信中功率自适应控制、速率自适应切换及统计信道特性具有重要意义,散射通信需要接收信号的精确信噪比作参考。介绍了一种运算简单、便于工程实现的盲估计算法即二阶矩四阶矩(M2M4)信噪比估计法的原理,将此算法通过硬件来实现。根据散射通信特点,对其进行一系列测试,通过对测试结果分析,得出了M2M4信噪比估计算法在散射通信具有适用性。

SNR estimation has a great meaning for adaptive power control,adaptive switch rate and channel characteristics statistic in troposcatter communication. A blind algorithm called as second - order and fourth-order moments estimator algorithm （ M2 M4 ） is introduced, which have such characteristics as simple operation and easy engineer implementation. The algorithm is realized by hardware devices ,and a series of tests are performed, and the test results prove the applicability of M2M4 algorithm in troposcatter communication.

针对多径信道，提出了一种基于序列相关的信噪比估计算法，利用本地序列与接收信号相关，并采用最小二乘估计法，精确地估计了接收信号幅度和噪声方差，得到了两径信道下信噪比的估计值。仿真结果表明该算法整体估计性能较好，特别适合于低信噪比条件下。在信噪比为-1dB时，与现有的频域和二阶矩四阶矩（M2M4，2-order and 4-order Moments）估计算法相比，该算法的归一化均方误差分别降低了0．09和0．2。

For the multipath channel, a SNR estimation algorithm based on sequence correlation is proposed. By utilizing the correlation of local sequence and received signal, the signal amplitudes and the noise variance are precisely estimated with Least squares estimation algorithm, and the estimated value of SNR in two-path channel is obtained. Simulation results show that this algorithm, as a whole, performs better than frequency domain and 2-order and 4-order moments (M2M4) estimation algorithms particularly suitable for the condition of low SNR. As the actual SNR is -1dB, the normalized mean square error of the proposed algorithm is reduced by 0.09 and 0.2 respectively.