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首页> 外文期刊>Vehicular Communications >On the performance of ECF-based multi-threshold receiver in NOMA systems for vehicular communications with unknown impulsive noise
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On the performance of ECF-based multi-threshold receiver in NOMA systems for vehicular communications with unknown impulsive noise

机译:基于ECF的多阈值接收器在云端系统中的性能与未知脉冲噪声的车辆通信中的性能

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摘要

In this paper, we derive an analytical expression for the bit error probability of a multi-threshold (MTh) detector in the downlink three-user non-orthogonal multiple access (NOMA) based vehicular communication system with unknown noise. The MTh detector directly detects the signal of vehicle user with the highest channel gain without detecting the signals of weaker users and removing their effects. It is shown that the bit error probability performance of the MTh detector is superior to that of the successive interference cancellation (SIC) detector in a special case. Then, we propose a blind empirical characteristic function (ECF) based method to estimate the signal levels of vehicle users, required to implement the MTh detector, in unknown noise. Next, we derive an analytical expression for the variance of the proposed ECF-based estimator which is validated via computer simulations. It is shown that the ECF-based estimator is asymptotically unbiased and consistent. Furthermore, we obtain the normalized Cramer-Rao lower bound (NCRLB) for the estimators of signal levels which shows that the ECF-based estimator is almost efficient in small generalized signal-to-noise ratio (GSNR) values. Numerical results show that the ECF-based MTh detector outperforms the absolute median-based SIC detector in the mixture of Gaussian and alpha-stable noise. (C) 2020 Elsevier Inc. All rights reserved.
机译:在本文中,我们从下行链路三用户非正交多次访问(NOMA)的车辆通信系统中的多阈值(MTH)检测器的比特误差概率达到了分析表达式,其基于噪声未知的噪声。 MTH检测器直接检测车辆用户的信号,在不检测较弱的用户的信号并去除它们的效果。结果表明,MTH检测器的误码概率性能优于特殊情况下的连续干扰消除(SIC)检测器的误差概率性能。然后,我们提出了一种基于盲经验特征功能(ECF)的方法来估计车辆用户的信号电平,所以在未知的噪声中需要实现MTH检测器。接下来,我们得出了通过计算机模拟验证的所提出的基于ECF的估计器的方差的分析表达式。结果表明,基于ECF的估计器是渐近的单偏见和一致的。此外,我们获得了信号电平的估计器的归一化Cramer-Rao下限(NCRLB),其表明基于ECF的估计器几乎高效地以小的广义信噪比(GSNR)值。数值结果表明,基于ECF的MTH检测器优于高斯和α稳定噪声混合的基于绝对中值的SiC检测器。 (c)2020 Elsevier Inc.保留所有权利。

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