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Performance analysis for orthogonal space-time block codes in the absence of perfect channel state information

机译:在没有完美信道状态信息的情况下正交空时分组码的性能分析

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Space-time codes are the joint design of channel coding, modulation, transmit and receive diversity to provide the best tradeoff between data rate, diversity advantage, and code complexity. Specific codes improve the transmission performance in wireless fading channel by providing both the diversity advantage and the coding advantage, where channel state information (CSI) is available at the receiver. But in practice, it is very difficult to estimate the CSI accurately because of the characteristic of wireless fading channel: multi-path fading and time-variance. Hence, a theoretical study of the symbol error rate (SEK) for orthogonal space-time block codes (OSTBC) is presented in this paper, in the absence of perfect CSI. It is proved that the squaring method used to simplify the decoding of OSTBC in [Li Xiangming et al., 2001] could also be used when there is no perfect CSI at receiver. Using this squaring method, a closed-form expression of both SNR and SER for OSTBC can also be derived under these conditions. Simulation results show that performance in the presence of the channel estimation errors is less than that with ideal condition, and that more estimation errors, means more performance loss. It is also proved through the simulations that the BER bound increases slowly when the variance of channel estimation errors is less than some little value, e.g. 0.2. So in practice, it is important to keep the variance of estimation errors less than this value.
机译:空时码是信道编码,调制,发送和接收分集的联合设计,可在数据速率,分集优势和代码复杂度之间提供最佳折衷。特定代码通过提供分集优势和编码优势来提高无线衰落信道中的传输性能,其中接收机可以使用信道状态信息(CSI)。但实际上,由于无线衰落信道的特性:多径衰落和时变,很难准确估计CSI。因此,本文提出了在没有完美CSI的情况下对正交空时分组码(OSTBC)的符号错误率(SEK)的理论研究。事实证明,[Li Xiangming et al。,2001]中用于简化OSTBC解码的平方方法也可以用于接收机中没有完美CSI的情况。使用这种平方方法,还可以在这些条件下得出OSTBC的SNR和SER的闭式表达式。仿真结果表明,存在信道估计误差时的性能要比理想情况下的性能要差,并且估计误差越大,意味着性能损失就越大。通过仿真还证明,当信道估计误差的方差小于某个小值时,例如,当信道估计误差的方差小于1时,BER限制缓慢增加。 0.2。因此,在实践中,保持估计误差的方差小于此值很重要。

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