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Blind deconvolution using a maximum likelihood channel estimator

机译:使用最大似然信道估计器进行盲反卷积

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A channel estimator is presented that does not depend on a learning sequence or estimated data sequence for identifying the unknown channel. The authors endeavor to solve the problem of blind equalization in two steps. First, a maximum-likelihood estimate is made of the unknown channel from the received data alone. These estimates are then used in a maximum likelihood sequence (Viterbi) decoder to recover the transmitted digital message. Simulation results show that the probability of error obtained by this approach is comparable to that obtained with a Viterbi decoder operating with known rather than estimated channel symbols.
机译:提出了不依赖于学习序列或估计的数据序列来识别未知信道的信道估计器。作者致力于分两步解决盲均衡问题。首先,仅从接收到的数据中对未知信道进行最大似然估计。然后,将这些估计值用于最大似然序列(Viterbi)解码器中,以恢复发送的数字消息。仿真结果表明,通过这种方法获得的错误概率与使用已知而不是估计的信道符号的维特比解码器获得的概率相当。

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