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Non-linear channel equalization using adaptive MPNN

机译：使用自适应MPNN的非线性信道均衡

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In this paper the Modified Probabilistic Neural Network (MPNN) is used for dealing with the problem of channel equalization. Some improvements are suggested for the MPNN so that it is more suitable for the current problem. Firstly, the MPNN is extended to process complex signals. Secondly, a stochastic gradient adaptation technique is proposed, such that when the network is being employed to equalize a slowly varying channel, it can self-adapt to the changing environment. Simulations have shown that the MPNN is able to effectively equalize 4-QAM symbol sequences transmitted through a non-linear, slowly time-varying channel. Finally, methods that further reduce the size of the network are proposed. Simulations show that the proposed method is able to reduce the size of the network considerably.
机译：本文采用改进的概率神经网络（MPNN）来解决信道均衡问题。建议对MPNN进行一些改进，使其更适合当前问题。首先，MPNN扩展为处理复杂信号。其次，提出了一种随机梯度自适应技术，使得当网络被用来均衡一个缓慢变化的信道时，它可以适应不断变化的环境。仿真表明，MPNN能够有效地均衡通过非线性，缓慢时变信道传输的4-QAM符号序列。最后，提出了进一步减小网络规模的方法。仿真表明，所提出的方法能够大大减小网络规模。

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