水声通信中变步长神经网络盲均衡算法研究

在水声通信领域多途引起的码间干拢可以用均衡消减。盲均衡不需要训练序列,这将有效的节省通信带宽,提高通信效率及通信性能。实际中的通信信道不可能是完全线性的,神经网络作为一种非线性动态系统,具有大规模并行处理及高度的鲁棒性特征,将其应用于水声信道盲均衡切实可行。文中对变步长BP算法的前馈神经网络进行了理论和算法分析,并通过计算机对其实现水声信道盲均衡进行了仿真。仿真结果表明采用变步长BP算法比采用传统BP算法的神经网络盲均衡器收敛速度快,均衡性能明显提高。

Blind equalization algorithm using variable step size neural network

In underwater acoustic communication, intersymbol interference caused by multipath effects can be mitigated using equalization. Blind equalization without training sequence is a bandwidth efficient way to solve this problem. In fact, communication channels are not completely linear. Neural network is a nonlinear dynamic system, which can be realized with large,scale parallel processing and has good robustness. Forward feedback neural network (FNN) using a variable step size BP algorithm is used to implement underwater acoustic channel equalization. Results of computer simulation indicate that, by using the proposed algorithm, higher convergence rate and better performance are obtained compared to the traditional BP algorithm.