One-step Calculation Circuit of Blind Signal Detection using Complex-valued Hopfield Neural Network
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摘要: 信号盲检测在大规模通信网络中具有重要的意义并得到了广泛的应用，如何快速得到信号盲检测结果是新一代实时通信网络的迫切需求。为此，该文从模拟电路的角度设计了一种能加速信号盲检测的复值Hopfield神经网络(CHNN)电路，该电路可一步完成大规模并行计算，提高信号盲检测速度，同时该电路可以通过调整忆阻器的电导和输入电压来实现可编程功能。Pspice仿真结果表明，该电路的计算精度可达99%以上，运行时间比Matlab软件仿真快3个数量级，此外，该电路具有良好的鲁棒性，即使在20%的噪声干扰下，仍能保持99%以上的计算精度。
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关键词:
- 电路设计 /
- 忆阻器 /
- 复值Hopfield神经网络 /
- 信号盲检测
Abstract: Blind signal detection is of great significance in large-scale communication networks and has been widely used. How to quickly obtain blind signal detection results is an urgent need for the new generation of real-time communication networks. Considering this demand, a Complex-valued Hopfield Neural Network (CHNN) circuit is designed that can accelerate blind signal detection from an analog circuit perspective, the proposed circuit can accelerate the blind signal detection by rapidly performing massively parallel calculation in one step. At the same time, the circuit can be programmable by adjusting the conductance and input voltage of the memristor. The Pspice simulation results show that the computing accuracy of the proposed circuit can exceed 99%. Compared with Matlab software simulation, the proposed circuit is three orders of magnitude faster in terms of computing time. And the accuracy can be maintained at more than 99% even under the interference of 20% noise. -
表 1 电路和软件计算时间比较(ms)
输入信号数量 计算时间 Pspice Matlab 5 阶 0.001 9.5 10 阶 0.03 10.8 20 阶 0.04 11.2 40 阶 0.07 13.3 80 阶 0.16 15.2 
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