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Abstract：
In this work, we present a method for optical frequency multiplication utilizing a hybrid deep learning approach that integrates the Residual Network (ResNet) with the Random Forest Regression (RFR) algorithm. Three different frequency multiplication modulation schemes are adopted to illustrate the method, which can obtain suitable parameters for these schemes. Based on the parameters predicted by the algorithm, the 8-tupling, 12-tupling, and 16-tupling mm-wave signals are generated by numerical simulation. The simulation results show that for 8-tupling frequency multiplication, an OSSR (optical sideband suppression ratio) is 30.73 dB and an RFSSR (radio frequency spurious suppression ratio) of 80 GHz is 42.29 dB. For 12-tupling frequency multiplication, the OSSR is 30.09 dB, and the RFSSR of the 120 GHz mm wave is 36.21 dB. For generating 16-tupling frequency mm-wave, an OSSR of 29.86 dB and an RFSSR of 34.52 dB are obtained. In addition, the impact of amplitude fluctuation and bias voltage drift on the quality of mm-wave signals is also studied.
摘要：
在这项工作中,我们提出了一种利用混合深度学习方法的光倍频方法，该方法将残差网络（ResNet）与随机森林回归（RFR）算法集成在一起。采用三种不同的倍频调制方案来说明该方法，这可以为这些方案获得合适的参数。根据算法预测的参数，8-tupling,12元组，并通过数值模拟产生16倍频毫米波信号。仿真结果表明,对于8倍倍频,OSSR(光边带抑制比)为30.73dB,80GHz的RFSSR(射频杂散抑制比)为42.29dB。对于12倍倍频乘法，OSSR为30.09dB，120GHz毫米波的RFSSR为36.21dB。为了产生16倍频毫米波，获得29.86dB的OSSR和34.52dB的RFSSR。此外,还研究了幅度波动和偏置电压漂移对毫米波信号质量的影响。
