论文标题
具有卷积神经网络（CNN）和二元卷积神经网络（BCNN）的毫米波射线系统的实验演示
Experimental Demonstration of Millimeter-Wave Radio-over-Fiber System with Convolutional Neural Network (CNN) and Binary Convolutional Neural Network (BCNN)
论文作者
论文摘要
毫米波（MM波）无线电纤维（ROF）系统已被广泛研究为有希望的解决方案，以向最终用户提供高速无线信号，并且已经研究了神经网络以解决各种线性和非线性障碍。但是，需要高计算成本和大量培训数据来有效提高系统性能。在本文中，我们提出并展示了基于高度计算的卷积神经网络（CNN）和二元卷积神经网络（BCNN）的决策方案，以解决这些局限性。提出的基于CNN和BCNN的决策方案在5 Gbps 60 GHz ROF系统中得到证明，最大为20 km纤维距离。与先前证明的神经网络相比，结果表明，位错误率（BER）性能和计算密集型培训过程得到了改善。所需的训练迭代次数减少了约50％，所需培训数据的量减少了30％以上。此外，在拟议的CNN和BCNN方案中，整个测得的接收功率范围超过3.5 dB的光功率范围仅需要进行一次培训，以进一步降低MM-WAVE ROF系统中实施神经网络决策方案的计算成本。
The millimeter-wave (mm-wave) radio-over-fiber (RoF) systems have been widely studied as promising solutions to deliver high-speed wireless signals to end users, and neural networks have been studied to solve various linear and nonlinear impairments. However, high computation cost and large amounts of training data are required to effectively improve the system performance. In this paper, we propose and demonstrate highly computation efficient convolutional neural network (CNN) and binary convolutional neural network (BCNN) based decision schemes to solve these limitations. The proposed CNN and BCNN based decision schemes are demonstrated in a 5 Gbps 60 GHz RoF system for up to 20 km fiber distance. Compared with previously demonstrated neural networks, results show that the bit error rate (BER) performance and the computation intensive training process are improved. The number of training iterations needed is reduced by about 50 % and the amount of required training data is reduced by over 30 %. In addition, only one training is required for the entire measured received optical power range over 3.5 dB in the proposed CNN and BCNN schemes, to further reduce the computation cost of implementing neural networks decision schemes in mm-wave RoF systems.
