  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.
