Photonics-aided 335 GHz PS-64QAM wireless transmission over 200 m employing complex-valued NN classification and random sampling techniques

Abstract

THz fiber-wireless technique can overcome the bandwidth bottleneck of electrical devices and has been popularized in different application scenarios. Furthermore, the probabilistic shaping (PS) technique can optimize both the transmission capacity and the distance, and has been extensively used in the optical fiber communication field. However, the probability of the point in the PS m-ary quadrature-amplitude-modulation (m-QAM) constellation varies with the amplitude, which leads to the class imbalance and degrades the performances of all supervised neural network classification algorithms. In this paper, we propose a novel complex-valued neural network (CVNN) classifier coupled with balanced random oversampling (ROS), which can be trained to restore the phase information simultaneously and overcome the class imbalance caused by PS. Based on this scheme, the fusion of oversampled features in complex domain increases the amount of the effective information of few classes, and thus improves the recognition accuracy effectively. It also has less requirement on the sample size than NN-based classifiers and largely simplifies the neural network architecture. By using our proposed ROS-CVNN classification method, single-lane 10 Gbaud 335 GHz PS-64QAM fiber-wireless transmission over 200 m free-space distance is experimentally realized, and the experimental results show that the efficient data rate is 44 Gbit/s considering the soft-decision forward-error-correction (SD-FEC) with 25% overhead. The results show that ROS-CVNN classifier outperforms the other real-valued NN equalizers and traditional Volterra-series by average 0.5 to 1 dB in receiver sensitivity at the bit error rate (BER) of 6 × 10−2 magnitude. Therefore, we believe that the combination of ROS and NN supervised algorithms has an application prospect for the future 6 G mobile communication.