2-D quantization scheme utilizing SOFM neural network clustering for a DRoF system

A two-dimensional (2-D) quantization scheme for the digitized radio-over-fiber (DRoF) transmission system is proposed and experimentally demonstrated. By using a self-organizing feature map (SOFM) neural network clustering method to implement the 2-D quantization, the spectral efficiency of the DRoF system can be effectively improved, and a better transmission performance is achieved. Compared with the conventional scalar quantization based on pulse code modulation in the common public radio interface, the sampled sequence is constructed as a 2-D array, and the SOFM neural network clustering method is employed to obtain an optimized quantization codebook, which can achieve a better error vector magnitude performance with the same quantization bits. In the experiments, the proposed scheme for the DRoF transmission is demonstrated in a 25 km 5  Gbaud/λ four-level pulse amplitude modulation (PAM-4) intensity modulation and direct detection optical link. For the 5G NR standard, carrier aggregations of 27, 20, 16, and 13 100 MHz orthogonal frequency division multiplexing with 4, 16, 64, and 256 QAM at quantization bits per sample of 3, 4, 5, and 6 are successfully achieved, respectively.