Multidimensional vector quantization-based fast statistical estimation in compressed digitalized radio-over-fiber systems.

Abstract

A multidimensional vector quantization-based fast statistical-estimation (VQ-FSE) algorithm is proposed to enhance data compression performance in digitalized radio over fiber (D-RoF) systems. The original samples with Gaussian distribution are first transformed into these with uniform distribution via companding transformation. After the companding transformation operation, the signal vector is constructed by grouping multiple samples in a certain way so that there is little correlation among them. The constructed signal vector may follow approximately multidimensional uniform distribution, and then multidimensional uniform quantization can be easily carried out, where the complex optimized process in nonuniform quantization is not required. For the proposed two-dimensional (2D) VQ-FSE algorithm, the proposed scheme is numerically verified in a 20km D-RoF system with 2Gbit/s RF wireless signal. Compared with the scalar-quantization-based FSE algorithm, its compression ratio is significantly enhanced. In comparison to the 2D k-means-clustering-based VQ algorithm, the proposed scheme shares a similar compression ratio and offers lower computational complexity. Therefore, the proposed algorithm has the ability to provide better compression and lower complexity for the digitized D-RoF system when the original sample follows Gaussian distribution.