Statistical data compression and differential coding for digital radio-over-fiber-based mobile fronthaul

Abstract

Digital radio over fiber (D-RoF), one of the candidates for 5G mobile fronthaul networks, is known for its high reliability and strong robustness against nonlinear channel degradations, which makes it suitable for short-reach fronthaul links supporting ultra-reliable low-latency communication in 5G. However, traditional D-RoF technology is limited by its lower bandwidth efficiency. In this paper, based on our previous work, advanced data-compression techniques with adaptive non-uniform quantizers and differential coding are discussed for a significant improvement of bandwidth efficiency in fronthaul networks. High-order differential coding based on a least- mean-square algorithm has been proposed to further improve the compression ratio with low complexity and high adaptability. By jointly applying a non-uniform quantizer and a differentiator, the signal-to-quantization-noise ratio and bandwidth efficiency can be improved by around 10 dB and 40%-60%, respectively, depending on the modulation formats in our proposed solution. We have experimentally demonstrated the transmission of 200 Gbps fronthaul links over a fiber distance of 80 km. The system is capable of encapsulating 110 × 120 MHz 5G new radio carriers with error-vector magnitude lower than 0.8%.