Spectrally efficient digitized radio-over-fiber system with k-means clustering-based multidimensional quantization

Area of Science:

• Telecommunications Engineering
• Signal Processing
• Optical Communications

Background:

• Digitized radio-over-fiber (D-RoF) systems are crucial for high-capacity wireless fronthaul.
• Conventional D-RoF systems face challenges in spectral efficiency and signal quality.
• Adaptive quantization methods are needed to optimize D-RoF performance.

Purpose of the Study:

• To propose and experimentally validate a spectrally efficient D-RoF system.
• To improve signal-to-noise ratio (SNR) and reduce error vector magnitude (EVM).
• To demonstrate high-capacity channel aggregation capabilities.

Main Methods:

• Grouping correlated analog signal samples into multidimensional vectors.
• Employing k-means clustering for adaptive quantization.
• Experimental demonstration of a 30 Gbit/s D-RoF system.

Main Results:

• Achieved carrier aggregation of up to 40x100 MHz OFDM channels (4-QAM) and 10x100 MHz OFDM channels (16384-QAM).
• Supported equivalent Common Public Radio Interface (CPRI) rates from 37 to 150 Gbit/s.
• Obtained 8% EVM with 4 quantization bits, reducible to 1% with 7 bits, and improved SNR by ~9 dB compared to conventional systems.

Conclusions:

• The proposed spectrally efficient D-RoF system effectively enhances performance metrics.
• K-means adaptive quantization offers significant advantages over traditional methods.
• The demonstrated system supports future high-capacity wireless communication demands.