A Vector Quantization Based Compression Algorithm for CPRI Link

Abstract

The future wireless networks, such as Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100 times to 1000 times the current 4G technology. For C-RAN based network architecture, there is a pressing need for tremendous enhancement of the effective data rate of the Common Public Radio Interface (CPRI). Compression of CPRI data is one of the potential enhancements. We introduce a vector quantization based compression algorithm for CPRI links, utilizing Lloyd algorithm. Methods to vectorize the I/Q samples and enhanced initialization of Lloyd algorithm for codebook training are investigated for improved performance. Multi-stage vector quantization is considered to reduce codebook search complexity. Simulation results show that our solution can achieve compression of 4 times for uplink and 4.5 times for downlink, within 2% Error Vector Magnitude (EVM) distortion. Remarkably, vector quantization codebook proves to be quite robust against data modulation mismatch, fading, signal-to-noise (SNR) and Doppler spread.

Adaptive Cloud Radio Access Networks: Compression and Optimization

“…Substituting (20) into (10), we obtain the upper bound of the BLER in Rayleigh fading channels. We observe from (18) and (20) that C-RAN achieves full diversity of order N with respect to signal-to-compression-plus-noise ratio. When the quantization noise powers at the RRHs are identical, i.e., δ 1 = · · · = δ N and therefore β 1 = · · · = β N , the lower bound and upper bound completely coincide and equal to ( 13) in Section IV-A.…”

A Simple and Efficient IQ Data Compression Method Based on Latency, EVM, and Compression Ratio Analysis

“…In BS, the number of additional bits per sample needed to remove the sign extension bits can be reduced by increasing N s . After applying the lossy methods, the authors of [24]- [26] have used the Huffman encoding to obtain additional compression gain [27]. The reason for applying the Huffman encoding is that it is optimal when encoding the IQ data samples separately.…”

“…The authors of [24], [25] have proposed a compression method which is consisted of cyclic prefix (CP) removal, K /L decimation, BS, vector quantization (VQ), and Huffman code. It can achieve the CR of 4 times or more within about 2% EVM.…”