Quantization-Aware Processing for Massive MIMO Uplink Cloud RAN

Abstract

As the deployment of a large number of antennas and more dense networks, the degradation brought by the finite fronthaul capacity needs to be taken into account in uplink cloud radio access networks (RANs). This letter proposes dimensionality reduction schemes to mitigate the degradation induced by quantization noise. The key idea is to transform observations at radio heads (RHs) in a reduced size, leading to less distorted quantized signals to be sent to the central processor (CP). By intensively using the quantization resource on these punctured observations, the decoding performance can be enhanced at the CP, especially for low-fronthaul capacity links. In the Gaussian source and Gaussian quantization setup, we prove that our scheme achieves a higher sum rate than conventional schemes. This gain is also confirmed by simulations.