Fronthaul Compression for Uplink Massive MIMO using Matrix Decomposition

TL;DR

This paper introduces an iterative blind deconvolution method leveraging convolution structure to compress uplink signals in massive MIMO, significantly reducing fronthaul load while enabling blind decoding of OFDM signals.

Contribution

It presents a novel iterative blind deconvolution technique using matrix decomposition for fronthaul compression in massive MIMO uplink systems.

Findings

Achieves 30-50 times compression ratios.

Enables blind decoding of OFDM signals.

Reduces fronthaul bandwidth requirements.

Abstract

Massive MIMO opens up attractive possibilities for next generation wireless systems with its large number of antennas offering spatial diversity and multiplexing gain. However, the fronthaul link that connects a massive MIMO Remote Radio Head (RRH) and carries IQ samples to the Baseband Unit (BBU) of the base station can throttle the network capacity/speed if appropriate data compression techniques are not applied. In this paper, we propose an iterative technique for fronthaul load reduction in the uplink for massive MIMO systems that utilizes the convolution structure of the received signals. We use an alternating minimisation algorithm for blind deconvolution of the received data matrix that provides compression ratios of 30-50. In addition, the technique presented here can be used for blind decoding of OFDM signals in massive MIMO systems.