# QR Approximation for Massive MIMO Fronthaul Compression

## TL;DR

This paper introduces a low-rank approximation method for massive MIMO fronthaul data compression, significantly reducing bandwidth requirements and enhancing error performance in 5G networks.

## Contribution

It proposes a novel dimension reduction scheme based on low-rank approximation tailored for massive MIMO fronthaul compression, addressing bandwidth bottlenecks.

## Key findings

- Achieves over 17x data compression
- Improves error performance through denoising
- Effective for 5G massive MIMO systems

## Abstract

Massive MIMO's immense potential to serve large number of users at fast data rates also comes with the caveat of requiring tremendous processing power. This favours a centralized radio access network (C-RAN) architecture that concentrates the processing power at a common baseband unit (BBU) connected to multiple remote radio heads (RRH) via fronthaul links. The high bandwidths of 5G make the fronthaul data rate a major bottleneck. Since the number of active users in a massive MIMO system is much smaller than the number of antennas, we propose a dimension reduction scheme based on low rank approximation for fronthaul data compression. Link level simulations show that the proposed method achieves more than 17x compression while also improving the error performance of the system through denoising.