Exploring O-RAN Compression Techniques in Decentralized Distributed MIMO Systems: Reducing Fronthaul Load

TL;DR

This paper investigates uplink fronthaul compression in O-RAN to reduce load in decentralized distributed MIMO systems, demonstrating effective data rate reduction with minimal impact on link performance through simulations.

Contribution

It introduces novel quantization strategies for fronthaul compression in O-RAN, balancing load reduction and system performance in DD-MIMO systems.

Findings

Fronthaul load is significantly reduced with proposed quantization techniques.

Link performance remains competitive despite compression.

Simulation results validate the effectiveness of the methods.

Abstract

This paper explores the application of uplink fronthaul compression techniques within Open RAN (O-RAN) to mitigate fronthaul load in decentralized distributed MIMO (DD-MIMO) systems. With the ever-increasing demand for high data rates and system scalability, the fronthaul load becomes a critical bottleneck. Our method uses O-RAN compression techniques to efficiently compress the fronthaul signals. The goal is to greatly lower the fronthaul load while having little effect on the overall system performance, as shown by Block Error Rate (BLER) curves. Through rigorous link-level simulations, we compare our quantization strategies against a benchmark scenario with no quantization, providing insights into the trade-offs between fronthaul data rate reduction and link performance integrity. The results demonstrate that our proposed quantization techniques not only lower the fronthaul load but also maintain a competitive link quality, making them a viable solution for enhancing the efficiency of next-generation wireless networks. This study underscores the potential of quantization in O-RAN contexts to achieve optimal balance between system capacity and performance, paving the way for more scalable and robust DD-MIMO deployments.