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

TY - GEN

T1 - Exploring O-RAN Compression Techniques in Decentralized Distributed MIMO Systems

T2 - Reducing Fronthaul Load

AU - Rahmani, Mostafa

AU - Zhao, Junbo

AU - Ranjbar, Vida

AU - Al-Tahmeesschi, Ahmed Abdulkareem J

AU - Ahmadi, Hamed

AU - Burr, Alister Graham

AU - Pollin, Sofie

PY - 2025

Y1 - 2025

N2 - This paper explores the application of uplink fronthaul compression techniques within Open RAN (O-RAN) to mitigate fronthaul load in decentralized distributed MIMO (DDMIMO) 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 tradeoffs 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 DDMIMO deployments.

AB - This paper explores the application of uplink fronthaul compression techniques within Open RAN (O-RAN) to mitigate fronthaul load in decentralized distributed MIMO (DDMIMO) 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 tradeoffs 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 DDMIMO deployments.

M3 - Conference contribution

BT - IEEE PIMRC 2025

PB - IEEE Communications Society

ER -