AIIM: Adaptive Inter-cell Interference Mitigation for Heterogeneous Multi-vendor 5G O-RAN Networks

2026-05-01

AI Summary

This work addresses severe inter-cell interference in densely deployed 5G heterogeneous multi-vendor O-RAN networks, where limited inter-base-station coordination degrades performance. To tackle this challenge, the authors propose AIIM xApp, an application implementing cross-cell physical resource block (PRB) coordination within the O-RAN near-real-time intelligent controller to adapt to diverse service requirements and dynamic channel conditions. They establish, for the first time, a scalable and reproducible multi-cell interference coordination learning framework on a full-stack O-RAN testbed integrating software-defined radios (SDRs) with virtualized gNBs and UEs, balancing physical-layer fidelity with experimental cost-efficiency. Using a hybrid platform built upon srsRAN, Open5GS, and ORAN-SC, they employ machine learning to model the relationship among neighboring-cell PRB overlap, path loss, and user QoS. Compared to proportional fair scheduling, their approach significantly improves QoS satisfaction rates and reduces PRB wastage due to interference, while maintaining comparable aggregate network throughput.

Abstract

Inter-cell interference is a persistent issue in dense 5G deployments, especially in heterogeneous Open Radio Access Network (O-RAN) environments where coordination between base stations is limited. This paper presents AIIM, an adaptive inter-cell interference mitigation xApp for the O-RAN near-real-time RAN Intelligent Controller (near-RT RIC) that performs coordinated physical resource block (PRB) allocation across multiple base stations under diverse traffic demands and channel conditions. Unlike prior studies that rely primarily on simulation or fully hardware-centric testbeds, AIIM is developed and evaluated in a full-stack O-RAN system built on srsRAN, Open5GS, and O-RAN Software Community (ORAN-SC), and deployed on a hybrid experimental platform that simultaneously combines software defined radio (SDR)-based and virtual gNodeBs (gNBs) and user equipment (UEs). This design preserves realistic PHY-layer interactions while substantially improving scalability, reproducibility, and cost-effectiveness for multi-cell interference experiments. AIIM explicitly models overlapping PRB regions across neighboring cells and learns coordinated allocation policies that adapt to per-user QoS demand and pathloss variation across the network. Experimental results show that AIIM improves QoS satisfaction and reduces interference-induced PRB loss relative to proportional-fair scheduling baselines while maintaining comparable aggregate network throughput. These results demonstrate the promise of scalable, learning-driven O-RAN control for practical interference management in heterogeneous multi-gNB 5G networks.\footnote{A video demonstration of the running system can be found at https://github.com/sireinders/AIIM-Multi-gNB-Interference.git.}

Problem

• Inter-cell interference
• Heterogeneous networks
• O-RAN
• 5G
• Multi-vendor

Innovation

• O-RAN
• inter-cell interference mitigation
• adaptive resource allocation
• near-RT RIC
• multi-gNB coordination