InterfO-RAN: Real-Time In-band Cellular Uplink Interference Detection with GPU-Accelerated dApps

TL;DR

InterfO-RAN is a GPU-accelerated, real-time interference detection system for 5G networks that uses CNNs to identify uplink interference with high accuracy, improving network reliability in dense environments.

Contribution

This paper introduces InterfO-RAN, the first GPU-accelerated O-RAN dApp for real-time uplink interference detection using CNNs in 5G networks.

Findings

Detection accuracy exceeds 91%

Detection latency under 650 microseconds

Trained on over 7 million NR UL slots in real-world environments

Abstract

Ultra-dense fifth generation (5G) and beyond networks leverage spectrum sharing and frequency reuse to enhance throughput, but face unpredictable in-band uplink (UL) interference challenges that significantly degrade Signal to Interference plus Noise Ratio (SINR) at affected Next Generation Node Bases (gNBs). This is particularly problematic at cell edges, where overlapping regions force User Equipments (UEs) to increase transmit power, and in directional millimeter wave systems, where beamforming sidelobes can create unexpected interference. The resulting signal degradation disrupts protocol operations, including scheduling and resource allocation, by distorting quality indicators like Reference Signal Received Power (RSRP) and Received Signal Strength Indicator (RSSI), and can compromise critical functions such as channel state reporting and Hybrid Automatic Repeat Request (HARQ) acknowledgments. To address this problem, this article introduces InterfO-RAN, a real-time programmable solution that leverages a Convolutional Neural Network (CNN) to process In-phase and Quadrature (I/Q) samples in the gNB physical layer, detecting in-band interference with accuracy exceeding 91% in under 650 us. InterfO-RAN represents the first O-RAN dApp accelerated on Graphics Processing Unit (GPU), coexisting with the 5G NR physical layer processing of NVIDIA Aerial. Deployed in an end-to-end private 5G network with commercial Radio Units (RUs) and smartphones, our solution was trained and tested on more than 7 million NR UL slots collected from real-world environments, demonstrating robust interference detection capabilities essential for maintaining network performance in dense deployments.