Disaggregated multi-domain interference classification for O-RAN

TL;DR

This paper presents a multi-domain, distributed interference classification architecture for O-RAN that significantly reduces latency and computational cost while maintaining high accuracy in real-time spectrum sharing scenarios.

Contribution

It introduces a novel multi-domain fusion approach leveraging O-RAN's disaggregated architecture for efficient CTI classification with low latency.

Findings

Achieves 400 μs inference latency on standard CPUs.

Reduces latency by 9x and computational cost by 11x compared to monolithic classifiers.

Maintains over 90% accuracy in high-interference conditions.

Abstract

Spectrum sharing and dynamic spectrum reuse are becoming increasingly critical in modern wireless networks to address spectrum scarcity. However, these techniques inevitably increase Cross-Technology Interference (CTI). In this context, the Open Radio Access Network (O-RAN), as a modern and disaggregated network architecture, necessitates accurate, low-latency, and computationally efficient CTI classification and mitigation to support real-time control and maintain Quality of Service (QoS). Unfortunately, existing solutions predominantly rely on high-complexity, monolithic deep learning-based solutions that, while achieving high classification accuracy, incur significant latency and computational overhead This paper exploits the O-RAN functional split to leverage multi-domain raw signal representations (time, frequency, and Channel State Information (CSI)) directly from the same data stream. Each domain is processed locally, naturally interleaving CTI within the distributed, disaggregated O-RAN architecture. This distributed strategy enables a cost-aware, multi-domain fusion architecture that balances classification accuracy with computational overhead and latency. Our proposed multi-domain distributed architecture achieves a 400 $\mu s$ inference latency on standard CPUs. Compared to a state-of-the-art monolithic frequency-domain classifier, this represents an average 9x reduction in latency and an 11-fold decrease in computational cost, while sacrificing only 4% in classification performance and maintaining >90% accuracy in high-interference conditions.