O-DSS: An Open Dynamic Spectrum Sharing Framework for Cellular-Radar Coexistence in Mid-band Frequencies

TL;DR

O-DSS is an ML-driven, real-time spectrum sharing framework that enables cellular networks to coexist with radars in mid-band frequencies, ensuring rapid detection and minimal interference.

Contribution

It introduces a flexible, O-RAN-compliant DSS framework utilizing ML for real-time radar detection and localization, improving over fixed ESC-based systems.

Findings

Achieves 60 ms detection latency and 700 ms evacuation latency.

Detects radars with ≥99% accuracy at SINR ≥ -4 dB.

Localizes radars with ≥95% recall at SINR ≥ 8 dB.

Abstract

The growing demand for mid-band spectrum necessitates efficient Dynamic Spectrum Sharing (DSS) to ensure coexistence between cellular networks and incumbent radar systems. Existing Spectrum Access System (SAS) frameworks rely on fixed Environmental Sensing Capability (ESC) sensors, which are latency-prone and inflexible. This paper introduces O-DSS, an O-RAN-compliant, Machine Learning (ML)-driven DSS framework that enables real-time cellular-radar coexistence in mid-band frequencies with shipborne and fast-moving airborne radars. O-DSS integrates radar detection from low-overhead Key Performance Metrics (KPMs) with spectrogram-based localization to drive fine-grained RAN control, including PRB blanking and radar-aware MCS adaptation. Deployed as a modular xApp, O-DSS achieves 60~ms detection and 700~ms evacuation latencies, outperforming existing baselines. Evaluations across simulations and Over-The-Air (OTA) testbeds show that O-DSS ensures robust incumbent protection while maintaining cellular performance by achieving radar detection of $\geq 99\%$ at SINR $\geq -4$~dB and localization recall of $\geq 95\%$ at SINR $\geq 8$~dB.