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 at SINR ~dB and localization recall of at SINR ~dB.

生成讲解博客的中文版本

生成AI智能校验的中文版本