O-DSS: An Open Dynamic Spectrum Sharing Framework for Cellular-Radar Coexistence in Mid-band Frequencies
Azuka J. Chiejina, Divyadharshini Muruganandham, Vini Chaudhary, Kaushik R. Chowdhury, Vijay Kumar Shah
摘要
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) testbed show that O-DSS ensures robust incumbent protection while maintaining cellular performance by achieving radar detection of ≥ 99% at SINR ≥ -4 dB and localization recall of ≥ 95% at SINR ≥ 8 dB.
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- ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic ControlLuca Baldesi, Francesco Restuccia, Tommaso MelodiaINFOCOM 2022 · 被引用 50 次
- DeepRadar: a deep-learning-based environmental sensing capability sensor design for CBRSShamik Sarkar, Milind M. Buddhikot, Aniqua Baset, Sneha Kumar KaseraMobiCom 2021 · 被引用 40 次
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