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Spectrum sharing between terrestrial 5G and incumbent networks in the satellite bands presents a promising avenue to satisfy the ever-increasing bandwidth demand of the next-generation wireless networks. However, protecting incumbent operations from harmful interference poses a fundamental challenge in accommodating terrestrial broadband cellular networks in the satellite bands. State-of-the-art spectrum-sharing policies usually consider several worst-case assumptions and ignore site-specific contextual factors in making spectrum-sharing decisions, and thus, often results in under-utilization of the shared band for the secondary licensees. To address such limitations, this paper introduces CAT3S (Context-Aware Terrestrial-Satellite Spectrum Sharing) framework that empowers the coexisting terrestrial 5G network to maximize utilization of the shared satellite band without creating harmful interference to the incumbent links by exploiting the contextual factors. CAT3S consists of the following two components: (i) context-acquisition unit to collect and process essential contextual information for spectrum sharing and (ii) context-aware base station (BS) control unit to optimize the set of operational BSs and their operation parameters (i.e., transmit power and active beams per sector). To evaluate the performance of the CAT3S, a realistic spectrum coexistence case study over the 12 GHz band is considered. Experiment results demonstrate that the proposed CAT3S achieves notably higher spectrum utilization than state-of-the-art spectrum-sharing policies in different weather contexts.
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This content will become publicly available on January 5, 2027
O-DSS: An Open Dynamic Spectrum Sharing Framework for Cellular-Radar Coexistence in Mid-band Frequencies
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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- Award ID(s):
- 2443035
- PAR ID:
- 10689383
- Publisher / Repository:
- IEEE INFOCOM 2026
- Date Published:
- Subject(s) / Keyword(s):
- Dynamic Spectrum Sharing, O-RAN, xApp
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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