Downlink Decoding Based Accurate Measurement of LTE Spectrum Tenancy

Mobile networks are embracing Dynamic Spectrum Access (DSA) to unleash data capacities of spectrum holes caused by tidal traffic. Being the largest mobile system, LTE has been standardized to operate in the DSA mode where the knowledge on the spectrum tenancy of LTE systems is required. Although there exists rich literature on spectrum sensing, measurement and modeling, they cannot satisfy the needs of accurately acquiring the spectrum tenancy of LTE systems. This is because most traditional measurements only provide inaccurate tenancy in coarse granularities, and therefore models built upon them are defective. To enable the precise discovery of spectrum assignments of an LTE cell from an outsider perspective, we build U-CIMAN to UnCover spectrum occupancy and user Information in Mobile Access Networks. The LTE protocol fields parsed by U-CIMAN not only accurately reveal the spectrum occupancy at the same granularity with LTE scheduling, but also provide important details associated with spectrum usage, i.e., rough user locations and traffic types. Besides insightful observations based on measurements enabled by U-CIMAN, we propose to characterize LTE spectrum occupancy using Vector Autoregression that captures the statistical distributions of spectrum tenancy intervals in multiple channels and the correlations among them.

Automated summary

Mobile networks are adopting Dynamic Spectrum Access (DSA) to increase data capacity by utilizing unused spectrum caused by fluctuations in network traffic. LTE, as the largest mobile system, operates in DSA mode and requires accurate knowledge of spectrum occupancy. Existing literature on spectrum sensing, measurement, and modeling cannot accurately acquire the spectrum occupancy of LTE systems. To address this issue, a system called U-CIMAN is developed to uncover spectrum occupancy and user information in mobile access networks. U-CIMAN parses LTE protocol fields to accurately reveal spectrum occupancy and provide important details such as user locations and traffic types. Additionally, a Vector Autoregression model is proposed to characterize LTE spectrum occupancy.