Enabling Spectrum Sharing via Spectrum Consumption Models

Scalable Dynamic Spectrum Access with IEEE 1900.5.2 Spectrum Consumption Models

“…However, these architectures rely on centralized databases, and the methods for determining compatibility among incumbents and secondary users are not standardized. These methods often depend on the tools used to perform spectrum use/occupancy analysis, and their results may be subject to debate by each of the parties involved [14].…”

“…SCMs provide an information model that can capture the boundaries of the use of spectrum by RF devices and systems so that their compatibility (i.e., non-interference) can be arbitrated by efficient and standardized computational methods [30]- [32]. The information captured in SCMs allows for the efficient determination of aggregate interference levels and aggregate compatibility between many devices.…”

“…It supports vibrant innovation and competition from a technical and business/market perspective. Motivated by DARPA's efforts on the Spectrum Collaboration Challenge (SC2) and also taking inspiration from the decentralized protocols developed for the Internet, this work presents novel approaches to perform largescale spectrum deconfliction operations for DSA environments based on the Spectrum Consumption Models (SCMs) defined in the IEEE 1900.5.2 standard [14]. An SCM describes the characteristics and boundaries of spectrum usage of an RF device and it facilitates the autonomous and dynamic selection of spectrum resources across a large number of devices/networks that are sharing spectrum to establish non-interfering wireless communication operations.…”

A cross-layer cooperative jamming scheme for social internet of things

“…Traditional methods for achieving a secure communication with high spectrum utilization include spectrum sensing and reusing technologies for the physical layer [9,10] and cryptography-based security mechanisms for the network layer [11][12][13] . However, both methods are difficult to implement in massive low-capability IoT devices.…”

Trading off Network Density with Frequency Spectrum for Resource Optimization in 5G Ultra-Dense Networks

“…In general, proper tradeoff between spectrum provision and densification provides more degrees of freedom for meeting user rate targets [9]. Assuming that the central processing units are also connected to each other through a backhaul and/or backbone supporting fast coordination among them [10,11], optimal energy-and spectrum-efficient tradeoff among multiple operators can be performed [7,12].…”