After the digital TV switch-over, national spectrum regulators are considering opportunistic spectrum access techniques in the TV White Spaces (TVWS) frequency band. At present, the reference solution envisages the utilization of geolocation spectrum databases (GLDBs), in which spectrum availability is computed through complex propagation models. However, recent studies indicate that the used path loss model in GLDBs could be either inaccurate or too much conservative, possibly reducing the use of TVWS for opportunistic use by secondary networks. In this paper, we investigate the possibility to enhance the estimation accuracy of GLDBs with sensing reports produced by a swarm of Unmanned Aerial Scanning Vehicles (UASVs). These latter are able to explore the scenario in both space and frequencies, and to build a fine-grained shadowing map which can be used to tune the accuracy of propagation model used by GLDB. A novel distributed mobility algorithm is described for the sensing coverage of the scenario, and an aggregation mechanism for the map creation is illustrated. Simulation results confirm the effectiveness of our scheme in terms of TVWS detection accuracy and scenario coverage issues.

Enhancing TV White-Spaces database with Unmanned Aerial Scanning Vehicles (UASVs)

Natalizio E.
2016-01-01

Abstract

After the digital TV switch-over, national spectrum regulators are considering opportunistic spectrum access techniques in the TV White Spaces (TVWS) frequency band. At present, the reference solution envisages the utilization of geolocation spectrum databases (GLDBs), in which spectrum availability is computed through complex propagation models. However, recent studies indicate that the used path loss model in GLDBs could be either inaccurate or too much conservative, possibly reducing the use of TVWS for opportunistic use by secondary networks. In this paper, we investigate the possibility to enhance the estimation accuracy of GLDBs with sensing reports produced by a swarm of Unmanned Aerial Scanning Vehicles (UASVs). These latter are able to explore the scenario in both space and frequencies, and to build a fine-grained shadowing map which can be used to tune the accuracy of propagation model used by GLDB. A novel distributed mobility algorithm is described for the sensing coverage of the scenario, and an aggregation mechanism for the map creation is illustrated. Simulation results confirm the effectiveness of our scheme in terms of TVWS detection accuracy and scenario coverage issues.
2016
9781450344050
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/384873
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