Realizing Future Intelligent Networks via Spatial and Multi-Temporal Data Acquisition in Disdrometer Networks

摘要

Data acquisition and qualitative precipitation estimation (QPE) via disdrometers play an important role in estimating rain-induced attenuation in wireless networks. However, existing disdrometer observations do not provide sufficient information for modelling intelligent wireless networks. The design of intelligent wireless networks requires that QPE parameters for a location be known at different epochs. This requires that disdrometers with spatial variability should be capable of multi-temporal QPE observations. A disdrometer architecture that addresses this challenge is presented in this paper. The proposed multi-temporal disdrometer incorporates a computing payload for storing QPE related data at multiple epochs. Performance evaluation shows that the use of the proposed multi-temporal disdrometer in QPE related data acquisition increases data suitable for QPE related modelling by up to 52.2% and 49.4% in the short term and long term respectively.