'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Spectrum trading is an important aspect of television white space (TVWS) and it is driven by
the failure of spectrum sensing techniques. In spectrum trading, the primary users lease their unoccupied
spectrum to the secondary users for a market fee. Although spectrum trading is considered as a reliable
approach, it is confronted with a spectrum transaction completion time problem, which negatively impacts
on end-users Quality of Service and Quality of Experience metrics. Spectrum transaction completion time
is the duration to successfully conduct TVWS spectrum trading. To address this issue, this paper proposes
simple mechanism auction reward truthful (SMART), a fast and iterative machine learning-assisted spectrum
trading model to address this issue. Simulated results indicate thatSMART out-performs referenced VERUM
algorithm in three key performance indicators: bit-error rate, instantaneous throughput, and probability of
dropped packets by 10%, 5%, and 15%, respectively