TY - GEN
T1 - Wireless spectrum occupancy prediction based on partial periodic pattern mining
AU - Huang, Pei
AU - Liu, Chin Jung
AU - Xiao, Li
AU - Chen, Jin
PY - 2012
Y1 - 2012
N2 - Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. The availability of spectrum holes vastly affects the throughput and delay of unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to improve spectrum extraction rate and reduce collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users will be able to make use of spectrum holes efficiently without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real life network activities we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved.
AB - Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. The availability of spectrum holes vastly affects the throughput and delay of unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to improve spectrum extraction rate and reduce collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users will be able to make use of spectrum holes efficiently without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real life network activities we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved.
KW - Cognitive Radio
KW - Dynamic Spectrum Access (DSA)
KW - Occupancy Prediction
KW - Partial Periodic Pattern Mining
UR - http://www.scopus.com/inward/record.url?scp=84868242544&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868242544&partnerID=8YFLogxK
U2 - 10.1109/MASCOTS.2012.16
DO - 10.1109/MASCOTS.2012.16
M3 - Conference contribution
AN - SCOPUS:84868242544
SN - 9780769547930
T3 - Proceedings of the 2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2012
SP - 51
EP - 58
BT - Proceedings of the 2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2012
Y2 - 7 August 2012 through 9 August 2012
ER -