TY - GEN
T1 - Prediction Based Adaptive RF Spectrum Reservation in Wireless Virtualization
AU - Adebayo, Abdulhamid
AU - Rawat, Danda B.
AU - Song, Min
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - With wireless virtualization, Wireless Infrastructure Providers (WIPs) are able to sublease out RF spectrum to multiple Wireless Virtual Network Operators (WVNO) who in turn offer services to their customers while sharing the same physical infrastructure. WVNOs are capable of leasing through a reservation process which may be accompanied by some strict guarantees, usually discouraging overbooking through certain penalties. On a global scale, it is important for WIPs to also be able to proactively reserve spectrum resources for consumer usage based on informed estimates. As part of the educated estimation, predictions are made from data of previous spectrum allocations and harmonized with aggregation of crowd-sourced data for events in a bid to reduce the probability of overbooking. The data aggregation effort relies on the the reliability of workers to generate highly accurate results using a community-based aggregation model. Also in this paper, a novel spectrum reservation prediction algorithm, namely Volume-conditioned Spectrum Selective Moving Average (VSSMA) is proposed using the trend similarity of spectrum allocation. The simulation results show that the relative mean error of the VSSMA algorithm is much lower than the Exponential Weighted Moving Average (EWMA) algorithm which is widely used now. We validate the desirable properties of the proposed approach through theoretical analysis, as well as simulations.
AB - With wireless virtualization, Wireless Infrastructure Providers (WIPs) are able to sublease out RF spectrum to multiple Wireless Virtual Network Operators (WVNO) who in turn offer services to their customers while sharing the same physical infrastructure. WVNOs are capable of leasing through a reservation process which may be accompanied by some strict guarantees, usually discouraging overbooking through certain penalties. On a global scale, it is important for WIPs to also be able to proactively reserve spectrum resources for consumer usage based on informed estimates. As part of the educated estimation, predictions are made from data of previous spectrum allocations and harmonized with aggregation of crowd-sourced data for events in a bid to reduce the probability of overbooking. The data aggregation effort relies on the the reliability of workers to generate highly accurate results using a community-based aggregation model. Also in this paper, a novel spectrum reservation prediction algorithm, namely Volume-conditioned Spectrum Selective Moving Average (VSSMA) is proposed using the trend similarity of spectrum allocation. The simulation results show that the relative mean error of the VSSMA algorithm is much lower than the Exponential Weighted Moving Average (EWMA) algorithm which is widely used now. We validate the desirable properties of the proposed approach through theoretical analysis, as well as simulations.
KW - aggregation
KW - prediction
KW - wireless virtualization
UR - http://www.scopus.com/inward/record.url?scp=85089432725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089432725&partnerID=8YFLogxK
U2 - 10.1109/ICC40277.2020.9149184
DO - 10.1109/ICC40277.2020.9149184
M3 - Conference contribution
AN - SCOPUS:85089432725
T3 - IEEE International Conference on Communications
BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
T2 - 2020 IEEE International Conference on Communications, ICC 2020
Y2 - 7 June 2020 through 11 June 2020
ER -