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12 April 2022

Proactive Radar Protection System in Shared Spectrum via Forecasting Secondary User Power Levels

Spectrum sharing in radar bands with interference forecasting for enhanced radar protection can help design proactive resource allocation solutions which can achieve high data rates for wireless communication networks on one hand and help protect the incumbent radar systems. We consider radar spectrum sharing in 5.6GHz where a weather radar operates as a primary system and the dominant secondary system is an enterprise network consisting of access points (APs) in a university campus. Our work models transmit the power of the APs as a time series with multinomial distribution based on real collected data. The aggregated interference due to the transmissions from the APs at the radar is forecasted using a long short-term memory (LSTM) based neural network. Monte Carlo dropout is utilized to generate prediction intervals that capture the uncertainties in the interference from the APs. Finally by using both average and upper limits of predicted interference time series a cloud-assisted efficient sharing and radar protection algorithm is proposed. Tracking the rotating radar is not required in the proposed system. The results show that the proposed efficient sharing and radar protection system ensures better radar protection and increased throughput for wireless communication users.

Authors

Sone Su P., Lehtomäki Janne, Khan Zaheer, Umebayashi Kenta, Javed Zunera

Publication type

A1 Journal article – refereed

Keywords

aggregated interference, DFS, LSTM, neural networks, radar, real network data, Spectrum sharing, time series forecasting, WLAN

Published

12 April 2022

Full citation

S. P. Sone, J. Lehtomäki, Z. Khan, K. Umebayashi and Z. Javed, "Proactive Radar Protection System in Shared Spectrum via Forecasting Secondary User Power Levels," in IEEE Access, vol. 10, pp. 40367-40380, 2022, doi: 10.1109/ACCESS.2022.3166844

DOI

http://dx.doi.org/10.1109/access.2022.3166844

Read the publication here

http://urn.fi/urn:nbn:fi-fe2022082656512

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