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

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