Channelized Blocking Probability Estimation Model for Infrastructure Based Mobile Cognitive Radio

Infrastructure based Cognitive Radio (CR) relies upon a fixed Base Station (BS) where the Secondary User (SU) may be directly connected or through relay chain. For operation in licensed band, either SUs or BS has to remain aware of the current occupancy status of primary channels. In existing literatures, usage histories of the channels are also used by researchers for prediction of future occupancy. Hence, CR operator software has to estimate the blocking probability when an SU places service request. Blocking probability can be estimated by last clock hour (e.g. from 10 am to 11am) occupancy, as in classical teletraffic theory and this estimation has been further improved through prediction models. Both the methods depend on hourly occupancy statistics. Researchers of present paper counts an hour as composed of immediately preceding 60 minutes (e.g. from 10:09 am to 11:08 am if SU place service request at 11:08 am instant). In the present work, minute wise collected occupancy data was calculated for 7 days for 50 cells with different channel capacities. Channelized blocking probability has been calculated based on immediate past 60 minutes occupancy. Instantaneous blocking probability has also been calculated based on current minute occupancy for all available channels as reference. A comprehensive prediction model is employed in the proposed work to compute the instantaneous blocking probability both on immediate minute occupancy basis and its preceding 60 minutes basis from time of request by SU. Validation through actual data establishes that Channelized Blocking Probability estimation model has lower error value compared to estimation through prediction models of other researchers. It was also observed that hourly basis prediction model has constant blocking probability value during clock hour, whereas, minute wise Grade of Service (GoS) prediction model addresses the local peak demand and hence leads to a stringent GoS estimation.