Finite Horizon Adaptive Optimal Distributed Power Allocation for Enhanced Cognitive Radio Network in the Presence of Channel Uncertainties

In this paper, novel enhanced Cognitive Radio Network (CRN) is considered by using power control where secondary users (SUs) are allowed to use wireless resources of the primary users (PUs) when PUs are deactivated, but also allow SUs to coexist with PUs while PUs are activated by managing interference caused from SUs to PUs. Therefore, a novel finite horizon adaptive optimal distributed power allocation (FH-AODPA) scheme is proposed by incorporating the effect of channel uncertainties for enhanced CRN in the presence of wireless channel uncertainties under two cases. In Case 1, proposed scheme can force the Signal-to-interference (SIR)of the SUs to converge to a higher target value for increasing network throughput when PU’s are not communicating within finite horizon. Once PUs are activated as in the Case 2, proposed scheme cannot only force the SIR’s of PUs to converge to a higher target SIR, but also force the SIR’s of SUs to converge to a lower value for regulating their interference to Pus during finite time period. In order to mitigate the attenuation of SIR’s due to channel uncertainties the proposed novel FH-AODPA allows the SIR’s of both PUs’ and SUs’ to converge to a desired target SIR while minimizing the energy consumption within finite horizon. Simulation results illustrate that this novel FH-AODPA scheme can converge much faster and cost less energy than others by adapting to the channel variations optimally.