PROJECT TITLE :
Mean Energy Efficiency Maximization in Cognitive Radio Channels With PU Outage Constraint
Energy efficiency (EE) is very crucial for future green wireless communication systems, particularly for cognitive radio networks (CRNs). However, the EE design for CRNs in fast-fading scenarios has not been fully studied. This letter investigates the mean EE maximization problem for the secondary user (SU) in the fading cognitive radio channels consisting of both SUs and primary users (PUs). We adopt PU outage probability constraint to ensure the quality of service (QoS) of PUs and consider both the peak and mean transmit power constraints for SUs. However, this problem is nontrivial since the mean EE is nonconvex and PU outage probability constraint belongs to chance constraints. With the aid of the fractional programming and Lagrangian duality theory, we propose an efficient algorithm to derive the optimal power allocation strategy for SU to maximize its mean EE while guaranteeing the QoS of PUs. Simulation results assess the performance of our proposed scheme and show the tradeoff between SUs' mean EE and PU outage probability threshold only in some certain range.
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