A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks

A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.

Numerical Investigation of a modified Kalina cycle system for high-temperature application and genetic algorithm based optimization of the multi-phase expander’s inlet condition

As an alternative to the conventional steam Rankine Cycle,Kalina Cycle has witnessed a growing interest over the past years for high-temperature applications(A working fluid temperature of 500◦C at the turbine inlet).However,the possibility of implementing an additional multi-phase expander on the weak ammonia-water solution loop of the Kalina cycle was hardly analyzed in the available literatures.In this research,two novel Kalina cycles(Kalina cycle-12A and Kalina cycle-12B)have been presented by integrating a multi-phase expander in addition to the turbine installed downstream of the Kalina evaporator.For Kalina cycle-12A,this additional multi-phase expander is positioned downstream of the Kalina separator and on the weak ammonia-water solution loop for Kalina Cycle-12B.A detailed mathematical model based on the thermodynamic laws has been developed to solve and optimize the Kalina Cycles.The influence of critical decision parameters,specifically the ammonia concentration on working fluid and evaporation pressure,were investigated.The optimization was performed based on the objective to maximize the net power output from the multi-phase expander under steady-state operating conditions.When the performance of the proposed Kalina cycles was compared with the conventional Kalina Cycle-12,both of them demonstrated superior performance,i.e.,net power output and peak thermal efficiency increased by a maximum value of 3.23%for the proposed Kalina Cycle-12A cycle and 3.94%for the proposed Kalina Cycle-12B cycle.In terms of second law efficiency,Kalina Cycle-12A is 3.68 percent more efficient than Kalina Cycle-12,while Kalina Cycle-12B is 4.04 percent more efficient.Furthermore,2nd law analysis also reveals,maximum destruction of exergy occurs at the condensers of the cycles.