Research Of Cognitive Engine And Key Technology Based On Optimum Decision

Cognitive radio technology allows cognitive users to utilize idle spectrum holes without interference to authorized users, the cognitive user and the authorized user to achieve coexistence, which significantly improve the spectrum utilization. Cognitive engine is the core intelligent technology of cognitive radio. It can sense the parameters of environment and know the cognitive user’s needs. Then CE will find the optimal allocation of transmission parameters, making cognitive user is not interfere authorized users, but also ensures the quality of communication. This thesis comes from the National 863 Program, has important theoretical significance and broad application prospects.This dissertation investigates the key technologies of cognitive engine optimization decision in depth, and the complete innovative research as follows.An optimization algorithm is proposed based on hill-climbing genetic algorithms (HCGA) cognitive engine. By adjusting the crossover and mutation probability of genetic algorithm parameters can enhance their ability to find the global optimum, but this time the algorithm to find the global optimum value near optimal and the algorithm accuracy is not high. The hill climbing algorithm combined with genetic algorithm to search for more accurate value, improve the performance of local optimization, and improve the algorithm accuracy. Simulation results show that, compared HCGA algorithm with traditional genetic algorithm, the optimization can improve the search accuracy of the system, and the convergence of the algorithm has been improved.Particle swarm optimization algorithm also exists the problem of accuracy is not high, we propose a hybrid binary PSO algorithm (HBPSO), that is based on binary PSO, combined with hill-climbing algorithm which has a strong local search ability to improve searching precision. Simulation results show that, HBPSO algorithm under three common scenarios can improve system optimization to enhance the accuracy and convergence of the algorithm.An optimal algorithm in cognitive decision engine based on MIMO Technology is proposed. The algorithm uses OFDM technology, and each antenna transmits a different sub-data stream, so that the system obtains multiplexing gain of multi-antenna. In the process of optimization, the cognitive engine in addition to get the optimal transmission power and modulation parameters, but also need to obtain the optimal antenna transmit power information on each sub-data stream configurations. Through this design, the cognitive decision engine optimization extended to the spatial dimension. The capacity and spectral efficiency of cognitive radio systems have been improved without increasing the signal bandwidth. This algorithm provides a reference for future research on MIMO cognitive engine.For cognitive user which has the ability of energy harvesting, proposed a power control algorithm based on game theory (EHPC). The utility functions in addition to considering the user’s transmit power and signal to interference ratio, also joined the residual energy of the device. The cognitive user can perceive and collect environment energy, and the maximum energy that can be collected is limited by the environment. So the residual energy of device is decided by the emission energy consumption and collecting energy. Meanwhile, the surplus energy of cognitive user has different calculation equations in the process of charging and discharging. Using Newton iteration, solve and prove that EHPC algorithm has a unique Nash equilibrium. In addition, through simulation experiments, we validate the effectiveness of the EHPC algorithm.Finally, the content of the whole dissertation is summarized, and several valuable research directions of cognitive engine in cognitive radio network application development are diacussed.