ABSTRACT
Quantum information science is a rising cross subject. Due to unique features in the information field, it may break the limitation of classic information system, be currently available in several aspects, namely, speeding computation, ensuring information security, expanding the capacity of information, improving the accuracy of detection. Particularly in recent years, quantum algorithms of intelligence, based on the parallel quantum computation, effectively simplify computation complexity belonging to some classic algorithms which are not easy to solve problem on the background of classic system.
Low error rate and reduced complexity of communications system are two ultimate targets for all detection techniques. In order to achieve the goals, the dissertation designs a new type of optimization detection algorithm which is expected to acquire desired performance-complexity trade-off.
First of all, the dissertation investigates the characteristic of Quantum Genetic Algorithm (QGA). It makes use of QGA, which has features of small population size and fast convergence, to optimize BP(Back Propagation) network and RBF(Radial Basis Function) network. Simulation results show that the neural networks optimized by QGA perform well in the test.
Secondly, the dissertation discusses the signal detection scheme with neural network optimized by QGA in the MIMO systems. It takes advantage of QGA to optimize the initial data of the neural network. Simulation results show the superiority of the proposed method in MIMO signals detection.
Finally, the dissertation investigates the signal detection scheme with neural network optimized by QGA in the MIMO-OFDM systems. It takes advantage of QGA to optimize the initial data of the neural network. Simulation results show that the proposed method is superior to the other algorithms in MIMO-OFDM signals detection.