To solve the difficulty of designing digital impacting filter in the receiver of random-polar modulated Extended Binary Phase Shift Keying with Continuous Phase (CP-EBPSK), a design method based on Quantum-behaved Par...To solve the difficulty of designing digital impacting filter in the receiver of random-polar modulated Extended Binary Phase Shift Keying with Continuous Phase (CP-EBPSK), a design method based on Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is proposed. Firstly, QPSO is introduced elaborately, and the basic flow of QPSO is also given. Then, the demodulation principle of digital impacting filter in the communication system of CP-EBPSK with random-polar is demonstrated, and QPSO is utilized to design the digital impacting filter, which also takes the effect of finite word length into consideration when implemented by hardware. Finally, the proposed method is simulated. Simulation results show that the digital impacting filter designed by new method can derive satisfied demodulation performance.展开更多
In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is des...In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60872075)
文摘To solve the difficulty of designing digital impacting filter in the receiver of random-polar modulated Extended Binary Phase Shift Keying with Continuous Phase (CP-EBPSK), a design method based on Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is proposed. Firstly, QPSO is introduced elaborately, and the basic flow of QPSO is also given. Then, the demodulation principle of digital impacting filter in the communication system of CP-EBPSK with random-polar is demonstrated, and QPSO is utilized to design the digital impacting filter, which also takes the effect of finite word length into consideration when implemented by hardware. Finally, the proposed method is simulated. Simulation results show that the digital impacting filter designed by new method can derive satisfied demodulation performance.
基金The National Natural Science Foundation of China (No.60872075)the National High Technology Research and Development Program of China (863 Program) (No. 2008AA01Z227)
文摘In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors.
