In order to satisfy increasingly greater demand for the performance of communication systems, a throughput efficient wireless system based on the extended binary phase shift keying （EBPSK） modulation is presented. S...In order to satisfy increasingly greater demand for the performance of communication systems, a throughput efficient wireless system based on the extended binary phase shift keying （EBPSK） modulation is presented. Simultaneously, corresponding analysis of power spectra is also given with a brief process. The optimal waveform is proposed without useful information loss, by removing linear spectra presenting periodic components. On this basis, the reasonable definition of bandwidth is discussed, which indicates that the EBPSK belongs to the category of the ultra narrow band （UNB） throughput-efficient communication. Meanwhile, the modulation parameters＇ effects on bandwidth, transmission rate and transmission performance are analyzed. Results illustrate the validity of theoretical analysis and spectrum optimization. Results also prove that this UNB system can obtain good bit error rate （BER） performance with high spectra efficiency.展开更多
Based on the theory of Duffing oscillator weak signal detection and the technology of extended binary phase shift keying （EBPSK） modulation, the chaotic demodulator using the Duffing oscillator for EBPSK signals was...Based on the theory of Duffing oscillator weak signal detection and the technology of extended binary phase shift keying （EBPSK） modulation, the chaotic demodulator using the Duffing oscillator for EBPSK signals was proposed. The proposed demodulator could avoid the problem of demodulation filters design, and shows the excellent anti-noise capability of chaotic oscillator detection. Numerical and experimental tests were taken to investigate the impact of modulation parameters T and 0 on bit error performance of the proposed method, and the performance limits were gotten. The results show that the proposed chaotic demodulator works well under a very low signal-to-noise ratio （SNR） conditions, and gets SNR gains about 20 dB to 30 dB from the impulse filter.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The National Natural Science Foundation of China(No.60472054)the Natural Science Foundation of Jiangsu Province(No.BK2007103)
文摘In order to satisfy increasingly greater demand for the performance of communication systems, a throughput efficient wireless system based on the extended binary phase shift keying （EBPSK） modulation is presented. Simultaneously, corresponding analysis of power spectra is also given with a brief process. The optimal waveform is proposed without useful information loss, by removing linear spectra presenting periodic components. On this basis, the reasonable definition of bandwidth is discussed, which indicates that the EBPSK belongs to the category of the ultra narrow band （UNB） throughput-efficient communication. Meanwhile, the modulation parameters＇ effects on bandwidth, transmission rate and transmission performance are analyzed. Results illustrate the validity of theoretical analysis and spectrum optimization. Results also prove that this UNB system can obtain good bit error rate （BER） performance with high spectra efficiency.
基金supported by the National Natural Science Foundation of China under Grant No.41476089
文摘Based on the theory of Duffing oscillator weak signal detection and the technology of extended binary phase shift keying （EBPSK） modulation, the chaotic demodulator using the Duffing oscillator for EBPSK signals was proposed. The proposed demodulator could avoid the problem of demodulation filters design, and shows the excellent anti-noise capability of chaotic oscillator detection. Numerical and experimental tests were taken to investigate the impact of modulation parameters T and 0 on bit error performance of the proposed method, and the performance limits were gotten. The results show that the proposed chaotic demodulator works well under a very low signal-to-noise ratio （SNR） conditions, and gets SNR gains about 20 dB to 30 dB from the impulse filter.
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.
