This paper presents an equalization algorithm for continuous phase modulation （CPM） over frequency-selective channels. A specific training sequence is first embedded in each data packet. By recursive least-squares ...This paper presents an equalization algorithm for continuous phase modulation （CPM） over frequency-selective channels. A specific training sequence is first embedded in each data packet. By recursive least-squares （RLS） estimation, the channel information parameters can be acquired, and a fractionally Simulation results show that the proposed algorithm can acquire the spaced equalizer performs joint decoding and equalization. channel information parameters rapidly and accurately, and that the fractionally spaced equalizer can eliminate the intersymbol interference （ISI） effectively, and is not sensitive to timing inaccuracy, so this algorithm can be exploited for demodulation system in burst mode.展开更多
A reduced state Soft Input Soft Output (SISO) a posteriori probability algorithm for Seri-ally Concatenated Continuous Phase Modulation (SCCPM) is proposed in this paper. Based on the Reduced State Sequence Detection ...A reduced state Soft Input Soft Output (SISO) a posteriori probability algorithm for Seri-ally Concatenated Continuous Phase Modulation (SCCPM) is proposed in this paper. Based on the Reduced State Sequence Detection (RSSD),it has more general form compared with other reduced state SISO algorithms. The proposed algorithm can greatly reduce the state number,thus leads to the computation complexity reduction. It also minimizes the degradation in Euclidean distance with decision feedback in the reduced state trellis. Analysis and simulation results show that the perform-ance degradation is little with proper reduction scheme.展开更多
The continuous phase modulation(CPM)technique is widely used in range telemetry due to its high spectral efficiency and power efficiency.However,the demodulation performance of the traditional maximum likelihood seque...The continuous phase modulation(CPM)technique is widely used in range telemetry due to its high spectral efficiency and power efficiency.However,the demodulation performance of the traditional maximum likelihood sequence detection(MLSD)algorithm significantly deteriorates in non-ideal synchronization or fading channels.To address this issue,this work proposes a convolutional neural network(CNN)called the cascade parallel crossing network(CPCNet)to enhance the robustness of CPM signals demodulation.The CPCNet model employs a multiple parallel structure and feature fusion to extract richer features from CPM signals.This approach constructs feature maps at different levels,resulting in a more comprehensive training of the model and improved demodulation performance.Simulation results show that under Gaussian channel,the proposed CPCNet achieves the same bit error rate(BER)performance as MLSD method when there is no timing error,but with 1/4 symbol period timing error,the proposed method has 2 dB demodulation gain compared with CNN and convolutional long short-term memory deep neural network(CLDNN).In addition,under Rayleigh channel,the BER of the proposed method is reduced by 5%-87%compared to that of MLSD in the wide signal-to-noise ratio(SNR)region.展开更多
In this paper, serially concatenated continuous phase modulation （SCCPM） system is analyzed and a reduced state soft input soft output （SlSO） a posteriori probability algorithm is proposed. Based on the reduced st...In this paper, serially concatenated continuous phase modulation （SCCPM） system is analyzed and a reduced state soft input soft output （SlSO） a posteriori probability algorithm is proposed. Based on the reduced state sequence detection （RSSD）, it has the more general form compared with other reduced state SISO algorithms. The proposed algorithm can greatly reduce the state number, thus leads to the computation complexity reduction. It also minimizes the degradation in Euclidean distance with decision feedback in the reduced state trellis. Analysis and simulation results show that the performance degradation is little with proper reduction scheme.展开更多
文摘This paper presents an equalization algorithm for continuous phase modulation （CPM） over frequency-selective channels. A specific training sequence is first embedded in each data packet. By recursive least-squares （RLS） estimation, the channel information parameters can be acquired, and a fractionally Simulation results show that the proposed algorithm can acquire the spaced equalizer performs joint decoding and equalization. channel information parameters rapidly and accurately, and that the fractionally spaced equalizer can eliminate the intersymbol interference （ISI） effectively, and is not sensitive to timing inaccuracy, so this algorithm can be exploited for demodulation system in burst mode.
基金Supported by NSFC & Microsoft Asia (60372048)China TRAPOYT, NSFC key project (60496316)+2 种基金863 Project (2005AA123910)RFDP (20050701007)MOE Key Project (104171).
文摘A reduced state Soft Input Soft Output (SISO) a posteriori probability algorithm for Seri-ally Concatenated Continuous Phase Modulation (SCCPM) is proposed in this paper. Based on the Reduced State Sequence Detection (RSSD),it has more general form compared with other reduced state SISO algorithms. The proposed algorithm can greatly reduce the state number,thus leads to the computation complexity reduction. It also minimizes the degradation in Euclidean distance with decision feedback in the reduced state trellis. Analysis and simulation results show that the perform-ance degradation is little with proper reduction scheme.
基金Supported by the Beijing Natural Science Foundation (L202003)。
文摘The continuous phase modulation(CPM)technique is widely used in range telemetry due to its high spectral efficiency and power efficiency.However,the demodulation performance of the traditional maximum likelihood sequence detection(MLSD)algorithm significantly deteriorates in non-ideal synchronization or fading channels.To address this issue,this work proposes a convolutional neural network(CNN)called the cascade parallel crossing network(CPCNet)to enhance the robustness of CPM signals demodulation.The CPCNet model employs a multiple parallel structure and feature fusion to extract richer features from CPM signals.This approach constructs feature maps at different levels,resulting in a more comprehensive training of the model and improved demodulation performance.Simulation results show that under Gaussian channel,the proposed CPCNet achieves the same bit error rate(BER)performance as MLSD method when there is no timing error,but with 1/4 symbol period timing error,the proposed method has 2 dB demodulation gain compared with CNN and convolutional long short-term memory deep neural network(CLDNN).In addition,under Rayleigh channel,the BER of the proposed method is reduced by 5%-87%compared to that of MLSD in the wide signal-to-noise ratio(SNR)region.
基金the National Natural Science Foundation of China (Grant Nos. 60496316, 60532060 and 60572146)the Research Fund for the Doctoral Program of Higher Education (Grant No. 20050701007)China Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE, MOE Key Project (Grant No. 107103)
文摘In this paper, serially concatenated continuous phase modulation （SCCPM） system is analyzed and a reduced state soft input soft output （SlSO） a posteriori probability algorithm is proposed. Based on the reduced state sequence detection （RSSD）, it has the more general form compared with other reduced state SISO algorithms. The proposed algorithm can greatly reduce the state number, thus leads to the computation complexity reduction. It also minimizes the degradation in Euclidean distance with decision feedback in the reduced state trellis. Analysis and simulation results show that the performance degradation is little with proper reduction scheme.
