Overlapped X domain multiplexing(Ov XDM) is a promising encoding technique to obtain high spectral efficiency by utilizing Inter-Symbol Interference(ISI). However, the computational complexity of Maximum Likelihood Se...Overlapped X domain multiplexing(Ov XDM) is a promising encoding technique to obtain high spectral efficiency by utilizing Inter-Symbol Interference(ISI). However, the computational complexity of Maximum Likelihood Sequence Detection(MLSD) increases exponentially with the growth of spectral efficiency in Ov XDM, which is unbearable for practical implementations. This paper proposes an Ov TDM decoding method based on Recurrent Neural Network(RNN) to realize fast decoding of Ov TDM system, which has lower decoding complexity than the traditional fast decoding method. The paper derives the mathematical model of the Ov TDM decoder based on RNN and constructs the decoder model. And we compare the performance of the proposed decoding method with the MLSD algorithm and the Fano algorithm. It’s verified that the proposed decoding method exhibits a higher performance than the traditional fast decoding algorithm, especially for the scenarios of a high overlapped multiplexing coefficient.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
基金supported by the National Natural Science Foundation of China under Grant No.61871049.
文摘Overlapped X domain multiplexing(Ov XDM) is a promising encoding technique to obtain high spectral efficiency by utilizing Inter-Symbol Interference(ISI). However, the computational complexity of Maximum Likelihood Sequence Detection(MLSD) increases exponentially with the growth of spectral efficiency in Ov XDM, which is unbearable for practical implementations. This paper proposes an Ov TDM decoding method based on Recurrent Neural Network(RNN) to realize fast decoding of Ov TDM system, which has lower decoding complexity than the traditional fast decoding method. The paper derives the mathematical model of the Ov TDM decoder based on RNN and constructs the decoder model. And we compare the performance of the proposed decoding method with the MLSD algorithm and the Fano algorithm. It’s verified that the proposed decoding method exhibits a higher performance than the traditional fast decoding algorithm, especially for the scenarios of a high overlapped multiplexing coefficient.
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.
