Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Dop...Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Doppler signal to shift,which affects the localization accu-racy.To solve this issue,this paper proposes a RFO estimation method based on range migration fitting.Due to the high frequency modulation slope of the linear frequency modulation(LFM)-mod-ulation radar signal,it is not affected by RFO in range compression.Therefore,the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction.Then,the matched filters are designed under different RFOs.When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time,the given RFO is the real RFO between the transceivers.The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s,the pulse repe-tition frequency(PRF)of the transmitter radar signal is not less than 1 kHz,the range detection is not larger than 1000 km,and the signal noise ratio(SNR)is not less than-5 dB.展开更多
This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed alg...This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.展开更多
Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is...Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality.The non-convex weighted sum rate(WSR)problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error(WMMSE)algorithm.We propose to apply deep unfolding to solve the optimization problem,which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations.We also incorporate momentum accelerated projection gradient descent(PGD)algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping.The momentum and step size in deep unfolding network are selected as trainable parameters for training.As shown in the simulation results,deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.展开更多
This paper aims at the theoretical analysis to the impact of government supervision and consumer purchasing behavior on food quality security, so as to look for safety strategies and measures to strengthen and improve...This paper aims at the theoretical analysis to the impact of government supervision and consumer purchasing behavior on food quality security, so as to look for safety strategies and measures to strengthen and improve the level of food safety in China. Reputation mechanism is introduced and Bayesian approach is based on, in which government supervision as well as consumer purchasing behavior is taken as crucial factors to impact on the food quality security. As to the proposed quantitative indicators, government supervision includes exposure rate, fine and etc.;at the same time, consumer purchasing behavior includes consumer’s WTP for security food and consumer expectations to food safety. Taking China’s dairy industry as an example, it makes simulation by Netlog. The results show that consumer purchasing behavior alone has little effect on the dairy companies’ decision-making to be honest or counterfeiting enterprises. However, combination government supervision with purchasing behavior has great impact, and plays very good effects on food safety.展开更多
This paper investigated a QoS-aware power allocation for relay satellite networks.For the given QoS requirements,we analyzed the signal model of relay transmission and formulated the power minimization problem which i...This paper investigated a QoS-aware power allocation for relay satellite networks.For the given QoS requirements,we analyzed the signal model of relay transmission and formulated the power minimization problem which is non-convex and difficult to solve.To find the optimal solution to the considered problem,we first analyzed the optimization problem and equivalently turn it into a convex optimization problem.Then,we provided a Lagrangian dual-based method to obtain the closed-form of the power allocation and provided an iterative algorithm to the optimal solution.Moreover,we also extended the results to the cooperative transmission mode.Finally,simulation results were provided to verify the superiority of the proposed algorithm.展开更多
基金supported in part by the National Natural Foundation of China(No.62027801).
文摘Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Doppler signal to shift,which affects the localization accu-racy.To solve this issue,this paper proposes a RFO estimation method based on range migration fitting.Due to the high frequency modulation slope of the linear frequency modulation(LFM)-mod-ulation radar signal,it is not affected by RFO in range compression.Therefore,the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction.Then,the matched filters are designed under different RFOs.When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time,the given RFO is the real RFO between the transceivers.The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s,the pulse repe-tition frequency(PRF)of the transmitter radar signal is not less than 1 kHz,the range detection is not larger than 1000 km,and the signal noise ratio(SNR)is not less than-5 dB.
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.
基金sponsored by National Natural Science Foundation of China (No. 61871422, No.62027801)
文摘Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality.The non-convex weighted sum rate(WSR)problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error(WMMSE)algorithm.We propose to apply deep unfolding to solve the optimization problem,which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations.We also incorporate momentum accelerated projection gradient descent(PGD)algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping.The momentum and step size in deep unfolding network are selected as trainable parameters for training.As shown in the simulation results,deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.
文摘This paper aims at the theoretical analysis to the impact of government supervision and consumer purchasing behavior on food quality security, so as to look for safety strategies and measures to strengthen and improve the level of food safety in China. Reputation mechanism is introduced and Bayesian approach is based on, in which government supervision as well as consumer purchasing behavior is taken as crucial factors to impact on the food quality security. As to the proposed quantitative indicators, government supervision includes exposure rate, fine and etc.;at the same time, consumer purchasing behavior includes consumer’s WTP for security food and consumer expectations to food safety. Taking China’s dairy industry as an example, it makes simulation by Netlog. The results show that consumer purchasing behavior alone has little effect on the dairy companies’ decision-making to be honest or counterfeiting enterprises. However, combination government supervision with purchasing behavior has great impact, and plays very good effects on food safety.
基金supported by the National Natural Science Foundation of China(No.62027801)。
文摘This paper investigated a QoS-aware power allocation for relay satellite networks.For the given QoS requirements,we analyzed the signal model of relay transmission and formulated the power minimization problem which is non-convex and difficult to solve.To find the optimal solution to the considered problem,we first analyzed the optimization problem and equivalently turn it into a convex optimization problem.Then,we provided a Lagrangian dual-based method to obtain the closed-form of the power allocation and provided an iterative algorithm to the optimal solution.Moreover,we also extended the results to the cooperative transmission mode.Finally,simulation results were provided to verify the superiority of the proposed algorithm.
