In this paper,we propose an asymmetric encrypted end-to-end communication system based on convolutional neural networks to solve the problem of secure transmission in the end-to-end wireless communication system.The s...In this paper,we propose an asymmetric encrypted end-to-end communication system based on convolutional neural networks to solve the problem of secure transmission in the end-to-end wireless communication system.The system generates a key generator through a convolutional neural network as a bridge.The private and public keys establish a key pair relationship of arbitrary length sequence information.The transmitter and receiver consist of autoencoders based on convolutional neural networks.For data confidentiality requirements,we design the loss function of the end-to-end communication model based on a convolutional neural network.We also design bugs based on different predictions about the information the system eavesdropper has.Simulation results show that the system performs well on additive Gaussian white noise and Rayleigh fading channels.A legitimate party can establish a secure transmission under a designed communication system;an illegal eavesdropper without a key cannot accurately decode it.展开更多
Most of the existing physical layer watermarking authentication schemes are based on a single-input single-output system and require pre-issue of shared keys.To address these problems,in this thesis,a physical layer a...Most of the existing physical layer watermarking authentication schemes are based on a single-input single-output system and require pre-issue of shared keys.To address these problems,in this thesis,a physical layer authentication scheme without the distribution keys is proposed based on the constellation dithering physical layer authentication watermarking mechanism with a multiple-input multiple-output(MIMO)system,and space-time block coding(STBC)is used to improve the robustness of transmission.Specifically,the legitimate node obtains channel state information(CSI)through channel probing and couples CSI with the message signal using a hash function to generate an authentication tag,which is then embedded through constellation dithering.The receiver extracts the tag and authenticates it using hypothesis testing.Performance analysis shows that the scheme is resistant to various attacks such as replay,interference,tampering,and forgery.Simulation results show that the use of MIMO multi-antenna diversity with STBC coding technique reduces the bit error rate(BER)of message signals and tag signals and improves the detection rate of legitimate signals.展开更多
In the one-bit massive multiple-input multiple-output(MIMO)channel scenario,the accurate channel estimation becomes more difficult because the signals received by the low-resolution analog-to-digital converters(ADC)ar...In the one-bit massive multiple-input multiple-output(MIMO)channel scenario,the accurate channel estimation becomes more difficult because the signals received by the low-resolution analog-to-digital converters(ADC)are quantized and affected by channel noise.Therefore,a one-bit massive MIMO channel estimation method is proposed in this paper.The channel matrix is regarded as a two-dimensional image.In order to enhance the significance of noise features in the image and remove them,the channel attention mechanism is introduced into the conditional generative adversarial network(CGAN)to generate channel images,and im-prove the loss function.The simulation results show that the improved network can use a smaller number of pilots to obtain better channel estimation results.Under the same number of pilots and signal-to-noise ratio(SNR),the channel estimation accuracy can be improved by about 7.5 dB,and can adapt to the scenarios with more antennas.展开更多
基金supported by the National Key Research and Development Program of China(No.2017YFE0135700)the High Level Talent Support Project of Hebei Province(No.A201903011)+3 种基金the Natural Science Foundation of Hebei Province(No.F2018209358)the Tsinghua Precision Medicine Foundation(No.2022TS003)the Telecommunications Research Centre(TRC)of University of Limerick,Ireland,the Science and Education for Smart Growth Operational Program(2014-2020)(No.BG05M2OP001-1.001-0003)co-financed by the European Union through the European Structural and Investment funds.
文摘In this paper,we propose an asymmetric encrypted end-to-end communication system based on convolutional neural networks to solve the problem of secure transmission in the end-to-end wireless communication system.The system generates a key generator through a convolutional neural network as a bridge.The private and public keys establish a key pair relationship of arbitrary length sequence information.The transmitter and receiver consist of autoencoders based on convolutional neural networks.For data confidentiality requirements,we design the loss function of the end-to-end communication model based on a convolutional neural network.We also design bugs based on different predictions about the information the system eavesdropper has.Simulation results show that the system performs well on additive Gaussian white noise and Rayleigh fading channels.A legitimate party can establish a secure transmission under a designed communication system;an illegal eavesdropper without a key cannot accurately decode it.
基金supported by the National Key Research and Development Program of China(No.2017YFE0135700)the High Level Talent Support Project of Hebei Province(No.A201903011)+3 种基金the Natural Science Foundation of Hebei Province(No.F2018209358)the Tsinghua Precision Medicine Foundation(No.2022TS003)the Ministry of Education and Science(MES)for NCDSC,part of the Bulgarian National Roadmap on RIs(No.D01-387/18.12.2020)the Telecommunications Research Centre(TRC)of University of Limerick,Ireland.
文摘Most of the existing physical layer watermarking authentication schemes are based on a single-input single-output system and require pre-issue of shared keys.To address these problems,in this thesis,a physical layer authentication scheme without the distribution keys is proposed based on the constellation dithering physical layer authentication watermarking mechanism with a multiple-input multiple-output(MIMO)system,and space-time block coding(STBC)is used to improve the robustness of transmission.Specifically,the legitimate node obtains channel state information(CSI)through channel probing and couples CSI with the message signal using a hash function to generate an authentication tag,which is then embedded through constellation dithering.The receiver extracts the tag and authenticates it using hypothesis testing.Performance analysis shows that the scheme is resistant to various attacks such as replay,interference,tampering,and forgery.Simulation results show that the use of MIMO multi-antenna diversity with STBC coding technique reduces the bit error rate(BER)of message signals and tag signals and improves the detection rate of legitimate signals.
基金National Key Re-search and Development Program of China(2017YFE0135700)High Level Talent Support Project of Hebei Province(A201903011)Natural Science Foundation of Hebei Province(F2018209358)。
文摘In the one-bit massive multiple-input multiple-output(MIMO)channel scenario,the accurate channel estimation becomes more difficult because the signals received by the low-resolution analog-to-digital converters(ADC)are quantized and affected by channel noise.Therefore,a one-bit massive MIMO channel estimation method is proposed in this paper.The channel matrix is regarded as a two-dimensional image.In order to enhance the significance of noise features in the image and remove them,the channel attention mechanism is introduced into the conditional generative adversarial network(CGAN)to generate channel images,and im-prove the loss function.The simulation results show that the improved network can use a smaller number of pilots to obtain better channel estimation results.Under the same number of pilots and signal-to-noise ratio(SNR),the channel estimation accuracy can be improved by about 7.5 dB,and can adapt to the scenarios with more antennas.
