Polar codes represent one of the major breakthroughs in 5G standard,and have been proven to be able to achieve the symmetric capacity of binary-input discrete memoryless channels using the successive cancellation list...Polar codes represent one of the major breakthroughs in 5G standard,and have been proven to be able to achieve the symmetric capacity of binary-input discrete memoryless channels using the successive cancellation list(SCL)decoding algorithm.However,the SCL algorithm suffers from a large amount of memory overhead.This paper proposes an adaptive simplified decoding algorithm for multiple cyclic redundancy check(CRC)polar codes.Simulation results show that the proposed method can reduce the decoding complexity and memory space.It can also acquire the performance gain in the low signal to noise ratio region.展开更多
Space time trellis coding （STTC） techniques have been proposed to achieve both diversity and coding gains in multiple input multiple output （MIMO） fading channels. But with more transmit antennas STTCs suffer from...Space time trellis coding （STTC） techniques have been proposed to achieve both diversity and coding gains in multiple input multiple output （MIMO） fading channels. But with more transmit antennas STTCs suffer from the design dificulty and complexity increasing. This paper proposes a scheme, named parallel concatenated space time trellis codes （PC-STTC）, to achieve the tradeoff between the performances and complexity of STTCs for a large number of transmit antennas. Simulation results and complexity comparison are provided to demonstrate the performance and superiority of the proposed scheme over conventional schemes in fast fading channels in low signal-to-noise ratio （SNR） regions. And an EXIT （extrinsic information transform） chart is given to analyze the iterative convergence of the proposed scheme. It shows that PC-STTC has better iterative convergence in low SNR regions.展开更多
Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision...Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for radiation source identification.However, training deep neural networks for classification requires a large number of labeled samples, and in non-cooperative applications, it is unrealistic. This paper proposes a method for the unlabeled samples of unknown radiation source. It uses semi-supervised learning to detect unlabeled samples and label new samples automatically. It avoids retraining the neural network with parameter-transfer learning. The results show that compared with the traditional algorithms, the proposed algorithm can offer better accuracy.展开更多
基金supported by the National Key R&D Program of China(2018YFB2101300)the National Science Foundation of China(61973056)
文摘Polar codes represent one of the major breakthroughs in 5G standard,and have been proven to be able to achieve the symmetric capacity of binary-input discrete memoryless channels using the successive cancellation list(SCL)decoding algorithm.However,the SCL algorithm suffers from a large amount of memory overhead.This paper proposes an adaptive simplified decoding algorithm for multiple cyclic redundancy check(CRC)polar codes.Simulation results show that the proposed method can reduce the decoding complexity and memory space.It can also acquire the performance gain in the low signal to noise ratio region.
基金supported by Shanghai Municipal Government and Nokia
文摘Space time trellis coding （STTC） techniques have been proposed to achieve both diversity and coding gains in multiple input multiple output （MIMO） fading channels. But with more transmit antennas STTCs suffer from the design dificulty and complexity increasing. This paper proposes a scheme, named parallel concatenated space time trellis codes （PC-STTC）, to achieve the tradeoff between the performances and complexity of STTCs for a large number of transmit antennas. Simulation results and complexity comparison are provided to demonstrate the performance and superiority of the proposed scheme over conventional schemes in fast fading channels in low signal-to-noise ratio （SNR） regions. And an EXIT （extrinsic information transform） chart is given to analyze the iterative convergence of the proposed scheme. It shows that PC-STTC has better iterative convergence in low SNR regions.
基金supported by the National Key R&D Program of China (2018YFB2101300)。
文摘Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for radiation source identification.However, training deep neural networks for classification requires a large number of labeled samples, and in non-cooperative applications, it is unrealistic. This paper proposes a method for the unlabeled samples of unknown radiation source. It uses semi-supervised learning to detect unlabeled samples and label new samples automatically. It avoids retraining the neural network with parameter-transfer learning. The results show that compared with the traditional algorithms, the proposed algorithm can offer better accuracy.
