In this paper, we investigate a two-way relay network consisting of two sources, multiple cooperative relays and an eavesdropper. To enhance secure communications, a new relay chatting based on transmission scheme is ...In this paper, we investigate a two-way relay network consisting of two sources, multiple cooperative relays and an eavesdropper. To enhance secure communications, a new relay chatting based on transmission scheme is proposed. Specifically, the proposed scheme selects a best relay that maximize the sum mutual information among the sources to forward the sources’ signals using an amplify-and-forward protocol, and the remaining relays transmit interference signals to confuse the eavesdropper via distributed beam forming. It can be found that the proposed scheme with relay chatting does not require the knowledge of the eavesdropper’s channel, and outperforms the joint relay and jammer selection scheme, which introduces the interference into the sources. Numerical results show that the secrecy outage probability of the proposed scheme converges to zero as the transmit power increases.展开更多
This paper presents an improved non-data-aided algo- rithm for carrier frequency estimation for burst M-ary PSK signals when modulation order M and training symbols are unknown. Unlike data-aided estimation, a phase c...This paper presents an improved non-data-aided algo- rithm for carrier frequency estimation for burst M-ary PSK signals when modulation order M and training symbols are unknown. Unlike data-aided estimation, a phase clustering algorithm is used first to estimate M and modulated information is removed by a vari- able interval linear phase unwrapping. Then, a high-order correlation algorithm with proper correction is present, which reduces the probability of phase ambiguity and promotes anti-noise capability of the estimation. Simulations are given to analyze the unbiased esti- mation range, and the asymptotic performance and symbol number are needed to compare with the former algorithms. The new algo- rithm has a large estimation range close to the theoretical maximum value for non-data-aided estimation and has a better performance than earlier non-data-aided techniques for large frequency offset, low signal-to-noise ratio, and limited symbol numbers.展开更多
Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world ne...Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity.Thus,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information.To that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining layer.Along this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode.The extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.展开更多
文摘In this paper, we investigate a two-way relay network consisting of two sources, multiple cooperative relays and an eavesdropper. To enhance secure communications, a new relay chatting based on transmission scheme is proposed. Specifically, the proposed scheme selects a best relay that maximize the sum mutual information among the sources to forward the sources’ signals using an amplify-and-forward protocol, and the remaining relays transmit interference signals to confuse the eavesdropper via distributed beam forming. It can be found that the proposed scheme with relay chatting does not require the knowledge of the eavesdropper’s channel, and outperforms the joint relay and jammer selection scheme, which introduces the interference into the sources. Numerical results show that the secrecy outage probability of the proposed scheme converges to zero as the transmit power increases.
基金Supported by the National Natural Science Foundation of China (61001111)
文摘This paper presents an improved non-data-aided algo- rithm for carrier frequency estimation for burst M-ary PSK signals when modulation order M and training symbols are unknown. Unlike data-aided estimation, a phase clustering algorithm is used first to estimate M and modulated information is removed by a vari- able interval linear phase unwrapping. Then, a high-order correlation algorithm with proper correction is present, which reduces the probability of phase ambiguity and promotes anti-noise capability of the estimation. Simulations are given to analyze the unbiased esti- mation range, and the asymptotic performance and symbol number are needed to compare with the former algorithms. The new algo- rithm has a large estimation range close to the theoretical maximum value for non-data-aided estimation and has a better performance than earlier non-data-aided techniques for large frequency offset, low signal-to-noise ratio, and limited symbol numbers.
基金This research was partially supported by the National Natural Science Foundation of China(Grants Nos.U1605251 and 61727809).
文摘Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity.Thus,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information.To that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining layer.Along this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode.The extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.
