In this paper,we consider a cognitive radio(CR) system with a single secondary user(SU) and multiple licensed channels.The SU requests a fixed number of licensed channels and must sense the licensed channels one by on...In this paper,we consider a cognitive radio(CR) system with a single secondary user(SU) and multiple licensed channels.The SU requests a fixed number of licensed channels and must sense the licensed channels one by one before transmission.By leveraging prediction based on correlation between the licensed channels,we propose a novel spectrum sensing strategy,to decide which channel is the best choice to sense in order to reduce the sensing time overhead and further improve the SU's achievable throughput.Since the correlation coefficients between the licensed channels cannot be exactly known in advance,the spectrum sensing strategy is designed based on the model-free reinforcement learning(RL).The experimental results show that the proposed spectrum sensing strategy based on reinforcement learning converges and outperforms random sensing strategy in terms of long-term statistics.展开更多
Spectrum sensing is one of the key issues in cognitive radio networks. Most of previous work concenates on sensing the spectrum in a single spectrum band. In this paper, we propose a spectrum sensing sequence predicti...Spectrum sensing is one of the key issues in cognitive radio networks. Most of previous work concenates on sensing the spectrum in a single spectrum band. In this paper, we propose a spectrum sensing sequence prediction scheme for cognitive radio networks with multiple spectrum bands to decrease the spectrum sensing time and increase the throughput of secondary users. The scheme is based on recent advances in computational learning theory, which has shown that prediction is synonymous with data compression. A Ziv-Lempel data compression algorithm is used to design our spectrum sensing sequence prediction scheme. The spectrum band usage history is used for the prediction in our proposed scheme. Simulation results show that the proposed scheme can reduce the average sensing time and improve the system throughput significantly.展开更多
To reasearch on the infrared target perception by pyroelectric infrared (PIR) sensor in network domain measurement,a closed sensing network domain composed of eight-PIR-sensor array is proposed for the minimum sensing...To reasearch on the infrared target perception by pyroelectric infrared (PIR) sensor in network domain measurement,a closed sensing network domain composed of eight-PIR-sensor array is proposed for the minimum sensing cell measurement in network domain and to realize the moving target perception and trajectory prediction. Moreover,the feasibility and accuracy of the proposed method are verified through experiments. The experimental results demonstrate that the maximum error between the real trajectory and the predicted trajectory of the minimum sensing cell measurement method is 0.64 m,which can achieve infrared target perception and moving trajectory prediction.展开更多
With the development and popularization of network technology, such as attacks from the network is also facing serious challenges, showing a ＂one foot in mind that＂ the situation. How can detect possible security ri...With the development and popularization of network technology, such as attacks from the network is also facing serious challenges, showing a ＂one foot in mind that＂ the situation. How can detect possible security risks and the type of attack, and provide preventive strategy is to network managers have been pursuing the goal of network security situational awareness can speak a variety of services and associated data as a highly organic whole, summarized network security and dependency relationships come more comprehensive, complete, accurate decision-making for network security assessment and countermeasures.展开更多
Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, cr...Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density, molecular weight and acentric factor has been used for solubility predic- tion of three disperse dyes in supercritical carbon dioxide （SC-C02） and ethanol as co-solvent. It was shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-C02. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposed model. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.展开更多
基金supported by National Nature Science Foundation of China(NO.61372109)
文摘In this paper,we consider a cognitive radio(CR) system with a single secondary user(SU) and multiple licensed channels.The SU requests a fixed number of licensed channels and must sense the licensed channels one by one before transmission.By leveraging prediction based on correlation between the licensed channels,we propose a novel spectrum sensing strategy,to decide which channel is the best choice to sense in order to reduce the sensing time overhead and further improve the SU's achievable throughput.Since the correlation coefficients between the licensed channels cannot be exactly known in advance,the spectrum sensing strategy is designed based on the model-free reinforcement learning(RL).The experimental results show that the proposed spectrum sensing strategy based on reinforcement learning converges and outperforms random sensing strategy in terms of long-term statistics.
基金Supported by the National Natural Science Foundation of China（No.60832009）, the Natural Science Foundation of Beijing （No.4102044） and the National Nature Science Foundation for Young Scholars of China （No.61001115）
文摘Spectrum sensing is one of the key issues in cognitive radio networks. Most of previous work concenates on sensing the spectrum in a single spectrum band. In this paper, we propose a spectrum sensing sequence prediction scheme for cognitive radio networks with multiple spectrum bands to decrease the spectrum sensing time and increase the throughput of secondary users. The scheme is based on recent advances in computational learning theory, which has shown that prediction is synonymous with data compression. A Ziv-Lempel data compression algorithm is used to design our spectrum sensing sequence prediction scheme. The spectrum band usage history is used for the prediction in our proposed scheme. Simulation results show that the proposed scheme can reduce the average sensing time and improve the system throughput significantly.
文摘To reasearch on the infrared target perception by pyroelectric infrared (PIR) sensor in network domain measurement,a closed sensing network domain composed of eight-PIR-sensor array is proposed for the minimum sensing cell measurement in network domain and to realize the moving target perception and trajectory prediction. Moreover,the feasibility and accuracy of the proposed method are verified through experiments. The experimental results demonstrate that the maximum error between the real trajectory and the predicted trajectory of the minimum sensing cell measurement method is 0.64 m,which can achieve infrared target perception and moving trajectory prediction.
文摘With the development and popularization of network technology, such as attacks from the network is also facing serious challenges, showing a ＂one foot in mind that＂ the situation. How can detect possible security risks and the type of attack, and provide preventive strategy is to network managers have been pursuing the goal of network security situational awareness can speak a variety of services and associated data as a highly organic whole, summarized network security and dependency relationships come more comprehensive, complete, accurate decision-making for network security assessment and countermeasures.
文摘Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density, molecular weight and acentric factor has been used for solubility predic- tion of three disperse dyes in supercritical carbon dioxide （SC-C02） and ethanol as co-solvent. It was shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-C02. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposed model. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.
