Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are ava...Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are available at the receiver and training symbols are required to estimate the channel from the transmitter to the receiver. However, increasing the number of the antennas increases the required training interval and reduces the available time with in whichdata may be transmitted. Learning the channel coefficients becomes increasingly difficult for the frequency selective channels. In this paper, with the subspace method and the delay character of delay diversity, a channel estimation method is proposed, which does not use training symbols. It addresses the transmit diversity for a frequency selective channel from a single carrier perspective in the form of a simple equivalent flat fading model. Monte Carlo simulations give the performance of channel estimation and the performance comparison of our channel-estimation-based detector with decision feedback equalization, which uses the perfect channel information.展开更多
The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the cro...The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.展开更多
Extracting, transportation and the using from fossil fuels can damage to the hydrosphere, the biosphere and the Earth＇s atmosphere. But humans always need to this valuable substance. The production of oil derivatives...Extracting, transportation and the using from fossil fuels can damage to the hydrosphere, the biosphere and the Earth＇s atmosphere. But humans always need to this valuable substance. The production of oil derivatives by means of forest waste and coal through the Fischer-Tropsch process is an appropriate solution for the cleanliness of all parts of the environment. For the production of favorite products by the synthesis of Fischer-Tropsch, the performance of the catalyst under different operating conditions should be predictable. For this reason, in this paper, eight mathematical models were determined for the selectivity of five products of methane, light hydrocarbons, gasoline, diesel and wax based on three factors of reduction temperature, time on stream, and He/CO ratio inlet gas on iron-based catalyst. The results showed that the reduction temperature factor had the most effective on the selectivity of hydrocarbon products, exception diesel, so that the increase of the reduction temperature led to increase of the selectivity of methane, light hydrocarbons, gasoline and reduce of the degree of selectivity of the wax and vice versa. For the diesel selectivity, factor of the He/CO ratio inlet gas was the most effective than other factors.展开更多
基金the National Natural Science Foundation of China (No.69872029)
文摘Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are available at the receiver and training symbols are required to estimate the channel from the transmitter to the receiver. However, increasing the number of the antennas increases the required training interval and reduces the available time with in whichdata may be transmitted. Learning the channel coefficients becomes increasingly difficult for the frequency selective channels. In this paper, with the subspace method and the delay character of delay diversity, a channel estimation method is proposed, which does not use training symbols. It addresses the transmit diversity for a frequency selective channel from a single carrier perspective in the form of a simple equivalent flat fading model. Monte Carlo simulations give the performance of channel estimation and the performance comparison of our channel-estimation-based detector with decision feedback equalization, which uses the perfect channel information.
基金supported by Guangxi key Laboratory Fund of Embedded Technology and Intelligent System under Grant No. 2018B-1the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034+1 种基金the National Natural Science Foundation of China under Grant No. 61771488, No. 61671473 and No. 61631020in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratory
文摘The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.
文摘Extracting, transportation and the using from fossil fuels can damage to the hydrosphere, the biosphere and the Earth＇s atmosphere. But humans always need to this valuable substance. The production of oil derivatives by means of forest waste and coal through the Fischer-Tropsch process is an appropriate solution for the cleanliness of all parts of the environment. For the production of favorite products by the synthesis of Fischer-Tropsch, the performance of the catalyst under different operating conditions should be predictable. For this reason, in this paper, eight mathematical models were determined for the selectivity of five products of methane, light hydrocarbons, gasoline, diesel and wax based on three factors of reduction temperature, time on stream, and He/CO ratio inlet gas on iron-based catalyst. The results showed that the reduction temperature factor had the most effective on the selectivity of hydrocarbon products, exception diesel, so that the increase of the reduction temperature led to increase of the selectivity of methane, light hydrocarbons, gasoline and reduce of the degree of selectivity of the wax and vice versa. For the diesel selectivity, factor of the He/CO ratio inlet gas was the most effective than other factors.
