A joint power control and relay selection scheme is considered for a cooperative and cognitive radio system where a secondary network shares spectrum with the primary network. In the secondary network, two secondary u...A joint power control and relay selection scheme is considered for a cooperative and cognitive radio system where a secondary network shares spectrum with the primary network. In the secondary network, two secondary users （SUs） communicate with each other via an assist relay. The main point is to provide the best system performance to SUs while maintaining the interference power at primary user （PU） under a certain level. Using convex optimization, a closed-form solution is obtained when optimizing the power allocation among the two nodes and relay. Based on this result, a joint power control and relay selection scheme with fewer variable dimensions is proposed to maximize the achievable rate of the secondary system. Simulation results demonstrate that the sum rate of the cognitive two-way relay network increases compared with a random relay selection and fixed power allocation.展开更多
An optimal cooperative beamforming for the amplify-and-forward (AF) MIMO two-way relay channels was designed. Supposing the channel state information (CSI) was perfectly known by the receiver and transmitter as well a...An optimal cooperative beamforming for the amplify-and-forward (AF) MIMO two-way relay channels was designed. Supposing the channel state information (CSI) was perfectly known by the receiver and transmitter as well as the relay, optimal beamforming vectors (matrices) of all nodes were jointly designed based on the criterion of minimizing the sum mean square errors (MSMSE). The analysis result shows that the performance effect of transmitting and receiving beamforming pairs is to maximize the receive signal-to-noise ratio (SNR) at two communication nodes, and the rank of the optimal relay beamforming matrix is no larger than two when there is only one data stream at each source node. A simplified algorithm was put forward to accomplish the design based on the analysis conclusions. Simulation results provide that the system performance, which is characterized in terms of bit error rates (BER), is significantly improved by cooperative beamforming, and the performance of the simplified method is not only very close to the optimal one but also with faster iteration speed and much lower computational complexity.展开更多
In order to save energy consumption of two-way amplifier forward(AF) relaying with channel estimation error, an energy efficiency enhancement scheme is proposed in this work. Firstly, through the analysis of two-way A...In order to save energy consumption of two-way amplifier forward(AF) relaying with channel estimation error, an energy efficiency enhancement scheme is proposed in this work. Firstly, through the analysis of two-way AF relaying mode with channel estimation error, the resultant instantaneous SNRs at end nodes is obtained. Then, by using a high SNR approximation, outage possibility is acquired and its simple closed-form expression is represented. Specially, for using the energy resource more efficiently, a low-complexity power allocation and transmission mode selection policy is proposed to enhance the energy efficiency of two-way AF relay system. Finally, relay priority region is identified in which cooperative diversity energy gain can be achieved. The computer simulations are presented to verify our analytical results, indicating that the proposed policy outperforms direct transmission by an energy gain of 3 dB at the relative channel estimation error less than 0.001. The results also show that the two-way AF relaying transmission loses the two-way AF relaying transmission loses its superiority to direct transmission in terms of energy efficiency when channel estimation error reaches 0.03.展开更多
Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information abo...Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk(TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.展开更多
基金supported by the National Natural Science Foundation of China (61250005)Jiangxi Postdoctoral Science Foundation(2013KY07)
文摘A joint power control and relay selection scheme is considered for a cooperative and cognitive radio system where a secondary network shares spectrum with the primary network. In the secondary network, two secondary users （SUs） communicate with each other via an assist relay. The main point is to provide the best system performance to SUs while maintaining the interference power at primary user （PU） under a certain level. Using convex optimization, a closed-form solution is obtained when optimizing the power allocation among the two nodes and relay. Based on this result, a joint power control and relay selection scheme with fewer variable dimensions is proposed to maximize the achievable rate of the secondary system. Simulation results demonstrate that the sum rate of the cognitive two-way relay network increases compared with a random relay selection and fixed power allocation.
基金Project(60902092)supported by the National Natural Science Foundation of China
文摘An optimal cooperative beamforming for the amplify-and-forward (AF) MIMO two-way relay channels was designed. Supposing the channel state information (CSI) was perfectly known by the receiver and transmitter as well as the relay, optimal beamforming vectors (matrices) of all nodes were jointly designed based on the criterion of minimizing the sum mean square errors (MSMSE). The analysis result shows that the performance effect of transmitting and receiving beamforming pairs is to maximize the receive signal-to-noise ratio (SNR) at two communication nodes, and the rank of the optimal relay beamforming matrix is no larger than two when there is only one data stream at each source node. A simplified algorithm was put forward to accomplish the design based on the analysis conclusions. Simulation results provide that the system performance, which is characterized in terms of bit error rates (BER), is significantly improved by cooperative beamforming, and the performance of the simplified method is not only very close to the optimal one but also with faster iteration speed and much lower computational complexity.
基金Project(IRT0852) supported by the Program for Changjiang Scholars and Innovative Research Team in University,ChinaProject(2012CB316100) supported by the National Basic Research Program of China+2 种基金Projects(61101144,61101145) supported by the National Natural Science Foundation of ChinaProject(B08038) supported by the "111" Project,ChinaProject(K50510010017) supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to save energy consumption of two-way amplifier forward(AF) relaying with channel estimation error, an energy efficiency enhancement scheme is proposed in this work. Firstly, through the analysis of two-way AF relaying mode with channel estimation error, the resultant instantaneous SNRs at end nodes is obtained. Then, by using a high SNR approximation, outage possibility is acquired and its simple closed-form expression is represented. Specially, for using the energy resource more efficiently, a low-complexity power allocation and transmission mode selection policy is proposed to enhance the energy efficiency of two-way AF relay system. Finally, relay priority region is identified in which cooperative diversity energy gain can be achieved. The computer simulations are presented to verify our analytical results, indicating that the proposed policy outperforms direct transmission by an energy gain of 3 dB at the relative channel estimation error less than 0.001. The results also show that the two-way AF relaying transmission loses the two-way AF relaying transmission loses its superiority to direct transmission in terms of energy efficiency when channel estimation error reaches 0.03.
基金Project(61232001) supported by National Natural Science Foundation of ChinaProject supported by the Construct Program of the Key Discipline in Hunan Province,China
文摘Researchers face many class prediction challenges stemming from a small size of training data vis-a-vis a large number of unlabeled samples to be predicted. Transductive learning is proposed to utilize information about unlabeled data to estimate labels of the unlabeled data for this condition. This work presents a new transductive learning method called two-way Markov random walk(TMRW) algorithm. The algorithm uses information about labeled and unlabeled data to predict the labels of the unlabeled data by taking random walks between the labeled and unlabeled data where data points are viewed as nodes of a graph. The labeled points correlate to unlabeled points and vice versa according to a transition probability matrix. We can get the predicted labels of unlabeled samples by combining the results of the two-way walks. Finally, ensemble learning is combined with transductive learning, and Adboost.MH is taken as the study framework to improve the performance of TMRW, which is the basic learner. Experiments show that this algorithm can predict labels of unlabeled data well.
