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- 저자 : ( Aili Han ) (검색결과 2 건)
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An Improved BER-Optimal Relay Selection Scheme for Decode-and-Forward Cooperative Networks
Yuhui Han,Mingji Yang,Aili Wang 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.1
We are concerned with the relay selection in a decode-and-forward cooperative network to minimize the bit error rate (BER) in a radio cell. This problem can be solved using maximum weighted (MW) matching algorithm, greedy matching algorithm or worst-link-first (WLF) matching algorithm. Among the algorithms, WLF matching algorithm has achieved much attention for its being less complex while the achieved performance is high. In order to further reduce the computational complexity, we propose an improved WLF relay selection scheme, in which relays are selected according to instantaneous channel state information (CSI) and a cooperative threshold is set up to eliminate some unsatisfying candidate relays from the set of alternative candidate relays and thus reduce the amount of calculation. Theory analysis and simulation results both show that the improved WLF relay selection scheme proposed can be easily implemented and achieve almost the same BER performance as that of WLF scheme while the computational complexity is much lower.
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Semi-supervised Cross-media Feature Learning via Efficient L<sub>2,q</sub> Norm
( Zhikai Zong ),( Aili Han ),( Qing Gong ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.3
With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of L<sub>2,q</sub> norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.
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