In this paper, we propose and evaluate outage performance of a mixed amplify-and-forward(AF) and decode-and-forward(DF) relaying protocol in underlay cognitive radio. Different from the conventional AF and DF protocol...In this paper, we propose and evaluate outage performance of a mixed amplify-and-forward(AF) and decode-and-forward(DF) relaying protocol in underlay cognitive radio. Different from the conventional AF and DF protocols, in the proposed protocol, a secondary source attempts to transmit its signal to a secondary destination with help of two secondary relays. One secondary relay always operates in AF mode, while the remaining one always operates in DF mode. Moreover, we also propose a relay selection method, which relies on the decoding status at the DF relay. For performance evaluation and comparison, we derive the exact and approximate closedform expressions of the outage probability for the proposed protocol over Rayleigh fading channel. Finally, we run Monte Carlo simulations to verify the derivations. Results presented that the proposed protocol obtains a diversity order of three and the outage performance of our scheme is between that of the conventional underlay DF protocol and that of the conventional underlay AF protocol.展开更多
Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and c...Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.展开更多
基金supported by the 2016 research fund of University of Ulsan
文摘In this paper, we propose and evaluate outage performance of a mixed amplify-and-forward(AF) and decode-and-forward(DF) relaying protocol in underlay cognitive radio. Different from the conventional AF and DF protocols, in the proposed protocol, a secondary source attempts to transmit its signal to a secondary destination with help of two secondary relays. One secondary relay always operates in AF mode, while the remaining one always operates in DF mode. Moreover, we also propose a relay selection method, which relies on the decoding status at the DF relay. For performance evaluation and comparison, we derive the exact and approximate closedform expressions of the outage probability for the proposed protocol over Rayleigh fading channel. Finally, we run Monte Carlo simulations to verify the derivations. Results presented that the proposed protocol obtains a diversity order of three and the outage performance of our scheme is between that of the conventional underlay DF protocol and that of the conventional underlay AF protocol.
文摘Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.
