How to allocate spectra to users efficiently and improve the revenue of providers are popular research topics recently. To address the problem of low revenue of providers in spectrum combinatorial auctions， Random Walk for Spectrum Combinatorial Auctions （RWSCA） mechanism was designed to maximize the revenue of spectrum providers by combining the characteristics of asymmetric distribution of user valuations. First， the idea of virtual valuation was introduced， the random walk algorithm was used to search for a set of optimal parameters in the parameter space， and the valuations of buyers were linearly mapped according to the parameters. Then， VCG （Vickrey-Clarke-Groves） mechanism based on virtual valuation was run to determine the users who won the auction and calculate the corresponding payments. Theoretical analysis proves that the proposed mechanism is incentive compatible and individually rational. In spectrum combinatorial auction simulation experiments， the RWSCA mechanism increases the provider’s revenue by at least 16.84%.

The research on the evolutionary mechanisms and models of Protein-Protein Interaction (PPI) network is significant for understanding the evolution of the biological systems as well as the formation process of the organisms. So far, there have been kinds of models based on different evolutionary mechanisms. All of these models exhibit certain topological characteristics emerging from the protein-protein interaction networks, while some limitations exist simultaneously. This paper focused on several classic protein-protein interaction network models, analyzing the main ideas of these models and comparing the topological characteristics derived from them with those of real protein-protein interaction networks. A summary of the features for each model was given based on the experiments. At last, several viewpoints for the future research of protein-protein interaction network models were also proposed to provide a useful reference for further studies.

As the new generation communication system, LTE/LTE-A requires reliable communication of higher standard for its high-throughput characteristic. Among those decoding algorithms of Turbo, Log-MAP algorithm, as a simplified algorithm, has a good performance, but its high complexity and delay is still a big problem; Max-Log-MAP algorithm with low complexity could not achieve a good performance as the Log-MAP algorithm. This paper proposed an improved Turbo decoding algorithm based on a linear approximation of the correction function. The improved algorithm adopted different correction fitting parameters for different regions. The simulation results demonstrate that, compared with the existing algorithms, this improved algorithm can achieve the same Bit Error Rate (BER) performance as the Log-MAP algorithm and effectively reduce the decoding delay. More importantly, the proposed algorithm is better for implementation.

The estimation of the content popularity in the Content Distributed/Delivery Network (CDN) system mainly relies on the experience of administrators, which implies strong subjectivity and cannot guarantee the Quality of Service (QoS). In the paper, the authors firstly preprocessed the data, and obtained the initial knowledge base to predict the film popularity. This paper used data mining techniques to learn the existing knowledge and predict the popularity of films. Thus, the films in the CDN system could be deployed more effectively and efficiently. The movie popularity predicted by Bayesian network classier was compared with the movie popularity predicted by decision tree. On the premise of the same correct classification rate and other classification parameters, the time taken to build model in the Bayesian network classifier can be shorter. Therefore, the Bayesian network classifier was preferred. The method can solve the inaccurate deployment caused by the administrators subjectivities and improve the efficiency of the CDN system.