A system model consisting of macro and micro base stations (BS) is introduced to solve the problem of power allocation in heterogeneous dense network. In this hierarchical framework, the problem of power allocation is modeled as a stackelberg game. Based on this model, a two-stage pricing algorithm is proposed to allocate power resource to each BS. In this algorithm, a power price is allocated to each micro-BS by macro-BS and all micro-BSs are calculating respective optimal transmit power based on this price to maximize individual utility. Then a grid-based scenario is introduced to verify the proposed theory. Theoretical analysis and simulation results both validate that the proposed scheme makes performance improvement on spectral and power efficiency. Most importantly, the computaitonal complexity of the proposed scheme is greatly improved, especially in dense deployment.

This letter proposes a low-complexity ‘harvest-and-forward’ relay strategy in simultaneous wireless information and power transfer (SWIPT) relay channels. In the first phase of relay transmission, the relay’s antennas are divided into two subsets. The signals received by the antennas in one subset are converted to energy, and the signals received by the antennas in the other subset are combined. In the second phase, the relay forwards the combined signal using all antennas with the harvested energy. A low complexity antenna selection (AS) algorithm is given to maximize the achievable rate over fading channels. The simulation results show that the achievable rate of this strategy is close to that of the two-stage strategy where a two-state procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting, and the optimized antenna set engaged in information forwarding. The proposed strategy has better performance than the two-stage strategy when the relay is equipped with medium-scale antennas, and the performance gap between two strategies grows with the increase of the number of the relay’s antennas. The computational complexity of the proposed strategy is O(N2) (N is the number of relay antennas), which is obviously lower than that of the two-stage strategy (O(3N3)).

Spectrum sensing is an essential ability to detect spectral holes in cognitive radio (CR) networks. The critical challenge to spectrum sensing in the wideband frequency range is how to sense quickly and accurately. Compressive sensing(CS) theory can be employed to detect signals from a small set of non-adaptive, linear measurements without fully recovering the signal. However, the existing compressive detectors can only detect some known deterministic signals and it is not suitable for the time-varying amplitude signal, such as spectrum sensing signals in CR networks. First, a model of signal detect is proposed by utilizing compressive sampling without signal recovery, and then the generalized likelihood ratio test (GLRT) detection algorithm of the time-varying amplitude signal is
derived in detail. Finally, the theoretical detection performance bound and the computation complexity are analyzed. The comparison between the theory and simulation results of signal detection performance over Rayleigh and Rician channel demonstrates the validity of the performance bound. Compared with the reconstructed spectrum sensing detection algorithm, the proposed algorithm greatly reduces the data volume and algorithm complexity for the signal with random amplitudes.

This study proposes a hybrid model of speech recognition parallel algorithm based on hidden Markov model (HMM) and artificial neural network (ANN). First, the algorithm uses HMM for time-series modeling of speech signals and calculates the voice to the HMM of the output probability score. Second, with the probability score as input to the neural network, the algorithm gets information for classification and recognition and makes a decision based on the hybrid model. Finally, Matlab software is used to train and test sample data. Simulation results show that using the strong time-series modeling ability of HMM and the classification features of neural network, the proposed algorithm possesses stronger noise immunity than the traditional HMM. Moreover, the hybrid model enhances the individual flaws of the HMM and the neural network and greatly improves the speed and performance of speech recognition.

Software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution, density PSO-based (DPSO) software module clustering algorithm is proposed. Firstly, the software system is converted into complex network diagram, and then the particle swarm optimization (PSO) algorithm is improved. The shortest path method is used to initialize the swarm and the probability selection approach is used to update the particle positions. Furthermore, density-based modularization quality (DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of
the DPSO algorithm. Hill climbing (HC) algorithm, genetic algorithm (GA), PSO and DPSO algorithm are compared in the modularization quality (MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than other traditional three algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.

This article puts forward a novel smooth rotated hyperbola model for support vector machine (RHSSVM) for classification. As is well known, the support vector machine (SVM) is based on statistical learning theory(SLT) and performs its high precision on data classification. However, the objective function is non-differentiable at the zero point. Therefore the fast algorithms cannot be used to train and test the SVM. To deal with it, the proposed method is based on the approximation property of the hyperbola to its asymptotic lines. Firstly, we describe the development of RHSSVM from the basic linear SVM optimization programming. Then we extend the linear model to non-linear model. We prove the solution of RHSSVM is convergent, unique, and global optimal. We show how
RHSSVM can be practically implemented. At last, the theoretical analysis illustrates that compared with other three typical models, the rotated hyperbola model has the least error on approximating the plus function. Meanwhile, computer simulations show that the RHSSVM can reduce the consuming time at most 54.6% and can efficiently handle large scale and high dimensional programming.

The trustee and the trustor may have no previous interaction experiences before. So, intermediate nodes which are trusted by both the trustor and the trustee are selected to transit trust between them. But only a few intermediate nodes are key nodes which can significantly affect the transitivity of trust. To the best of our knowledge, there are no algorithms for finding key nodes of the trust transitivity. To solve this problem, the concept of trust is presented, and a comprehensive model of the transitivity of trust is provided. Then, the key nodes search (KNS) algorithm is proposed to find out the key nodes of the trust transitivity. The KNS algorithm is verified with three real social network datasets and the results show that the algorithm can find out all the key nodes for each node in directed,
weighted, and non-fully connected social Internet of things (SIoT) networks.

Cloud storage is getting more and more popular as a new trend of data management. Data replication has been widely used as a means of increasing the data availability in large-scale cloud storage systems where failures are normal. However, most data replication schemes do not fully consider cost and latency issues when users need large amounts of remote replicas. We present animproved dynamic replication management scheme (IDRMS). By adding a prediction model, the optimal allocation of replicas among the cloud storage nodes is determined that the total communication cost and network delay are minimal. When the local data block is frequently requested, the data replicas can be moved to a closer or cheaper node for cost reduction and increased efficiency. Moreover, we replace
the B+ tree with the B*tree to speed up the search speed and reduce workload with the lowest blocking probability. We define the value of popularity to adjust the placement of replicas dynamically. We divide the data nodes in the network into hot nodes and cool nodes. By changing to visit cool nodes instead of hot nodes, we can balance the workload in the network. Finally, we implement IDRMS in Matlab simulation platform and simulation results demonstrate that IDRMS outperforms other replication management schemes in terms of communication cost and load balancing for large-scale cloud storage.

Network virtualization provides a powerful way of sharing substrate networks. Efficient allocation of network resources for multiple virtual networks (VNs) has always been a challenging task. In particular, with the demands of the customized VN requests are increasing, many problems arise as network conditions change dynamically. Especially, when the resources conflicting appear during the lifetime of VNs, it needs service provider (SP) to provide a fast and effective solution. Recently, software defined network (SDN) has emerged as a new networking paradigm, SDN’s centralized control and customizable routing features present new opportunities for convenient and flexible embedding VNs in the network. However, due to the limitations of the SDN, in the short term, replacing
all legacy devices in current operational networks by SDN-enabled switches is impractical. Thus, in our study, we focus on the scenario of VN embedding (VNE) in software-defined hybrid networks. In this work, first of all, we propose partially deploying SDN nodes, and then, we use the characteristics of SDN to allocate resources for VN requests, and redirect the path for requests conflict in hybrid SDN network. We formulate the problems and provide simple algorithms to solve them. Simulation results show that our scheme is high responsiveness and acceptance ratio.

This paper investigates the propagation of computer viruses and establishes a novel propagation model. In contrast to the existing models, this model can directly indicate the impact of removable media and external computers on the propagation of computer virus. The stability results of equilibrium point are derived by Hurwitz criterion and Bendixson Dulac criterion. The effectiveness of the proposed results is shown by numerical simulation. In order to show the superiority of the proposed model, some comparisons with the existing models are presented. The acceptable threshold and the reasonable strategies for suppressing the propagation of computer virus are also suggested, respectively.

The problem of solving differential equations and the properties of solutions have always been an important content of differential equation the study. In practical application and scientific research, it is difficult to obtain analytical solutions for most differential equations. In recent years, with the development of computer technology, some new intelligent algorithms have been used to solve differential equations. They overcomes the drawback of traditional methods and provide the approximate solution in closed form (i.e., continuous and differentiable). The least squares support vector machine (LS-SVM) has nice properties in solving differential equations. In order to further improve the accuracy of approximate analytical solutions and facilitative calculation, a novel method based on numerical methods and LS-SVM methods is presented to solve linear ordinary differential equations (ODEs). In our approach, a high precise of the numerical solution is added as a constraint to the nonlinear LS-SVM regression model, and the optimal parameters of the model are adjusted to minimize an appropriate error function. Finally, the approximate solution in closed form is obtained by solving a system of linear equations. The numerical experiments demonstrate that our proposed method can improve the accuracy of approximate solutions.