In the environment of heterogeneous wireless networks, it is vital to select a currently optimal network for every subscriber for each application. The use of multiple attribute decision making (MADM) for heterogeneous network selection can provide subscribers with satisfactory service quality. Consequently, converting heterogeneous network selection into a multiple attribute decision making (MADM) problem, we present an improved multiple attribute decision making (MADM) algorithm based on group decision theory in this paper. At first, the algorithm combines weight vectors of multiple attribute decision making to obtain a combinational weight vector. Then the results' compatibility will be assessed. If they do not meet the requirements of compatibility, the judgment matrix will be modified until a comprehensive vector that satisfies compatibility requirements is produced. Then the vector will be combined with SAW method for network selection. The simulation results show that the algorithm can provide users with satisfactory quality of service (QoS).

Getting along with the improvement of green communications, the energy efficiency (EE) of wireless network becomes more and more important. However, in multi-relay-assisted systems, the related work obviously depends on the end-to-end performance, whereas EE of relays has not attracted enough attention. The authors propose an orthogonal frequency division multiplexing (OFDM) multi-relay system based on amplify and forward (AF) mode. Taking incorporating EE and remaining energy as factor, decision criteria of attending cooperation is designed. The EE based asynchronous power iteration method is presented, and the existence and convergence of nash equilibrium (NE) is proven. Furthermore, a joint algorithm in subcarrier pairing, relay selection and power allocation is given. Genetic algorithm (GA) and iteration method are integrated to improve the convergence speed additionally. Simulation demonstrates the obvious improvement of EE, decrease of power consumption and increase of lifetime in relays of this algorithm with the constraint of minimum data transmission rate.

This paper presents a new hybrid system of the fuzzy c-means clustering algorithm and adaptive-two-stage linear approximation for the nonlinear distortion cancellation of the RF power amplifier (PA). This mechanism can eliminate the noise, adaptively model the PA’s instantaneous change, and then correct the nonlinear distortion efficiently. The paper puts forward using the FCM clustering algorithm for clustering the received signal to eliminate white noise, and then uses adaptive-two-stage linear approximation to fit the inverse function of the amplitude’s and phase’s nonlinear mapping in the training phase. Firstly, the parameters involved in the linear function and the similarity function are trained using gradient-descent and minimum mean-square error criterion. Secondly, the sample signals directly employ the proposed approach’s training result to eliminate nonlinear distortion. This hybrid method is easier to realize than the multi-segment linear approximation and could more efficiently reduce the received signal’s BER.

As the traditional character-oriented frame synchronization methods are no longer applicable to the byte-misaligned stream, and the efficiency of the bit-oriented method is hardly acceptable, a Character-Oriented Bit-Shift Stream Frame Synchronization Method (COBS-FS) is presented in this paper. In order to measure the performance of the given method, a bit-oriented frame synchronization method (KMP-FS), which is based on Knuth-Morris-Pratt algorithm, is used for comparison.COBS-FS is compared with a bit-oriented frame synchronization method (KMP-FS) which is based on Knuth-Morris-Pratt algorithm, and it is proved in theory that the formerthe COBS-FS has a much lower cost in frame header searching. Finally, experiments have been carried out on both methods, and the results show that the COBS-FS has a better performance than the KMP-FS in both computational effort and execution time.

Coordinated multi-point transmission/reception (CoMP) was proposed currently as an effective technology to improve cell-edge throughput in next-generation wireless systems. Most of the existing work discussed clustering methods mostly to maximize the edge user throughput while neglecting the problem of energy efficiency, such as those algorithm clustering base stations (BSs) of better channel condition and BSs of worse channel condition together. In addition, BSs usually increase the transmit power to achieve higher throughput without any considering of interference caused to other users, that may result in energy waste. The authors focus on the throughput maximizing problem while fully considering energy saving problem in CoMP systems. A coefficient is defined to describe the fitness of clusters. Then a sub-carrier allocation algorithm with clustering method is put forward for CoMP downlink, which can save the transmit power of BS and increase the throughput. Furthermore a power allocation scheme is proposed based on non-cooperation game; in which the transmit power is decreased by BSs generally to reach the Nash equation (NE). Simulation shows that the proposed sub-carrier allocation scheme and power allocation algorithm are better than the existing ones on users’ throughput while consumes much less energy.

Network traffic classification aims at identifying the application types of network packets. It is important for Internet service providers (ISPs) to manage bandwidth resources and ensure the quality of service for different network applications. However, most classification techniques using machine learning only focus on high flow accuracy and ignore byte accuracy. The classifier would obtain low classification performance for elephant flows as the imbalance between elephant flows and mice flows on Internet. The elephant flows, however, consume much more bandwidth than mice flows. When the classifier is deployed for traffic policing, the network management system cannot penalize elephant flows and avoid network congestion effectively. This article explores the factors related to low byte accuracy, and secondly, it presents a new traffic classification method to improve byte accuracy at the aid of data cleaning. Experiments are carried out on three groups of real-world traffic datasets, and the method is compared with existing work on the performance of improving byte accuracy. Experiment shows that byte accuracy increased by about 22.31% on average. The method outperforms the existing one in most cases.

Non-center network computing environments have some unique characteristics, such as instability, heterogeneity, autonomy, distribution and openness, which bring serious issues of security and reliability. This article proposes a brand-new credibility protection mechanism for resource sharing and collaboration in non-center network computing environments. First, the three-dimensional hierarchical classified topology (3DHCT) is proposed, which provides a basic framework for realizations of the identity credibility, the behavior credibility and the capability credibility. Next, the agent technology is utilized to construct the credibility protection model. This article also proposes a new comprehensive credibility evaluation algorithm with simple, efficient, quantitative and able to meet the requirements of evaluating behavior credibility and the capability credibility evaluation as well. The Dempster-Shafer theory of evidence and the combination rule are used to achieve the evaluation of the capability credibility. The behavior credibility is evaluated with the current and historical performance of nodes for providers and consumers to realize more accurate prediction. Based on the non-center network computing simulation test platform, simulation is been conducted to test the performance and validity of the proposed algorithms. Experiment and analysis show that the proposed algorithms are suitable for large-scale, dynamic network computing environments, and able to maintain the credibility for networks without relying on central node, make a non-center network gradually evolve into an orderly, stable and reliable computing environment efficiently.

Heterogeneous cellular networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low power base station (BS) overlapping the conventional macro cell. But due to the disparity between the transmit powers of the macro BS and the low power BS, cell association strategy developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic concentrated on the macro BS. In this paper, we propose a load-balance cell association scheme for heterogeneous cellular network aiming to maximize the network capacity. By relaxing the association constraints, we can get the upper bound of optimal solution and convert the primal problem into a convex optimization problem. Furthermore we propose a Lagrange multipliers based distributed algorithm by using Lagrange dual theory to solve the convex optimization, which converges to an optimal solution with a theoretical performance guarantee. With the proposed algorithm, mobile terminals (MTs) need to jointly consider their traffic type, received signal-to-interference-noise-ratios (SINRs) from BSs, and the load of BSs when they choose server BS. Simulation results show that the load balance between macro and pico BS is achieved and network capacity is improved significantly by our proposed cell association algorithm.

With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine (VM) is becoming a major issue for cloud providers. The existing virtual machine placement (VMP) solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization (MLU). On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.

Exploring theory and methods to analyze the impacts of non real-time services on jitter performance of real-time services is a quite challenging but meaningful job. This article puts forward a general theoretical method to calculate packet jitter in network node bearing mixed services. Based on queuing theory and Markov theory, the network node is modeled as a double-queue single-server and limited-cache queuing system with thresholds. Two-state Markov-modulated Bernoulli process (MMBP-2) and interrupted Bernoulli process (IBP) are used to model the arrival processes of real-time services and small data services respectively in a packet switched network. In order to depict the interaction between real-time services and small data services, a four-dimensional discrete-time Markov chain is implemented to describe the transition of the system states. By solving the system model, expressions for packet jitter are obtained. The results given by the model are then compared with the simulation results obtained by network simulator, version 3 (NS-3).

Network virtualization is a promising way to overcome the current ossification of the Internet. It is essential challenge to find effective, efficient and robust embedding algorithms for recovering virtual network. The virtual network mapping algorithm based on integer programming which was proposed months ago. But it did consider the faults of physical network resources, which is so called survivable virtual network embedding (VNE) problem. Previous strategies for enabling survivability in network virtualization focused on providing protection for the physical network or enhancing the virtual networks by providing backup physical resources in advance, and treated all the physical failures as link failures. In the article, a dynamic recovery method is proposed to solve the survivable virtual network embedding problem based on the integer programming VNE algorithm. The dynamic recovery method doesn’t need to backup physical resources and it makes more substrate resources which can be used in the embedding. The dynamic recovery process will be activated only when physical failures occur. Different algorithms are used to recovery node and link failures. Simulations show that the method helps to recover almost all of physical failures by finding the substitute nodes and paths, and its performance is very close to that of pure VNE method without considering physical failures.

In order to achieve maximization of parallelism, effective distribution of rendering tasks, balance between performance and flexibility in graphics processing pipeline, this article presents design, performance analysis and optimization for multi-core interactive graphics processing unit (MIGPU). This processor integrates twelve processing cores with specific instruction set architecture and many sophisticated application-specific accelerators into a 3D graphics engine. It is implemented on XC6VLX550T field programmable gate array (FPGA). MIGPU supports OpenGL2.0 with programmable front-end processor, vertex shader, plane clipper, geometry transformer, three-D clippers and pixel shaders. For boosting the performance of MIGPU, the relationship model is established between primitive types, vertices, pixels, and the effect of culling, clipping, and memory access, and shows a way to improve the speed up of the graphics pipeline. It is capable of assigning graphics rendering tasks to different processors for efficiency and flexibility. The pixel filling rate can reach to 40 Mpixel/s at its peak performance.

A hands-free method is proposed to control an electric powered wheelchair (EPW) based on surface electromyography (sEMG) signals. A CyberLink device is deployed to obtain and analyze forehead sEMG signals generated by the facial movements. The autoregressive (AR) model is used to extract sEMG features. Then, the back-propagation artificial neural network (BPANN) is proposed to recognize different facial movement patterns and improved by Bayesian regularization and Levenberg-Marquardt (LM) algorithm. A sEMG based human-machine interface (HMI) is designed to map facial movement patterns into corresponding control commands. The experimental results show that the method is simple, real-time and have a high recognition rate.

A direction-of-arrival (DOA) estimation algorithm is presented based on covariance differencing and sparse signal recovery, in which the desired signal is embedded in noise with unknown covariance. The key point of the algorithm is to eliminate the noise component by forming the difference of original and transformed covariance matrix, as well as cast the DOA estimation considered as a sparse signal recovery problem. Concerning accuracy and complexity of estimation, the authors take a vectorization operation on difference matrix, and further enforce sparsity by reweighted ?1-norm penalty. We utilize data-validation to select the regularization parameter properly. Meanwhile, a kind of symmetric grid division and refinement strategy is introduced to make the proposed algorithm effective and also to mitigate the effects of limiting estimates to a grid of spatial locations. Compared with the covariance-differencing-based multiple signal classification (MUSIC) method, the proposed is of salient features, including increased resolution, improved robustness to colored noise, distinguishing the false peaks easily, but with no requiring of prior knowledge of the number of sources.