In wireless cellular networks, the interference alignment (IA) is a promising technique for interference management. A new IA scheme for downlink cellular network with multi-cell and multi-user was proposed. In the proposed scheme, the interference in the networks is divided into inter-cell interference (ICI) among cells and inter-user interference (IUI) in each cell. The ICI is aligned onto a multi-dimensional subspace by multiplying the ICI alignment precoding matrix which is designed by the singular value decomposition (SVD) scheme at the base station (BS) side. The aligned ICI is eliminated by timing the interference suppression matrix which is designed by zero-forcing (ZF) scheme at the user equipment (UE) side. Meanwhile, the IUI is aligned by multiplying the IUI alignment precoding matrix which is designed based on Nash bargaining solution (NBS) in game theory. The NBS is solved by the particle swarm optimization (PSO) method. Simulations show that, compared with the traditional ZF IA scheme, the proposed scheme can obtain higher data rate and guarantee the data rate fairness of UEs with little additional complexity.

Considering the joint channel estimation and data detection in time-varying orthogonal frequency division multiplexing (OFDM) and addressing transmission performance degradation induced by the severe inter-carrier interference (ICI) at very high speed, a new progressive iterative channel estimation scheme is proposed. To alleviate the error propagation of the inaccurate data due to ICI, the measurement subcarriers in the Kalman filter is designed to be extended from pilots subcarriers to all the subcarriers progressively through the iterations. Furthermore, in iteration process, the interference of the non-pilot data to the measurement subcarriers is considered to be part of noise in the modified Kalman filter, which improves the estimation accuracy. Simulation indicates that the proposed scheme improves the performance in fast time-varying situation.

With rapid development communication system, high signal to noise ratio (SNR) system is required. In high frequency bandwidth, high loss, low Q inductors and high noise figure is a significant challenge with on-chip monolithic microwave integrated circuits (MMICs). To overcome this problem, high Q, low loss transmission line characteristics was analyzed. Compared with the same inductor value of the lumped component and the transmission line, it has a higher Q value and lower loss performance in high frequency, and a 2-stage common-source low noise amplifier (LNA) was presented, which employs source inductor feedback technology and high Q low loss transmission line matching network technique with over 17.6 dB small signal gain and 1.1 dB noise figure in 15 GHz–18 GHz. The LNA was fabricated by WIN semiconductors company 0.15 μm gallium arsenide (GaAs) P high electron mobility transistor (P-HEMT) process. The total current is 15 mA, while the DC power consumption is only 45 mW.

The performance of massive multiple-input multiple-output (MIMO) system is limited by pilot contamination. To reduce the pilot contamination, uplink and downlink precoding algorithms are put forward based on interference alignment criterion. In the uplink receiving processing, the target function aligns the pilot contamination and the interference signals to the same null space and acquires the maximal space degree of the desired signals. The uplink receiving precoding matrix is solved on maximal signal to interference plus noise ratio (SINR) criterion considering the impact of the pilot contamination on channel estimations. The uplink receiving precoding matrix is used as the downlink transmitting precoding matrix. Exploiting the channel reciprocity, it is proved that, if the uplink receiving precoding matrix achieves maximal SINR, the identical precoding matrix can be used in the downlink transmission and acquires maximal signal to leakage plus noise ratio (SLNR). Simulations show that the spectrum efficiency of the proposed algorithm can reach about 1.5 times higher than that of popular matched filtering (MF) precoding algorithm, and about 1.1 times higher than multi-cell minimum mean square error (MMSE) precoding algorithm. The performance of the proposed algorithm can be improved approximately linearly with the increasing of the number of antennas.

Recently, Internet energy efficiency is paid more and more attention. New Internet architectures with more energy efficiency were proposed to promote the scalability in energy consumption. The content-centric networking (CCN) proposed a content-centric paradigm which was proven to have higher energy efficiency. Based on the energy optimization model of CCN with in-network caching, the authors derive expressions to tradeoff the caching energy and the transport energy, and then design a new energy efficiency cache scheme based on virtual round trip time (EV) in CCN. Simulation results show that the EV scheme is better than the least recently used (LRU) and popularity based cache policies on the network average energy consumption, and its average hop is also much better than LRU policy.

The message delivery ratio and transmission delay is affected deeply by road traffic flow in vehicular ad hoc networks (VANETs). An opportunistic routing based on geography and road traffic flow for VANETs (ORRIS) was proposed. ORRIS leverages the knowledge of geography positions, motion vectors and road traffic flows. In order to estimate the traffic flow density, the history of encounter number of the vehicles in the opposite direction is considered in ORRIS. The forwarding decisions are made by distributed vehicles based on the geography topology and the road traffic flow. The real map based simulation results show that ORRIS has a better performance than other algorithms, especially when the road traffic is busy or the traffic flow rates have great differences between roads.

The host load prediction problem in cloud computing has also been received much attention. To solve this problem, we have to use the historical load data to predict the future load level. Accurate prediction methods are useful for host load balance and virtual machine migration. Although cloud is likely to grids at some extent, the length of tasks are much shorter and host loads change more frequently with higher noise. The above characteristics introduce challenges for host load prediction. In this paper, based on the proposed exponentially segmented pattern and the corresponding transformation, prediction problem is transformed into the traditional classification problem. This classification problem can be solved based on the traditional methods, and features are given for training the classification model. For achieving accurate prediction, a new feature periodical coefficient is introduced and some existed classification methods are implemented. Experiments on the real world dataset invalidate the efficiency of the new proposed feature, which is in the most effective combinations of features, it increases successful rate (SR) 1.33%~2.82% and decreases the mean square error (MSE) 1.37%~2.91%. And the results also show that support vector machine (SVM) method can achieve nearly the same performance as the Bayes methods and their performance is about 50% higher in successful rate and 17% better in the mean square error compared to the existed methods.

With expansion of cloud-based applications, it is necessarily for business service provider to deal with how to provision resources to server customers. In cloud download, the service provider faces great challenge on determining when to process job and when to notify the user to meet the conflicting objectives, i.e., the service profit maximization and user satisfaction maximization. An economic model was proposed to find job and notification scheduling strategies for making tradeoffs between user satisfaction and profit of the system. In this model, optimization problems are formulated as follows, profit maximization under a satisfaction target and then satisfaction optimization with a profit bound. By solving these optimization problems, the optimal scheduling strategies are obtained under various quality requirements of service. It is demonstrated that the proposed strategy can obtain better performance than the system without the intelligent scheduling strategy.

Based on observation of the growing mechanism in Twitter-like online social networks, an online social network (OSN) evolution model was proposed. a renewal mechanism for the old nodes and an accelerated growth mechanism was introduced for the new nodes, comparing with the native copying model. Topological characteristics of the generated networks, such as degree distribution, average shortest-path length and clustering coefficient, are analyzed and numerized. These properties are validated with some crawled datasets of real online social networks.

A structure-based microstrip passband filter with the center frequency of 2.6 GHz was put forward by using double-mode resonator based on defected ground structure (DGS) technology. A double-mode coupling resonator combined with DGS structure was used to achieve the resonance frequency and the filter size. CST microwave studio was used to optimize the attenuation performance to get the filter parameters. A structure-based passband filter was fabricated. Simulation is consistent with the measurement. Performance shows that the proposed filter could be suitable for the time division-long term devolution (TD-LTE) microwave systems at 2.6 GHz.

Compressed sensing (CS) algorithm enables sampling rates significantly under classical Nyquist rate without sacrificing reconstructed image quality. It is known that, a great number of images have many similar areas which are composed by the same number of grayscale or color. A new CS scheme, namely clustering compressed sensing (CCS), was proposed for image compression, and it introduces clustering algorithm onto framework of CS based on similarity of image blocks. Instead of processing the image as a whole, the image is firstly divided into small blocks, and then the clustering algorithm was proposed to cluster the similar image blocks. Afterwards, the optimal public image block in each category is selected as the representative for transmission. The discrete wavelet transform (DWT) and Gaussian random matrix are applied to each optimal public image block to obtain the random measurements. Different from equal measurements, the proposed scheme adaptively selects the number of measurements based on different sparsity of image blocks. In order to further improve the performance of the CCS algorithm, the unequal-CCS algorithm based on the characteristics of wavelet coefficients was proposed as well. The low frequency coefficients are retained to ensure the quality of reconstructed image, and the high frequency coefficients are compressed by the CCS algorithm. Experiments on images demonstrate good performances of the proposed approach.

Based on the analysis of typical hybrid-type content addressable memory (CAM) structures, a hybrid-type CAM architecture with lower power consumption and higher stability was proposed. This design changes the connection of a N-type metal-oxide-semiconductor (NMOS) transistor in the control circuit, which greatly reduces the power consumption during comparison by making the match line simply discharge to the NMOS threshold voltage. A comparative study was made between conventional and the proposed hybrid-type CAM architecture by semiconductor manufacturing international corporation (SMIC) 65 nm complementary metal-oxide-semiconductor (CMOS) technology. Simulation shows that the power consumption of the proposed structure is reduced by 23%. Furthermore, the proposed design also adjusts the match line (ML) discharge path. In case that, the not and type (NAND-type) block is matched and the not or type (NOR-type) block is mismatched, the jitter voltage on the match line can be decreased largely.

Manipulated digital image is got interesting in recent years. Digital images can be manipulated more easily with the aid of powerful image editing software. Forensic techniques for authenticating the integrity of digital images and exposing forgeries are urgently needed. A geometric-based forensic technique which exploits the principle of vanishing points is proposed. By means of edge detection and straight lines extraction, intersection points of the projected parallel lines are computed. The normalized mean value (NMV) and normalized standard deviation (NSD) of the distances between the intersection points are used as evidence for image forensics. The proposed method employs basic rules of linear perspective projection, and makes minimal assumption. The only requirement is that the parallel lines are contained in the image. Unlike other forensic techniques which are based on low-level statistics, this method is less sensitive to image operations that do not alter image content, such as image resampling, color manipulation, and lossy compression. This method is demonstrated with images from York Urban database. It shows that the proposed method has a definite advantage at separating authentic and forged images.

Fix n electrons on the disk . They generate a static electric field. Let a be an electron located in the disk . It shows that the closed disk with center a and radius R contains at least one static equilibrium point.

The semi-tensor product (STP) of matrices was used in the article, as a new matrix analysis tool, to investigate the problem of verification of self-verifying automata (SVA). SVA is a special variant of finite automata which is essential to nondeterministic communication with a limited number of advice bits. The status, input and output symbols are expressed in vector forms, the dynamic behaviour of SVA is modelled as an algebraic equation of the states and inputs, in which the matrix product is STP. By such algebraic formulation, three necessary and sufficient conditions are presented for the verification problem, by which three algorithms are established to find out all the strings which are accepted, rejected, or unrecognized by a SVA. Testing examples show the correctness of the results.