This article investigates the significant performances of orthogonal frequency division multiplexing (OFDM)-based dual-hop system in the presence of phase noise (PN). A scenario with Rayleigh fading statistics on both hops is assumed. Amplification factor for this amplify-and-forward (AF) relay networks system is divided into two conditions, average power scaling (APS) and instantaneous power scaling (IPS). Before deriving signal-to-noise ratios (SNR) under APS and IPS, the Gaussianity of intercarrier interference (ICI) is proved firstly. The accurate closed-form expressions of end-to-end SNR cumulative distribution functions (CDF) and probability density functions (PDF) for both cases are obtained later. With the help of moment generating functions (MGF), we have closed-form asymptotic expressions of bit error rate (BER), which show that the BER of system in the presence of PN cannot exceed a fixed level even when SNR in high regime. Finally, simulations verify accuracy of the results. Conclusion analysis will provide a useful help in future application of the system.

In this paper, the performance of frequency synchronization in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system is analyzed for the purpose of carrier frequency offset (CFO) estimation and compensation. Speci?cally, a joint transmit antenna selection (ST) and receive maximum ratio combining (MRC）（ST/MRC） method is adopted, that is, only one transmit antenna with the highest channel power is selected while MRC is used at the receiver to maximize the sum of frequency synchronization metric. The mean square error (MSE) closed-form expressions of CFO estimation are derived for several antenna con?gurations. Simulations in both ?at and multipath fading channels validate the theoretical analysis.

Opportunistic routing (OR) could adapt to dynamic wireless sensor networks (WSNs) because of its inherent broadcast nature. Most of the existing OR protocols focus on the variations of propagation environment which are caused by channel fading. However, a few works deal with the dynamic scenario with mobile nodes. In this paper, a mobile node (MN) aware OR (MN-OR) is proposed and applied to a WSN in the high-speed railway scenario where the destination node is deployed inside a high speed moving train, and the MN-OR not only considers the mobility of node but also utilizes the candidate waiting time induced by the timer-based coordination scheme. Specifically, to reduce the number of duplicate transmissions and mitigate the delay of information transmission, a new selection strategy of the candidate forwarders is presented. In addition, two priority assignment methods of the candidate forwarders are proposed for the general relay nodes (GRNs) and the rail-side nodes (RSNs) according to their different routing requirements. Extensive simulation results demonstrate that the proposed MN-OR protocol can achieve better network performances compared with some existing routing schemes such as the well-known Ad-hoc on-demand distance vector routing (AODV) and the extremely opportunistic routing (ExOR) protocols.

This paper investigates the performance of the method used to reduce the decoding complexity of rateless codes through the deletion of the received symbols with low reliability. In the decoder, the received symbols whose absolute value of logarithm likelihood ratio (LLR) is lower than the threshold are removed, together with their corresponding edges, and thus not involved in the decoding process. The relationship between the deletion probability and the likelihood ratio deletion threshold is derived. The average mutual information per received symbol is analyzed in the case of deletion. The required number of symbols for the decoder to keep the same performance as regular decoding decreases since the average mutual information per symbol increases with the deletion, thus reducing the decoding complexity. This paper analyzes the reduction of decoding computations and the consequent transmission efficiency loss from the perspective of mutual information. The simulation results of decoding performance are consistent with those of the theoretical analysis, which show that the method can effectively reduce the decoding complexity at the cost of a slight loss of transmission efficiency.

The concept of dense small cell has been recently emerged as a promising architecture that can signi?cantly improve spectrum efficiency and system capacity. However, it brings frequent handover for user equipment (UE). Furthermore, this will bring a great deal of signaling overhead to the core network. Virtual technology has been received widespread attention for solving this problem. Its essence is to form virtual cells by clustering various terminals properly. The local mobility management proposed recently is based on the virtual technology. Therefore, the formation process of virtual cells is the basis for the research in local mobility management. So clustering scheme for dense small cell network has been studied in this paper, and a maximum benefit merging algorithm based on undirected weighted graph has been proposed. There are X2 interfaces between the cluster head and each of cluster members within the same cluster. The cluster heads manage the handover among cluster members acting as the local anchors. The proposed clustering scheme is useful for local mobility management. The simulation results show that the proposed clustering algorithm can decrease the signaling overhead more than 70% and 20% compared with the non-clustering algorithm and other clustering algorithms respectively.

It is of great value and significance to model the interests of microblog user in terms of business and sociology. This paper presents a framework for mining and analyzing personal interests from microblog text with a new algorithm which integrates term frequency-inverse document frequency (TF-IDF) with TextRank. Firstly, we build a three-tier category system of user interest based on Wikipedia. In order to obtain the keywords of interest, we preprocess the posts, comments and reposts in different categories to select the keywords which appear both in the category system and microblogs. We then assign weight to each category and calculate the weight of keyword to get TF-IDF factors. Finally we score the ranking of each keyword by the TextRank algorithm with TF-IDF factors. Experiments on real Sina microblog data demonstrate that the precision of our approach significantly outperforms other existing methods.

For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping (SLAM), a novel SLAM based on improved Rao-Blackwellized H∞ particle filter (IRBHF-SLAM) algorithm is proposed. The iterated unscented H∞ filter (IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor’s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM (UFastSLAM) under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system (ROS), runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX (URG) laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional (2-D) grid-map with high precision in different environments.

Gallium nitride (GaN) high electron mobility transistor (HEMT) with symmetrical structure as a control device is discussed in this paper. The equivalent circuit model is proposed on the basis of physical and electrical properties of the GaN HEMT device. A transistor with 0.5 μm gate length and 6 × 125 μm gate width is fabricated to verify the model, which can be treated as a single pole single throw (SPST) switch due to the ON state and OFF state. The measurement results show a good agreement with the simulation results, which demonstrates the effectiveness of the proposed model.

Electrical connectors play a significant role in the electronic and communication systems. As they are often exposed in the atmosphere environment, it is extremely easy for them to cause electrical contact failure. It is essential to carry out the reliability modeling and predict the lifetime. In the present work, the accelerated lifetime testing method which is on account of the uniform design method was designed to obtain the degradation data under multiple environmental stresses of temperature and particulate contamination for electrical connectors. Based on the degradation data, the pseudo life can be acquired. Then the reliability model was established by analyzing the pseudo life. Accordingly, the reliability function and reliable lifetime function were set up, and the reliable lifetime of the connectors under the multiple environment stresses of temperature and particulate contamination could be predicted for electrical connectors.

Existing learning-based super-resolution (SR) reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network (CNN) and spatio-temporal similarity (STCNN-SR) was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not only the mapping relationship among associated low-resolution (LR) and high-resolution (HR) image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.

In this paper, a modified susceptible infected susceptible (SIS) epidemic model is proposed on community structure networks considering birth and death of node. For the existence of node’s death would change the topology of global network, the characteristic of network with death rate is discussed. Then we study the epidemiology behavior based on the mean-field theory and derive the relationships between epidemic threshold and other parameters, such as modularity coefficient, birth rate and death rates (caused by disease or other reasons). In addition, the stability of endemic equilibrium is analyzed. Theoretical analysis and simulations show that the epidemic threshold increases with the increase of two kinds of death rates, while it decreases with the increase of the modularity coefficient and network size.

Conventional outdoor navigation systems are usually based on orbital satellites, e.g., global positioning system (GPS) and global navigation satellite system (GLONASS). The latest advances from wearable, e.g., BaiduEye and Google Glass, have enabled new approaches to leverage information from the surrounding environment. For example, they enable the change from passively receiving information to actively requesting information. Thus, such changes might inspire brand new application scenarios that were not possible before. In this work, we propose a vision-based navigation system based on wearable like Baidu Eye. We discuss the associated challenges and propose potential solutions for each of them. The system utilizes crowd sensing to collect and build a traffic signpost database for positioning reference. Then it leverages context information, such as cell identification (Cell ID), signal strength, and altitude combined with traffic sign detection and recognition to enable real-time positioning. A hybrid cloud architecture is proposed to enhance the capability of sensing devices (SD) to realize the proposed vision.