When trying to estimate time-varying multipath channels by applying a basis expansion model (BEM) in orthogonal frequency division multiplexing (OFDM) systems, pilot clusters are contaminated by inter-carrier interference (ICI). The pilot cluster ICI (PC-ICI) degrades the estimation accuracy of BEM coefficients, which degrades system performance. In this paper, a PC-ICI suppression scheme is proposed, in which two coded symbols defined as weighted sums of data symbols are inserted on both sides of each pilot cluster. Under the assumption that the channel has Flat Doppler spectrum, the optimized weight coefficients are obtained by an alternating iterative optimization algorithm, so that the sum of the PC-ICI generated by the encoded symbols and the data symbols is minimized. By approximating the optimized weight coefficients, they are independent of the channel tap power. Furthermore, it is verified that the proposed scheme is robust to the estimation error of the normalized Doppler frequency offset and can be applied to channels with other types of Doppler spectra. Numerical simulation results show that, compared with the conventional schemes, the proposed scheme achieves significant improvements in the performance of PC-ICI suppression, channel estimation and system bit-error-ratio (BER).

Security researchers/vendors detect malicious websites based on several website features extracted by honeyclient analysis. However, web-based attacks continue to be more sophisticated along with the development of countermeasure techniques. Attackers detect the honeyclient and evade analysis using sophisticated JavaScript code. The evasive code indirectly identifies vulnerable clients by abusing the differences among JavaScript implementations. Attackers deliver malware only to targeted clients on the basis of the evasion results while avoiding honeyclient analysis. Therefore, we are faced with a problem in that honeyclients cannot analyze malicious websites. Nevertheless, we can observe the evasion nature, i.e., the results in accessing malicious websites by using targeted clients are different from those by using honeyclients. In this paper, we propose a method of extracting evasive code by leveraging the above differences to investigate current evasion techniques. Our method analyzes HTTP transactions of the same website obtained using two types of clients, a real browser as a targeted client and a browser emulator as a honeyclient. As a result of evaluating our method with 8,467 JavaScript samples executed in 20,272 malicious websites, we discovered previously unknown evasion techniques that abuse the differences among JavaScript implementations. These findings will contribute to improving the analysis capabilities of conventional honeyclients.

Efficiency and flexibility of collections have a significant impact on the overall performance of applications. The current approaches to implement collections have two main drawbacks: (i) they limit the efficiency of collections and (ii) they have not adequate support for collection composition. So, when the efficiency and flexibility of collections is important, the programmer needs to implement them himself, which leads to the loss of reusability. This article presents neoCollection, a novel approach to encapsulate collections. neoCollection has several distinguishing features: (i) it can be applied on data elements efficiently and flexibly (ii) composition of collections can be made efficiently and flexibly, a feature that does not exist in the current approaches. In order to demonstrate its effectiveness, neoCollection is implemented as an extension to Java and C++.

Smallcells have recently emerged as a potential approach for local area deployments that can satisfy high data rate requirements, reduce energy consumption and enhance network coverage. In this paper, we work on maximizing the weighted sum energy efficiency (WS-EE) for densely deployed smallcell networks. Due to the combinatorial and the general fractional program nature of the resource allocation problem, WS-EE maximization is non-convex and the optimal joint resource blocks (RBs) and power allocation is NP-hard. To solve this complex problem, we propose to decompose the primal problem into two subproblems (referred as RBs allocation and power control) and solve the subproblems sequentially. For the RBs allocation subproblem given any feasible network power profile, the optimal solution can be solved by maximizing throughput locally. For the power control subproblem, we propose to solve it locally based on a new defined pricing factor. Then, a distributed power control algorithm with guaranteed convergence is designed to achieve a Karush-Kuhn-Tucker (KKT) point of the primal problem. Simulation results verify the performance improvement of our proposed resource allocation scheme in terms of WS-EE. Besides, the performance evaluation shows the tradeoff between the WS-EE and the sum rate of the smallcell networks.

This paper theoretically revisits linear passive two-port systems from the viewpoint of power transfer. Instead of using the conventional S21 magnitude, we propose generalizing the kQ product as a figure of merit for two-port performance evaluation. We explore three examples of power transfer schemes, i.e. inductive, capacitive, and resistive channels. Starting from their voltage-current equations, the kQ formula is analytically derived for each scheme. The resultant formulas look different in appearance but are all physically consistent with ωM/R, which stems from the original definition of kQ product in a primitive transformer. After comprehensively learning from the three examples, we finally extend the theory to a black-box model that represents any kind of power transfer channel. In terms of general two-port Z-parameters, useful mathematical expressions are deduced for the optimum load, input impedance, and maximum power transfer efficiency. We also supplement the theory with helpful graphics that explain how the generalized kQ behaves as a function of the circuit parameters.

A 4.5-/4.9-GHz band-selective GaN HEMT high-efficiency power amplifier has been designed and evaluated for next-generation wireless communication systems. An optimum termination impedance for each high-efficiency operation band was changed by using PIN diodes inserted into a harmonic treatment circuit at the output side. In order to minimize the influence of the insertion loss of the PIN diodes, an additional line is arranged in parallel with the open-ended stub used for second harmonic treatment, and the line and stub are connected with the PIN diodes to change the effective characteristic impedance. The fabricated GaN HEMT amplifier achieved a maximum power-added efficiency of 57% and 66% and a maximum drain efficiency of 62% and 70% at 4.6 and 5.0GHz, respectively, with a saturated output power of 38dBm, for each switched condition.

The power consumption of server platforms has been increasing as the amount of hardware resources equipped on them is increased. Especially, the capacity of DRAM continues to grow, and it is not rare that DRAM consumes higher power than processors on modern servers. Therefore, a reduction in the DRAM energy consumption is a critical challenge to reduce the system-level energy consumption. Although it is well known that improving row buffer locality(RBL) and bank-level parallelism (BLP) is effective to reduce the DRAM energy consumption, our preliminary evaluation on a real server demonstrates that RBL is generally low across 15 multithreaded benchmarks. In this paper, we investigate the memory access patterns of these benchmarks using a simulator and observe that cache line-grained channel interleaving schemes, which are widely applied to modern servers including multiple memory channels, hurt the RBL each of the benchmarks potentially possesses. In order to address this problem, we focus on a row-grained channel interleaving scheme and compare it with three cache line-grained schemes. Our evaluation shows that it reduces the DRAM energy consumption by 16.7%, 12.3%, and 5.5% on average (up to 34.7%, 28.2%, and 12.0%) compared to the other schemes, respectively.

In this letter, we study the resource allocation for the downlink cooperative non-orthogonal multiple access (NOMA) systems based on the amplifying-and-forward protocol relay transmission. A joint power allocation and amplification gain selection scheme are proposed. Fractional programming and the iterative algorithm based on the Lagrangian multiplier are used to allocate the transmit power to maximize the energy efficiency (EE) of the systems. Simulation results show that the proposed scheme can achieve higher energy efficiency compared with the minimum power transmission (MPT-NOMA) scheme and the conventional OMA scheme.

This paper presents a meta-structured circular polarized array antenna with wide scan angle. In order to widen the scanning angle of array antennas, this paper investigates unit antenna beamwidth and the coupling effects between array elements, both of which directly affect the steering performance. As a result, the optimal array distance, the mode configuration, and the antenna structure are elucidated. By using the features of the miniaturized mu-zero resonance (MZR) antenna, it is possible to design the antenna at optimum array distance for wide beamwidth. In addition, by modifying via position and gap configuration of the antenna, it is possible to optimize the mode configuration for optimal isolation. Finally, the 3dB steerable angle of 66° is successfully demonstrated using a 1x8 MZR CP antenna array without any additional decoupling structure. The measured beam patterns at a scan angle of 0°, 22°, 44°, and 66°agree well with the simulated beam patterns.

For general multiple-input multiple-output (MIMO) interference networks, determining the feasibility conditions of interference alignment (IA) to achieve the maximum degree of freedom (DoF), is tantamount to accessing the maximum spatial resource of MIMO systems. In this paper, from the view of antenna configuration, we first explore the IA feasibility in the K-user MIMO interference channel (IC), G-cell MIMO interference broadcast channel (IBC) and interference multiple access channel (IMAC). We first give the concept of the equalized antenna, and all antenna configurations are divided into two categories, equalized antennas and non-equalized ones. The feasibility conditions of IA system with equalized antennas are derived, and the feasible and infeasible regions are provided. Furthermore, we study the correlations among IC, IBC and IMAC. Interestingly, the G-cell MIMO IBC and IMAC are two special ICs, and a systemic work on IA feasibility for these three interference channels is provided.

With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, F-measure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.

In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices.

In this letter, we consider a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system, in which the confidential message intended for the information receiver (IR) should be kept secret from the energy receiver (ER). Our goal is to design the optimal transmit covariance matrix so as to maximize the secrecy energy efficiency (SEE) of the system while guaranteeing the secrecy rate, energy harvesting and transmit power constraints. To deal with the original non-convex optimization problem, we propose an alternating optimization (AO)- based algorithm and also prove its convergence. Simulation results show that the proposed algorithm outperforms conventional design methods in terms of SEE.

The energy consumption of network virtualization environments (NVEs) has become a critical issue. In this paper, we focus on reducing the data switching energy consumption of NVE. We first analyze the data switching energy of NVE. Then, we propose a dynamic energy efficient virtual link resource reallocation (eEVLRR) approach for NVE. eEVLRR dynamically reallocates the energy efficient substrate resources (s-resources) for virtual links with dynamic changes of embeddable s-resources to save the data switching energy. In order to avoid traffic interruptions while reallocating, we design a cross layer application-session-based forwarding model for eEVLRR that can identify and forward each data transmission flow along the initial specified substrate data transport path until end without traffic interruptions. The results of performance evaluations show that eEVLRR not only guarantees the allocated s-resources of virtual links are continuously energy efficient to save data switching energy but also has positive impacts on virtual network acceptance rate, revenues and s-resources utilization.

This paper describes a top-down design methodology to optimize resonant capacitance in a wireless power transfer system with 3-D stacked two receivers. A 1:2 selective wireless power transfer is realized by a frequency/time division multiplexing scheme. The power transfer function is analytically formulated and the optimum tuning capacitance is derived, which is validated by comparing with system simulation results. By using the optimized values, power transfer efficiencies at 6.78MHz and 13.56MHz are simulated to be 80% and 84%, respectively, which are <3% worse than a conventional wireless power transfer system.

Internal quantum efficiency (IQE) is usually estimated from temperature dependence of photoluminescence (PL) intensity by assuming that the IQE at cryogenic temperature is unity. III-nitride samples, however, usually have large defect density, and the assumption is not necessarily valid. In 2016, we proposed a new method to estimate accurate IQE values by simultaneous PL and photo-acoustic (PA) measurements, and demonstratively evaluated the IQE values for various GaN samples. In this study, we have applied the method to InGaN quantum-well active layers and have estimated the IQE values and their excitation carrier-density dependence in the layers.

Approximate computing is a promising solution for future energy-efficient designs because it can provide great improvements in performance, area and/or energy consumption over traditional exact-computing designs for non-critical error-tolerant applications. However, the most challenging issue in designing approximate circuits is how to guarantee the pre-specified computation accuracy while achieving energy reduction and performance improvement. To address this problem, this paper starts from the state-of-the-art general approximate adder model (GeAr) and extends it for more possible approximate design candidates by relaxing the design restrictions. And then a maximum-error-distance-based performance/accuracy formulation, which can be used to select the performance/energy-accuracy optimal design from the extended design space, is proposed. Our evaluation results show the effectiveness of the proposed method in terms of area overhead, performance, energy consumption, and computation accuracy.

Cellular heterogeneous networks (HetNets) with densely deployed small cells can effectively boost network capacity. The co-channel interference and the prominent energy consumption are two crucial issues in HetNets which need to be addressed. Taking the traffic variations into account, this paper proposes a theoretical framework to analyze spectral efficiency (SE) and energy efficiency (EE) considering jointly further-enhanced inter-cell interference coordination (FeICIC) and spectrum allocation (SA) via a stochastic geometric approach for a two-tier downlink HetNet. SE and EE are respectively derived and validated by Monte Carlo simulations. To create spectrum and energy efficient HetNets that can adapt to traffic demands, a non-convex optimization problem with the power control factor, resource partitioning fraction and number of subchannels for the SE and EE tradeoff is formulated, based on which, an iterative algorithm with low complexity is proposed to achieve the sub-optimal solution. Numerical results confirm the effectiveness of the joint FeICIC and SA scheme in HetNets. Meanwhile, a system design insight on resource allocation for the SE and EE tradeoff is provided.

Heterogeneous Small Cell Network (HSCN) will have wide application given its ability to improve system capacity and hot spot coverage. In order to increase the efficiency of spectrum and energy, a great deal of research has been carried out on radio resource management in HSCN. However, it is a remarkable fact that the user experience in terms of traffic rate demands has been neglected in existing research with excessive concentration on network capacity and energy efficiency. In this paper, we redefined the energy efficiency (EE) and formulate the joint optimization problem of user experience and energy efficiency maximization into a mixed integer non-linear programming (MINLP) problem. After reformulating the optimization problem, the joint subchannel (SC) allocation and power control algorithm is proposed with the help of cluster method and genetic algorithm. Simulation results show that the joint SC allocation and power control algorithm proposed has better performance in terms of user experience and energy consumption than existing algorithms.