In this paper, the artificial noise (AN)-aided multiple-input single-output (MISO) cognitive radio network with simultaneous wireless information and power transfer (SWIPT) is considered, in which the cognitive user adopts the power-splitting (PS) receiver architecture to simultaneously decode information and harvest energy. To support secure communication and facilitate energy harvesting, AN is transmitted with information signal at cognitive base station (CBS). The secrecy energy efficiency (SEE) maximization problem is formulated with the constraints of secrecy rate and harvested energy requirements as well as primary user's interference requirements. However, this challenging problem is non-convex due to the fractional objective function and the coupling between the optimization variables. For tackling the challenging problem, a double-layer iterative optimization algorithm is developed. Specifically, the outer layer invokes a one-dimension search algorithm for the newly introduced tight relaxation variable, while the inner one leverages the Dinkelbach method to make the fractional optimization problem more tractable. Furthermore, closed-form expressions for the power of information signal and AN are obtained. Numerical simulations are conducted to demonstrate the efficiency of our proposed algorithm and the advantages of AN in enhancing the SEE performance.

Green cognitive radio (CR) plays an important role in offering secondary users (SUs) with more spectrum with smaller energy expenditure. However, the energy efficiency (EE) issues associated with green CR for fading channels have not been fully studied. In this paper, we investigate the average EE maximization problem for spectrum-sharing CR in fading channels. Unlike previous studies that considered either the peak or the average transmission power constraints, herein, we considered both of these constraints. Our aim is to maximize the average EE of SU by optimizing the transmission power under the joint peak and average transmit power constraints, the rate constraint of SU and the quality of service (QoS) constraint of primary user (PU). Specifically, the QoS for PU is guaranteed based on either the average interference power constraint or the PU outage constraint. To address the non-convex optimization problem, an iterative optimal power allocation algorithm that can tackle the problem efficiently is proposed. The optimal transmission powers are identified under both of perfect and imperfect channel side information (CSI). Simulations show that our proposed scheme can achieve higher EE over the existing scheme and the EE achieved under perfect CSI is better than that under imperfect CSI.

This paper presents a novel statistic computation technique for energy detection-based spectrum sensing with multiple antennas. The presented technique computes the statistic for signal detection after combining all the signals. Because the computation of the statistic for all the received signals is not required, the presented technique reduces the computational complexity. Furthermore, the absolute value of all the received signals are combined to prevent the attenuation of the combined signals. Because the statistic computations are not required for all the received signals, the reduction of the computational complexity for signal detection can be expected. Furthermore, the presented technique does not need to choose anything, such as the binary phase rotator in the conventional technique, and therefore, the performance degradation due to wrong choices can be avoided. Numerical examples indicate that the spectrum sensing performances of the presented technique are almost the same as those of conventional techniques despite the complexity of the presented technique being less than that of the conventional techniques.

This paper presents a novel spectrum sensing technique based on selection diversity combining in cognitive radio networks. In general, a selection diversity combining scheme requires a period to select an optimal element, and spectrum sensing requires a period to detect a target signal. We consider that both these periods are required for the spectrum sensing based on selection diversity combining. However, conventional techniques do not consider both the periods. Furthermore, spending a large amount of time in selection and signal detection increases their accuracy. Because the required period for spectrum sensing based on selection diversity combining is the summation of both the periods, their lengths should be considered while developing selection diversity combining based spectrum sensing for a constant period. In reference to this, we discuss the spectrum sensing technique based on selection diversity combining. Numerical examples are shown to validate the effectiveness of the presented design techniques.

A triple-band CP rectenna for ambient RF energy harvesting is presented in this paper. A simple broadband CP slot antenna has been proposed with the bandwidth of 51.1% operating from 1.53 to 2.58GHz, which can cover GSM-1800, UMTS-2100 and 2.45GHz WLAN bands. Accordingly, a triple-band rectifying circuit is designed to convert RF energy in the above bands, with the maximum RF-DC conversion efficiency of 42.5% at a relatively low input power of -5dBm. Additionally, the rectenna achieves the maximum conversion efficiency of 12.7% in the laboratory measurements. The measured results show a good performance in the laboratory measurements.

To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.

This paper investigates the secrecy energy efficiency maximization (SEEM) problem in a simultaneous wireless information and power transfer (SWIPT) system, wherein a legitimate user (LU) exploits the power splitting (PS) scheme for simultaneous information decoding (ID) and energy harvesting (EH). To prevent interference from eavesdroppers on the LU, artificial noise (AN) is incorporated into the confidential signal at the transmitter. We maximize the secrecy energy efficiency (SEE) by joining the power of the confidential signal, the AN power, and the PS ratio, while taking into account the minimum secrecy rate requirement of the LU, the required minimum harvested energy, the allowed maximum radio frequency transmission power, and the PS ratio. The formulated SEEM problem involves nonconvex fractional programming and is generally intractable. Our solution is Lagrangian relaxation method than can transform the original problem into a two-layer optimization problem. The outer layer problem is a single variable optimization problem with a Lagrange multiplier, which can be solved easily. Meanwhile, the inner layer one is fractional programming, which can be transformed into a subtractive form solved using the Dinkelbach method. A closed-form solution is derived for the power of the confidential signal. Simulation results verify the efficiency of the proposed SEEM algorithm and prove that AN-aided design is an effective method for improving system SEE.

This paper proposes a successive interference cancellation (SIC) of independent component analysis (ICA) aided spatial division multiple access (SDMA) for Gaussian filtered frequency shift keying (GFSK) in Bluetooth low energy (BLE) systems. The typical SDMA scheme requires estimations of channel state information (CSI) using orthogonal pilot sequences. However, the orthogonal pilot is not embedded in the BLE packet. This fact motivates us to add ICA detector into BLE systems. In this paper, focusing on the covariance matrix of ICA outputs, SIC can be applied with Cholesky decomposition. Then, in order to address the phase ambiguity problems created by the ICA process, we propose a differential detection scheme based on the MAP algorithm. In practical scenarios, it is subject to carrier frequency offset (CFO) as well as symbol timing offset (STO) induced by the hardware impairments present in the BLE peripherals. The packet error rate (PER) performance is evaluated by computer simulations when BLE peripherals simultaneously communicate in the presence of CFO and STO.

A lumped element kinetic inductance detector (LeKID) relying on a superconducting resonator is a promising candidate for sensing high energy particles such as neutrinos, X-rays, gamma-rays, alpha particles, and the particles found in the dark matter owing to its large-format capability and high sensitivity. To develop a high energy camera, we formulated design rules based on the experimental results from niobium (Nb)-based LeKIDs at 1 K irradiated with alpha-particles of 5.49 MeV. We defined the design rules using the electromagnetic simulations for minimizing the crosstalk. The neighboring pixels were fixed at 150 µm with a frequency separation of 250 MHz from each other to reduce the crosstalk signal as low as the amplifier-limited noise level. We examined the characteristics of the Nb-based resonators, where the signal decay time was controlled in the range of 0.5-50 µs by changing the designed quality factor of the detectors. The amplifier noise was observed to restrict the performance of our device, as expected. We improved the energy resolution by reducing the filling factor of inductor lines. The best energy resolution of 26 for the alpha particle of 5.49 MeV was observed in our device.

In this study, product of two independent and non-identically distributed (i.n.i.d.) random variables (RVs) for κ-µ fading distribution and α-µ fading distribution is considered. The statistics of the product of RVs has been broadly applied in a large number of communications fields, such as cascaded fading channels, multiple input multiple output (MIMO) systems, radar communications and cognitive radios (CR). Exact close-form expressions of probability density function (PDF) and cumulative distribution function (CDF) with exact series formulas for the product of two i.n.i.d. fading distributions κ-µ and α-µ are deduced more accurately to represent the provided product expressions and generalized composite multipath shadowing models. Furthermore, ergodic channel capacity (ECC) is obtained to measure maximum fading channel capacity. At last, interestingly unlike κ-µ, η-µ, α-µ in [9], [17], [18], these analytical results are validated with Monte Carlo simulations and it shows that for provided κ-µ/α-µ model, non-linear parameter has more important influence than multipath component in PDF and CDF, and when the ratio between the total power of the dominant components and the total power of the scattered waves is same, higher α can significantly improve channel capacity over composite fading channels.

The decentralized energy network is one of the promising solutions as a next-generation power grid. In this system, each house has a photovoltaic (PV) panel as a renewable energy source and a battery which is an essential component to balance between generation and demand. The common objective of the battery management on such systems is to minimize only the purchased energy from a power company, but battery degradation caused by charge/discharge cycles is also a serious problem. This paper proposes a State-of-Health (SOH) aware system-level battery management methodology for the decentralized energy network. The power distribution problem is often solved with mixed integer programming (MIP), and the proposed MIP formulation takes into account the SOH model. In order to minimize the purchased energy and reduce the battery degradation simultaneously, the optimization problem is divided into two stages: 1) the purchased energy minimization, and 2) the battery aging factor reducing, and the trade-off exploration between the purchased energy and the battery degradation is available. Experimental results show that the proposed method achieves the better trade-off and reduces the battery aging cost by 14% over the baseline method while keeping the purchased energy minimum.

Near real-time periodic tasks, which are popular in multimedia streaming applications, have deadline periods that are longer than the input intervals thanks to buffering. For such applications, the conventional frame-based schedulings cannot realize the optimal scheduling due to their shortsighted deadline assumptions. To realize globally energy-efficient executions of these applications, we propose a novel task scheduling algorithm, which takes advantage of the long deadline period. We confirm our approach can take advantage of the longer deadline period and reduce the average power consumption by up to 18%.

This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.

This paper studies the joint optimization of precoding, transmit power and data rate allocation for energy-efficient full-duplex (FD) cloud radio access networks (C-RANs). A new nonconvex problem is formulated, where the ratio of total sum rate to total power consumption is maximized, subject to the maximum transmit powers of remote radio heads and uplink users. An iterative algorithm based on successive convex programming is proposed with guaranteed convergence to the Karush-Kuhn-Tucker solutions of the formulated problem. Numerical examples confirm the effectiveness of the proposed algorithm and show that the FD C-RANs can achieve a large gain over half-duplex C-RANs in terms of energy efficiency at low self-interference power levels.

In this paper, we consider the clustering problem of independent general subspaces. That is, with given data points lay near or on the union of independent low-dimensional linear subspaces, we aim to recover the subspaces and assign the corresponding label to each data point. To settle this problem, we take advantages of both greedy strategy and energy minimization strategy to propose a simple yet effective algorithm based on the assumption that an m-branched (i.e., perfect m-ary) tree which is constructed by collecting m-nearest neighbor points in each node has a high probability of containing the near-exact subspace. Specifically, at first, subspace candidates are enumerated by multiple m-branched trees. Each tree starts with a data point and grows by collecting nearest neighbors in the breadth-first search order. Then, subspace proposals are further selected from the enumeration to initialize the energy minimization algorithm. Eventually, both the proposals and the labeling result are finalized by iterative re-estimation and labeling. Experiments with both synthetic and real-world data show that the proposed method can outperform state-of-the-art methods and is practical in real application.

This paper investigates the spectrum allocation problem. Under the current spectrum management mode, large amount of spectrum resource is wasted due to uncertainty of user's demand. To reduce the impact of uncertainty, a presale mechanism is designed based on spectrum pool. In this mechanism, the spectrum manager provides spectrum resource at a favorable price for presale aiming at sharing with user the risk caused by uncertainty of demand. Because of the hierarchical characteristic, we build a spectrum market Stackelberg game, in which the manager acts as leader and user as follower. Then proof of the uniqueness and optimality of Stackelberg Equilibrium is given. Simulation results show the presale mechanism can promote profits for both sides and reduce temporary scheduling.

In this letter, we investigate the price-based power allocation for non-orthogonal multiple access (NOMA) networks, where the base station (BS) can admit the users to transmit by pricing their power. Stackelberg game is utilized to model the pricing and power purchasing strategies between the BS and the users. Based on backward induction, the pricing problem of the BS is recast into the non-convex power allocation problem, which is equivalent to the rate allocation problem by variable replacement. Based on the equivalence problem, an optimal price-based power allocation algorithm is proposed to maximize the revenue of the BS. Simulation results show that the proposed algorithm is superior to the existing pricing algorithm in items of the revenue of BS and the number of admitted users.

Super resolution (SR) image reconstruction has attracted increasing attention these years and many SR image reconstruction algorithms have been proposed for restoring a high-resolution image from one or multiple low-resolution images. However, how to objectively evaluate the quality of SR reconstructed images remains an open problem. Although a great number of image quality metrics have been proposed, they are quite limited to evaluate the quality of SR reconstructed images. Inspired by this, this paper presents a blind quality index for SR reconstructed images using first- and second-order structural degradation. First, the SR reconstructed image is decomposed into multi-order derivative magnitude maps, which are effective for first- and second-order structural representation. Then, log-energy based features are extracted on these multi-order derivative magnitude maps in the frequency domain. Finally, support vector regression is used to learn the quality model for SR reconstructed images. The results of extensive experiments that were conducted on one public database demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed method is less dependent on the number of training images and has low computational cost.

For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.

Energy consumption is one of the important issues in communication networks, and it is expected that network devices such as network interface cards will be turned off to decrease the energy consumption. Moreover, fast failure recovery is an important issue in large-scale communication networks to minimize the impact of failure on data transmission. In order to realize both low energy consumption and fast failure recovery, a method called LE-MRC (Low-Energy based Multiple Routing Configurations) has been proposed. However, LE-MRC can degrade network robustness because some links ports are turned off for reducing the energy consumption. Nevertheless, network robustness is also important for maintaining the performance of data transmission and the network functionality. In this paper, for realizing both low energy consumption and fast failure recovery while maintaining network robustness, we propose Robustness and Low-Energy based Multiple Routing Configurations (RLE-MRC). In RLE-MRC, some links are categorized into unnecessary links, and those links are turned off to lower the energy consumption. In particular, the number of excluded links is determined based on the network robustness. As a result, the energy consumption can be reduced so as not to degrade the network robustness significantly. Simulations are conducted on some network topologies to evaluate the performance of RLE-MRC. We also use ns-3 to evaluate how the performance of data transmission and network robustness are changed by using RLE-MRC. Numerical examples show that the low energy consumption and the fast failure recovery can be achieved while maintaining network robustness by using RLE-MRC.