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This paper presents a CMOS-compatible gate-assisted photonic demodulator with contrast enhancement (GAPD-CE) techniques. To form an asymmetric field inside the substrate that will facilitate the transfer of photogenerated electrons, p-well and channel doping techniques are applied under the polysilicon guides. Modulation contrast (MC) values extracted from TCAD simulation show that the modified structure effectively collects electrons generated deep within the substrate. A prototype of the GAPD-CE is fabricated in a 0.18- $\mu \text{m}$ standard CMOS foundry process. An MC of 83% and 57% at 1 MHz and 30 MHz, respectively, are achieved under 850 nm illumination, demonstrating the potential of the proposed device for time-of-flight (ToF) sensing applications.

Nowadays, ocean networks gradually become increasingly important for communication among network entities such as maritime and sea-crossing users. However, ocean networks are highly dynamic because the communication links are composed of satellite and microwave links, which could be easily influenced by the environment such as local climate. Thus, network transmission in ocean networks faces great challenges, including low reliability and low efficiency. In this paper, we propose a smart ocean network architecture, where we use Software Defined Network (SDN) to perform unified management of the network, and Segment Routing (SR) to control data forwarding paths. In this way, we can control network flows and optimize network routing among diverse network entities in an ocean network. However, many Quality of Service (QoS) guaranteed applications in ocean networks, such as remote control, require lower delay. To guarantee the performance for such applications, we further propose QoS routing algorithms based on Fuzzy-Lagrange for the smart ocean networks architecture, where the optimization objective is to ensure service quality provided to users. According to experimental results, it is proved that, in comparison with the benchmark algorithms, the Fuzzy-Lagrange (FuzLag) algorithm proposed based on link fuzzification and Lagrangian Relaxation can improve the performance by 23% at most.

Foreground and background image segmentation plays an important role in the field of image processing. Convex shape priors are introduced in some image segmentation methods to estimate those implicit boundaries of image objects when these convex objects are partly obscured by unrelated surroundings that appeared in images. Classical convex shape priors based image segmentation methods use binary label functions to distinguish different image regions, which are sensitive to model parameter choices and often show poor performance in segmentation results. To solve these problems, in this paper fuzzy membership functions, which can be seen as relaxed forms of typical binary label functions, are chosen to represent different image parts, and a new variational model with respect to fuzzy membership functions is proposed in which a convex shape prior on fuzzy membership functions is designed to guarantee estimated image foregrounds having convex shapes in geometry, while to avoid the indicator function from being over-smoothed, a minimax concave regularization term is introduced to measure the smoothness of fuzzy membership functions. In order to construct an efficient algorithm to obtain desired solutions, the original model we proposed is transformed into an equivalent constrained minimization problem using the variable splitting technology, and then numerical solutions for the equivalent constrained model can be solved by an iteration algorithm which integrates the alternating direction method of multipliers, the fast Fourier transform, and the soft thresholding method. Numerical results demonstrate our proposed image segmentation method can keep estimated foreground regions convex in geometry. Compared with classical image segmentation methods, our proposed method shows more robust in model parameter choices and can obtain better convex shape in image segmentation results.

With the rapid increase in the penetration rate of renewable energy, the power quality of grid-connected wind farm is deteriorated seriously with power fluctuation and point of common coupling (PCC) voltage instability. The conventional solution is to use the energy storage device and the reactive power compensation apparatus to adjust separately, which complicates the system structure. Besides, an active power filter is needed additionally when harmonic pollution caused by local nonlinear load is serious. To solve these problems, this paper investigates the modular multilevel converter-based battery energy storage system (MMC-BESS), which can realize fluctuating power suppression, PCC voltage regulation, and harmonic mitigation simultaneously in grid-connected wind farm. Firstly, the topology and energy flow of MMC-BESS have been studied. Then, three-layer balance control of the state of charge (SOC) is proposed, which is essential to maintaining the normal operation of the device. Finally, the overall control of MMC-BESS applied in the grid-connected wind farm is given. Plenty of experimental results are obtained from a downscaled MMC-BESS prototype, which validate the effectiveness of the proposed control strategy.

We propose a modular multilevel converter (MMC) based three-phase four-wire (3P4W) split capacitor distribution static synchronous compensator (DSTATCOM), aiming at compensating unbalanced and reactive load currents. Due to the zero-sequence current compensation, the circulating current characteristics of 3P4W MMC-DSTATCOM are different from conventional MMCs. Moreover, the distinct working principle of MMC would affect the features of split capacitor voltage. The decoupled positive-, negative- and zero-sequence second-order and DC components of the circulating current are deduced explicitly. Two proportional-integral controllers with dual dq transformation are employed to suppress the positive- and negative-sequence components of second-order circulating current, while quasi proportional-resonance controller is designed to eliminate the zero-sequence component. Besides, the phenomenon of the unbalanced split capacitor voltages is revealed, and fast-tracking balancing method by controlling zero-sequence current flowing through the split capacitors is provided. Digital simulation results verify the accuracy of the analysis and the feasibility of the suppression methods.

Nuclear norm and l1 norm are the common regularization in salient object detection. However, existing literatures show that these terms either 1) are very slow for large scale problems due to singular value decomposition (SVD) on full matrix in every iteration, or 2) over-penalize the large singular values. In this paper, we propose to use respectively the non-convex weighted Schatten-p quasi-norm and lp-norm (0

Insomnia is a prevalent sleep disorder that causes serious harm to individuals and society. It is closely linked to not only personal factors but also social, economic and other factors. This study explores the influencing factors and spatial differentiation of insomnia from the perspective of social media. This paper chose China's largest social media platform, Sina Weibo, as its data source. Then, based on the collected relevant data of 288 Chinese cities from 2013 to 2017, it explored the impact of economic, social, and environmental factors and an educated population on insomnia. Additionally, the importance and interaction of each influencing factor were analyzed. According to the results, the gross domestic product (GDP), proportion of households connected to the Internet and number of students in regular institutions of higher education are the major factors that influence insomnia, and their influences show obvious spatial nonstationarity. Rapid GDP growth has increased the probability of insomnia, and the positive correlation between the proportion of households connected to the internet and insomnia has strengthened annually. Although the impact of insomnia on college students decreased in some regions, the overall impact was still increasing annually, and spatial nonstationarity was obvious. Properly controlling GDP growth and unnecessary time spent online and guiding people to develop healthy Internet surfing habits and lifestyles will help improve their sleep quality. Our research results will help relevant professionals better understand the distribution of regional insomnia and provide a reference for related departments to formulate regional insomnia prevention and treatment policies.

The submodule (SM) capacitor voltage ripple and the root mean square (RMS) of the arm current are related to the costs and losses of the modular multilevel converter (MMC). The optimization issues have received great attention. The second-order circulating current control and third-order harmonic voltage injection are two conventional optimization methods, but the existing research neglects their coupling effects. In this paper, these two methods are comprehensively investigated and coordinately combined. First, the various performances of MMC under multiple injections are characterized. Then, a multi-objective optimization model considering the output capability of arm voltage and the energy balance of capacitors, is proposed to reduce the SM capacitor voltage ripple and the RMS of the arm current. The amplitudes and phases of the injected second- and third-order harmonic voltages are designed coordinately by genetic algorithm. The simulation and experimental results have validated the effectiveness and feasibility of the proposed method.

The motion trajectory of sea cucumbers reflects the behavior of sea cucumbers, and the behavior of sea cucumbers reflects the status of the feeding and individual health, which provides the important information for the culture, status detection and early disease warning. Different from the traditional manual observation and sensor-based automatic detection methods, this paper proposes a detection, location and analysis approach of behavior trajectory based on Faster R-CNN for sea cucumbers under the deep learning framework. The designed detection system consists of a RGB camera to collect the sea cucumbers' images and a corresponding sea cucumber identification software. The experimental results show that the proposed approach can accurately detect and locate sea cucumbers. According to the experimental results, the following conclusions are drawn: (1) Sea cucumbers have an adaptation time for the new environment. When sea cucumbers enter a new environment, the adaptation time is about 30 minutes. Sea cucumbers hardly move within 30 minutes and begin to move after about 30 minutes. (2) Sea cucumbers have the negative phototaxis and prefers to move in the shadows. (3) Sea cucumbers have a tendency to the edge. They like to move along the edge of the aquarium. When the sea cucumber is in the middle of the aquarium, the sea cucumber will look for the edge of the aquarium. (4) Sea cucumbers have unidirectional topotaxis. They move along the same direction with the initial motion direction. The proposed approach will be extended to the detection and behavioral analysis of the other marine organisms in the marine ranching.

Mobile edge computing (MEC) is regarded as a key technology to reduce the network pressure from the computing-intensive and latency-sensitive applications in future wireless networks. Non-orthogonal multiple access (NOMA) can achieve high spectral efficiency by allowing multiple users to reuse the same resources. In this paper, we consider a novel NOMA-based MEC network to improve the energy efficiency during task offloading process. With multiple access points (APs) being deployed, we formulate a joint optimization problem which involves user association, computing resource allocation, and transmit power control, with the objective of minimizing the total energy consumption of all users in such a network. Specifically, the problem can be modeled as a many-to-one matching game with externality due to the co-channel interference and resource competition among users. We then propose a matching-coalition approach coupled with computing resource allocation and power control to find the optimal solution, and also provide the analyses on related properties including stability, convergence, and complexity. Simulation results show that the proposed approach can efficiently reduce the total energy consumption in comparison to other simplified approaches.

Increasing demand for higher data-rate wireless connectivity with lower latency is fueling the explorations of millimeter-wave (mmWave) spectrum and massive MIMO communications. Both technologies are recognized as the key enablers of 5G and beyond 5G (B5G) networks. Hybrid beamforming is one of the most promising energy and cost-effective approaches to realize mmWave massive MIMO communications with lower complexity and smaller training overhead. With the motivation of giving more insights and in-deep technical recommendations to B5G network designers regarding hybrid beamforming, we present a hybrid beamforming taxonomy in terms of channel state information (CSI) availability, frequency bandwidth, architecture complexity, analog beamformer components, number of users, connectivity to RF chains, and the digital and analog beamforming design. Furthermore, we provide a comprehensive survey on the state-of-the-art use-cases for each classification followed by identification of the future challenges and open research issues.

Inefficient supply-demand matching makes the fleet management a research hotpot in ride-sharing platforms. With the booming of mobile network services, it is promising to abate the supply-demand gap with effective vehicle dispatching. In this article, we propose a QRewriter - Dueling Deep Q-Network (QRewriter-DDQN) algorithm, to dispatch multiple available vehicles in ahead to the locations with high demand to serve more orders. The QRewriter-DDQN algorithm factorizes into a Dueling Deep Q-Network (DDQN) module and a QRewriter module, which are parameterized by neural networks and Q-table with Reinforcement Learning (RL) methods, respectively. Particularly, DDQN module utilizes the Kullback-Leibler (KL) distribution distance between supply (available vehicles) and demand (orders) as excitation to capture the complex dynamic variations of supply-demand. Afterwards, the QRewriter module learns to improve the DDQN dispatching policy with the streamlined and effective Q-table in RL. Importantly, the higher performance improvement space of the DDQN dispatching policy can be obtained by aggregating QRewriter state into low-dimension meta state. A simulator is designed to train and test the performance of QRewriter-DDQN, the experiment results show the significant improvement of QRewriter-DDQN in terms of order response rate.

A novel compressive optical cryptosystem based on single-pixel imaging is proposed. By using random sparse sampling in the encryption process, the plaintext is encrypted and compressed into a noise-like intensity sequence. As a result, the ciphertext is much less than the plaintext in volume. For decryption, a convex optimization algorithm is employed to reconstruct the plaintext with the help of the correct key based on the compressive sensing theory. Computer simulations and optical experiments are conducted and a series of results verify the validity and feasibility of the proposed method.

Modal estimation of aberration can quantify the amount of aberration, which plays an important role in optical system optimization, aberration correction, and so on. Here, we propose a simple but effective method to find the modal wavefront of aberration. Once the aberrated wavefront has been detected, it will be automatically analyzed with a method based on one-dimensional standard polynomial fitting. According to the analysis, the modal wavefront and the property of aberration can be obtained for further studies. Here, for example, we show that it can be used to calculate the optimal correction wavefront. Experiments were implemented to testify the correctness and effectiveness of this method. This method provides users another option for aberration interpretation.

We consider practical hardware implementation of polar decoders. To reduce latency due to the serial nature of successive cancellation, existing optimizations improve parallelism with two approaches, i.e., multi-bit decision or reduced path splitting. In this paper, we combine the two procedures into one with an error-pattern-based architecture. It simultaneously generates a set of candidate paths for multiple bits with pre-stored patterns. For rate-1 (R1) or single parity-check nodes, we prove that a small number of deterministic patterns are required to guarantee performance preservation. For general nodes, low-weight error patterns are indexed by syndrome in a look-up table and retrieved in $O(1)$ time. The proposed flip-syndrome-list decoder fully parallelizes all constituent code blocks without sacrificing performance, and thus is suitable for ultra-low-latency applications. Meanwhile, two code construction optimizations are presented to further reduce complexity and improve performance.

The structure of network topology in heterogeneous sensor networks changes fast. As a result, the coverage and control ability of sensor nodes will be undermined. In order that we can address this problem, we propose an event-driven mechanism coverage algorithm based on Sensing-cloud-computing in heterogeneous sensor networks (EMC-SC). First, the network coverage model is utilized to compute the boundary relationship between the dual square and the sensor nodes coverage area. Then, the Poisson distribution model is employed to analyze the coverage performance of the randomly deployed sensor nodes and further compute the probability of the coverage which is effective over the monitoring area. Next, the node state mechanism scheduling algorithm is determined according to the amount of exchanged information between the neighbor nodes and the sensor nodes as well as the remaining sensor nodes energy. Finally, the results of the simulation show that, if it is compared with other algorithms which exist, the EMC-SC algorithm which is proposed could improve the network lifetime and the network coverage rate by 12.73% and 16.22%, respectively. Therefore, the effectiveness of the proposed algorithm and the validity of the proposed algorithm are verified.

By adding Buck-type AC/AC converter to conventional power capacitor, dynamic capacitor (D-CAP) can be formed to compensate variable rather than fixed reactive power. However, some nonlinear distortion factors, namely grid harmonic voltage, voltage drop of switches and dead-time, would cause harmonics in its compensation current, meanwhile, its capacitance may interact with the grid inductance to cause series resonance. Both nonlinear distortion factors and series resonance would distort the waveform of its compensation current and make the grid current failed to meet the power quality standard IEEE Std.519. This paper focuses on the output current distortion of Buck-type D-CAP in reactive power compensation. For harmonic distortion, through establishing the equivalent circuit of Buck-type D-CAP, the generation of low-order harmonic current is investigated under three nonlinear factors. A current reshaping method to simultaneously diminish the harmonics caused by the three nonlinear distortion factors is thus adopted, which is a feedback control of output current harmonics. Then for series resonance, based on the block diagram of Buck-type D-CAP within the grid, transfer function from grid voltage to the output current is deduced and the corresponding Bode diagram is depicted. An active damping method is therefore suggested to shrink the low-order series resonance peak, by detecting power capacitor's voltage for feedback control. Finally, a combined control, reactive current reshaping with series resonance damping is proposed in this paper for three-phase Buck-type D-CAP. A wide variety of experimental results from a 33kVar/220V laboratory prototype are provided to demonstrate the validity of the combined control.

When communication infrastructure is damaged due to natural disasters, employing unmanned aerial vehicle (UAV) as a flying base station (BS) and device-to-device (D2D) communication paradigms are among the essential strategies for seamless and reliable services. However, the communication reliability of the flying BS and the energy efficiency (EE) of the D2D communications may be degraded due to the shared spectrum coexistence of the UAV and D2D pairs. In this paper, we consider the uplink transmission to an UAV which is underlaid with D2D communication pairs randomly distributed on multiple frequency bands. For such a scenario, we derive tractable expressions for the successful transmission probability, the average sum-rate, and the EE of the network based on stochastic geometry principles, that perfectly match the simulation results. We also address the effects of D2D and UAV-connected users' densities on the EE and sum-rate as performance metrics in the uplink scenario. Finally, some critical insights are provided based on the trade-off between different network parameters, and accordingly recommendations are given for maximizing the EE of such networks in post-disaster situations.

The conventional data-based routing protocols are usually vulnerable to a large number of energy voids or hotspots in Wireless Sensor Networks (WSNs). In order to address this problem, we propose Mobile Intelligent Fog Computing: An Energy-efficient Cross-layer-sensing Clustering Method (ECCM). The first, according to the cross-layer projection principle, the proposed algorithm employs the sensing-event-driven mechanism to project the fog nodes onto the sensing layer, and constructs a powerful virtual control node. Then the control procedure of the cluster-based routing protocol in sensor networks is uploaded to the fog layer and the fog computation is employed to achieve the distributed clustering of the event-field nodes. The second, the optimized data aggregation routing is constructed, which centers the projectile fog nodes. The data in the bottom-layer routing of the sensor network is thus replaced, and the network load is balanced and reduced. The third, in the optimization of the routing protocol, we introduce the Particles Swarm Optimization, (PSO) algorithm and elect a group of optimal nodes as the cluster heads, without the cost of any competition overhead, the energy overhead of the network can be effectively reduced and balanced, which curbs the rapid exhaustion of the node energy and prolongs the network lifetime. Finally, it is shown by the simulation results that the construction and the maintenance of the routing structure are small, which could optimize the data aggregation efficiency and improve the network performance.

In this paper, the exponential leader-following consensus problem is investigated for networked multi-agent systems. In order to further reduce the usage of the communication resources, event-triggered delayed impulsive control strategy is put forward, which successfully combines the delayed impulsive control and event-triggered mechanism, and the delayed impulsive controller is implemented only at event-triggered instants. By constructing the novel Lyapunov based event-triggered strategy and impulsive control theory, some easy-to-check conditions are derived such that the error system is exponentially stable. Furthermore, it is shown that the Zeno behavior can be eliminated from the event-triggering rules. Numerical simulation is proposed to illustrate the effectiveness of the theoretical results.

Millimeter wave (mm-Wave) communications are emerging to meet the increasing demand for high transmission data rate in high user density areas. Meanwhile, the mm-Wave base station (BS) needs to employ a large number of antenna elements to increase the gain as well as serve a huge number of users. However, a vast number of antenna elements causes dimensionality problem in channel correlation matrix (rotation matrix). Therefore, we propose a novel codebook construction design based on CUR-decomposition technique to reduce the dimensionality problem. In this paper, the original correlation matrix is decomposed to the product of three low dimension matrices (C, U, and R). The new rotated codebook is then constructed by the new rotation matrix. Moreover, we evaluate the new decomposition matrix with the original matrix in terms of compression ratio and mismatch error. We also provide the achievable sum rate capacities for singular value decomposition, zero forcing, and a matched filter techniques to compare with the proposed method. Furthermore, the system capacity enhancement related to the number of antenna elements and the required feedback bits are analyzed. Simulation results show that the proposed method achieves much better system performance since the dimensionality problem is solved. The proposed method can be applied in the fifth generation massive antenna multi-user system with over a hundred antenna elements.

Recently, broadband maritime communication has attracted much attention due to the rapid development of blue economy. In addition to the conventional MF/HF/VHF bands, there has been increasing interests in the utilization of higher frequency bands to provide broadband data service to the sea area. To design efficient maritime communication systems, the first and a fundamental requirement is to develop a framework to understand the wireless channels. In an integrated air-ground-sea communications network, there are two major type of channels to be investigated, namely the air-to-sea channel (e.g., for communication links from the aircraft-based base stations or relays) and the near-sea-surface channel (for land-to-ship/ship-to-land or ship-to-ship communications). Due to the unique features of the maritime propagation environment such as sparse scattering, sea wave movement, and the ducting effect over the sea surface, the modeling of these maritime channel links differs from conventional terrestrial wireless channels in many aspects and, consequently, will result in significant impact on the transceiver design. In this survey, we highlight the most notable differences from the modeling perspective as well as the channel characteristics for the air-to-sea and near-sea-surface channel links, with more focus on the latter. After a thorough review of existing modeling approaches and measurement campaigns, we conclude that the sparse and the location-dependent properties constitute the most important and distinctive characteristics of the maritime wireless channels. As such, we further remark on the challenges and research topics for future development of maritime communications.

Color transfer is to generate synthetic images by changing the color of target images with new colors obtained from given source images, while the geometrical structure of the synthetic images remains the same. Classical color transfer models use a total variation (TV) regularizer to preserve the details and suppress the noise of the synthetic images. These models can sometimes cause staircase effect and geometrical structure details over-smoothed. To overcome these problems, we propose a new color transfer model in which an adaptive second-order total generalized variation (TGV) regularizer is designed. Here, the adaptive second-order TGV regularizer is a weighted second-order TGV regularizer. The weight is computed by an adaptive edge indicator function. In addition, an efficient algorithm is developed to program our new model. The algorithm is based on a weighted primal-dual method. Experimental results and comparisons demonstrate that our new color transfer model can generate better results than classical TV regularizer-based models in the aspects of the inhibition of staircase effect and the preservation of image details.

In the salient object detection, the given image can be decomposed into background regions (low-rank part) and salient regions (sparse part). In this paper, we present a novel sparse gradient-based structured matrix decomposition model for salient object detection. We use the l1 norm of logistic function on the singular values to approximate the rank function, which avoid over-penalized problem of the nuclear norm. And a group sparsity induced norm regularization is imposed on the salient part to explore the relationship among superpixels. In order to widen the gap between salient regions and background regions in feature space, we suggest a sparse gradient regularization to replace the conventional Laplacian regularization. Finally, the model is solved through an augmented Lagrange multipliers method, and highlevel priors are embedded into our model to promote the performance. Experiments indicate that the proposed method performs better in terms of various evaluation metrics than the state-of-the-art methods.

Real-time vehicle classification is an important issue in intelligent transport systems. In this paper, we propose a novel model to classify five distinct groups of vehicle images from actual life based on AdaBoost algorithm and deep convolutional neural networks (CNNs). The experimental results demonstrate that the proposed model attains the highest classification accuracy of 99.50% on the test data set, while it takes only 28 ms to identify a vehicle image. This performance significantly outperforms the traditional algorithms, such as SIFT-SVM, HOG-SVM, and SURF-SVM. Moreover, the proposed deep CNN-based feature extractor has less parameters, thereby occupies much smaller storage resources as compared with the state-of-the-art CNN models. The high prediction accuracy and low storage cost confirm the effectiveness of our proposed model for vehicle classification in real time.

With the popularity of social network-based services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. Device-to-device (D2D) communication, as a promising solution to overcome wireless spectrum crisis, can enable fast content delivery based on user activities in social networks. In this paper, we address the content delivery problem related to optimization of peer discovery and resource allocation by combining both the social and physical layer information in D2D underlay networks. The social relationship, which is modeled as the probability of selecting similar contents and estimated by using the Bayesian nonparametric models, is used as a weight to characterize the impact of social features on D2D pair formation and content sharing. Next, we propose a 3-D iterative matching algorithm to maximize the sum rate of D2D pairs weighted by the intensity of social relationships while guaranteeing the quality of service requirements of both cellular and D2D links simultaneously. Moreover, we prove that the proposed algorithm converges to a stable matching and is weak Pareto optimal, and also provide the theoretical complexity. Simulation results show that the algorithm is able to achieve more than 90% of the optimum performance with a computation complexity 1000 times lower than the exhaustive matching algorithm. It is also demonstrated that the satisfaction performance of D2D receivers can be increased significantly by incorporating social relationships into the resource allocation design.

Energy Internet, as a major trend in power system, can provide an open framework for integrating equipment of energy generation, transmission, storage, consumption, and so on, so that global energy can be managed and controlled efficiently by information and communication technologies. In this paper, we focus on the coordinated management of renewable and traditional energy, which is a typical issue on energy connections. We consider a conventional power system consisting of the utility company, the energy storage company, the microgrid, and electricity users. First, we formulate the energy management problem as a three-stage Stackelberg game, and every player in the electricity market aims to maximize its individual payoff while guaranteeing the system reliability and satisfying users' electricity demands. We employ the backward induction method to solve the three-stage non-cooperative game problem, and give the closed-form expressions of the optimal strategies for each stage. Next, we study the big data-based power generation forecasting techniques, and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Furthermore, we prove the properties of the proposed energy management algorithm including the existence and uniqueness of Nash equilibrium and Stackelberg equilibrium. Simulation results show that accurate prediction results of wind power is conducive to better energy management.

The explosive growth of mobile date traffic and ubiquitous mobile services cause an high energy consumption in mobile devices with limited energy supplies, which has become a bottleneck for deploying device-to-device (D2D) communication. Simultaneous wireless information and power transfer (SWIPT), which enables mobile devices to harvest energy from the radio frequency signals, has emerged as a promising solution to improve the energy efficiency (EE) performance. In this paper, we address joint power control and spectrum resource allocation problem in SWIPT-based energy-harvesting D2D underlay networks. First, we formulate joint optimization problem as a 2-D matching between D2D pairs and cellular user equipments (CUEs), and propose a preference establishment algorithm based on Dinkelbach method and Lagrange dual decomposition. Second, we propose an energy-efficient stable matching algorithm by exploring the Gale-Shapley algorithm, which is able to maximize the EE performance of D2D pairs and the amount of energy harvested by CUEs simultaneously. Third, we provide in-depth theoretical analysis of the proposed matching algorithm in terms of stability, optimality, and complexity. Simulation results demonstrate that the proposed algorithm can bring significant EE performance gains compared with some heuristic algorithms.

Energy efficiency (EE) is critical to fully achieve the huge potentials of device-to-device (D2D) communications with limited battery capacity. In this paper, we consider the two-stage EE optimization problem, which consists of a joint spectrum and power allocation problem in the first stage, and a context-aware D2D peer selection problem in the second stage. We provide a general tractable framework for solving the combinatorial problem, which is NP-hard due to the binary and continuous optimization variables. In each stage, user equipments (UEs) from two finite and disjoint sets are matched in a two-sided stable way based on the mutual preferences. First, the preferences of UEs are defined as the maximum achievable EE. An iterative power allocation algorithm is proposed to optimize EE under a specific match, which is developed by exploiting nonlinear fractional programming and Lagrange dual decomposition. Second, we propose an iterative matching algorithm, which first produces a stable match based on the fixed preferences, and then dynamically updates the preferences according to the latest matching results in each iteration. Finally, the properties of the proposed algorithm, including stability, optimality, complexity, and scalability, are analyzed in detail. Numerical results validate the efficiency and superiority of the proposed algorithm under various simulation scenarios.

Two-dimensional in-phase coherently coupled vertical cavity surface emitting laser (VCSEL) array with high beam quality was fabricated by proton implantation. Indium-tin-oxide (ITO) current spreading layer was employed to improve the uniformity of the injection current. This makes a coherent array to operate in in-phase mode even in a relatively larger scale. The appropriate thickness of ITO current spreading layer was obtained by experiment (80 nm). The procedure of proton implantation defined array is considerably simple and low cost. A 3 × 3 in-phase coherently coupled array with 2.4° (1.28 × D.L.) of lobe beamwidth was achieved. It sufficiently demonstrates the advantages of the high beam quality of the in-phase coherently coupled arrays. The output power of the array was up to 4.5 mw under pulse-wave condition.