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Despite much research on the interdisciplinarity of the publications across subject categories, few studies analyze the interdisciplinarity of the technological knowledge flow among research entities (i.e., patent assignees, inventors, and countries). In this article, we propose a framework to identify the roles of the research entities by measuring the interdisciplinarity of the technological knowledge flow. Research entities are positioned on the two-dimensional maps based on the integration index, diffusion index, and specialization index, which are used to measure the degree of the interdisciplinarity of different research entities in knowledge flow. In order to explore the role of the research entities in knowledge flow, we put forward four kinds of research entities: diverse knowledge provider, active mediator of diverse knowledge flow, diverse knowledge consumer, and negative mediator of diverse knowledge flow. The case study related to synthetic biology indicates that this method is valid and could explore effectively, the roles of the research entities to further guide the creation of new inventions by converging prominent technologies beyond technology field boundaries in facilitating active technology flow and support technological competitive evaluation and policy decision-making. [ABSTRACT FROM AUTHOR]

Technology convergence can trigger technological innovation and change. Therefore, it is required to develop an approach to predict the convergence between technology fields that did not exist in the past. It will allow a frim to preoccupy a completely new competitive advantage that is different from that of its competitors. The timely anticipation of converging technology fields allows the innovating firms to recognize the changing business developments associated with the technology convergence. A variety of researchers have presented supervised learning-based approaches to predict potential technology fields where technology convergence is taking place using patents. They have developed machine learning models which capture the associations between the past and future connections between technology classes. Although their contributions are absolutely significant, they have a limitation in that they do not consider in depth the technological properties that are outputs of technological activities performed in each technology field. To ensure that the predicted future connections between technology fields are reasonable, technological properties that can specifically imply technology convergence should be clearly reflected in the process of the supervised learning. Motivated to remedy this problem, this study proposes a supervised learning-based approach to anticipating potential technology convergence by using the link prediction results, the technological influence relationships, and the technological relevance between technology classes. Using these as input features, several classification models that predict new technology convergence are trained and a voting classifier is developed to ensemble all the models. This study is expected to contribute to identifying new technology opportunities that can be realized through technology convergence. Furthermore, this study will assist firms to reflect the identified opportunities on their technology roadmap and make business decisions to penetrate the relevant market in a timely manner.

In this article, we introduce a potential game-based approach to implement collaborative user scheduling and power allocation in the uplink multibeam satellite-based Internet of things (S-IoT) networks. First of all, a framework of the multibeam uplink S-IoT system is proposed, where full frequency reuse and co-channel interference among different spot beams are considered. To provide broadband S-IoT service effectively, we formulate an initial optimization problem of maximizing uplink sum transmission capacity and then transform it into an interference avoidance problem to address the mathematical intractability. Specifically, a game-theoretic model is implemented to solve the transformed optimization problem, which is proved to be a potential game and existence of Nash equilibriums (NEs). Moreover, an iterative algorithm with low computational complexity, motivated by the finite improvement property, is designed to implement collaborative user scheduling and power allocation to NE point. Finally, the simulation results prove the convergence and effectiveness of the proposed potential game-based approach. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV)-based 5G networks deliver specific services to a end-users through the creation of Service Function Chains (SFCs), which are composed of a number of Virtualized Network Functions (VNFs) interconnected via a set of virtual links (vLinks). VNFs consume computational resources of the network's servers, while vLinks utilize the communicational resources of the network. The efficient utilization of network resources remains a challenge in NFV-based networks. To this end, in this paper, we propose an analytical framework for the Admission Ratio (AR) calculation in NFV-based converged optical-wireless 5G networks. The proposed methodology employs a network slicing architecture in which different network slices form end-to-end logically isolated networks and each slice delivers a specific service type to the users through its SFC(s). In the proposed analysis, we not only take into account the occupancy distribution in both the network's computational and communicational domains (servers and fiber links), but we also consider the SFC establishment AR by taking into account different sub-service-classes belonging to different slices. The accuracy of the model is evaluated through the comparison of analytical and simulation results and was found satisfactory. Furthermore, the proposed model is employed for the determination of the optimal (minimum) capacity for all SFCs elements (VNFs and vLinks), in a way that users belonging to a specific slice experience a predefined value of AR as minimum. Additionally, our calculations deploy recursive formulas, which have a low computational complexity, as opposed to time-consuming simulations, without requiring the application of complex optimization algorithms. [ABSTRACT FROM AUTHOR]

Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability. [ABSTRACT FROM AUTHOR]

Despite the promising results, tensor robust principal component analysis (TRPCA), which aims to recover underlying low-rank structure of clean tensor data corrupted with noise/outliers by shrinking all singular values equally, cannot well preserve the salient content of image. The major reason is that, in real applications, there is a salient difference information between all singular values of a tensor image, and the larger singular values are generally associated with some salient parts in the image. Thus, the singular values should be treated differently. Inspired by this observation, we investigate whether there is a better alternative solution when using tensor rank minimization. In this paper, we develop an enhanced TRPCA (ETRPCA) which explicitly considers the salient difference information between singular values of tensor data by the weighted tensor Schatten p-norm minimization, and then propose an efficient algorithm, which has a good convergence, to solve ETRPCA. Extensive experimental results reveal that the proposed method ETRPCA is superior to several state-of-the-art variant RPCA methods in terms of performance. [ABSTRACT FROM AUTHOR]

Background: More and more multinational corporations (MNCs) based in East Asia are adopting international social media to communicate messages on corporate social responsibility (CSR), but little research has investigated their content qualitatively. Literature review: I reviewed the literature related to the convergence and divergence of corporate behaviors under the framework of the institutional theory. Comparative studies on web-based CSR communication (CSRC) were also addressed, and CSR development in China and Korea was described as the research context to develop hypotheses and research questions. Research questions: 1. How do Chinese and Korean CSRCs resemble each other in international social media? 2. What are the differences between Chinese and Korean CSRCs in international social media? Research methodology: I selected six Chinese companies and six Korean firms from the 2019 Forbes 2000 world's-largest-corporations ranking, and analyzed the content of their English-language Facebook accounts using “Leximancer,” a powerful textual analysis software package. Results: Results indicated that the CSR messages of Chinese and Korean firms resembled each other at a macrolevel but differed at a microlevel. In addition, the CSR messages of Chinese companies were more likely to reflect national strategic agendas, while Korean firms placed greater emphasis on short-term events and legal compliance. Conclusions: Chinese and Korean corporations have adopted an approach of “global topics, native framing” in their CSRCs to achieve a balance between internal and external legitimacy. [ABSTRACT FROM AUTHOR]

With the increase in the number of optimization objectives, balancing the convergence and diversity in evolutionary multiobjective optimization becomes more intractable. So far, a variety of evolutionary algorithms have been proposed to solve many-objective optimization problems (MaOPs) with more than three objectives. Most of the existing algorithms, however, find difficulties in simultaneously counterpoising convergence and diversity during the whole evolutionary process. To address the issue, this paper proposes to solve MaOPs via multistage evolutionary search. To be specific, a two-stage evolutionary algorithm is developed, where the convergence and diversity are highlighted during different search stages to avoid the interferences between them. The first stage pushes multiple subpopulations with different weight vectors to converge to different areas of the Pareto front. After that, the nondominated solutions coming from each subpopulation are selected for generating a new population for the second stage. Moreover, a new environmental selection strategy is designed for the second stage to balance the convergence and diversity close to the Pareto front. This selection strategy evenly divides each objective dimension into a number of intervals, and then one solution having the best convergence in each interval will be retained. To assess the performance of the proposed algorithm, 48 benchmark functions with 7, 10, and 15 objectives are used to make comparisons with five representative many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

To meet the ever increasing demand for better service quality and availability, the next generation digital TV (DTV) broadcast, a.k.a., the ATSC 3.0, is developed with new capabilities to deliver 4K ultra-high-definition (4K-UHD) services to fixed receivers, robust HD-quality services to mobile receivers, as well as non-broadcast services (e.g., broadband), including Internet of Things (IoT), Connected Vehicles, etc. To realize these capabilities, it becomes necessary to evolve the existing single-transmitter high-power-high-tower (HPHT) network to multi-transmitter low-power-low-tower single-frequency-networks (SFN), which requires installing new transmitters. Each new transmitter needs a feederlink to receive the service data from the broadcast gateway (BG) for transmission. This feederlink contributes to a significant portion of installation and operational costs of the new transmitters. This paper proposes a wireless in-band distribution link (WIDL) technology, where the distribution data is delivered to the newly deployed transmitters from the existing DTV towers wirelessly sharing the same TV band with the broadcast services. The distribution signal is multiplexed with service signal in the same ATSC 3.0 waveform. Layered division multiplexing (LDM) technology is proposed to achieve more efficient transmission of the distribution signal. The WIDL offers better performance and more robust operation than the on-channel repeater (OCR) technologies. More importantly, it offers the possibility of delivering backhaul data for future applications over the DTV infrastructure, such as IoT and connected vehicles. Therefore, the proposed WIDL is one enabling technology to achieve convergence of broadcast services with broadband and other wireless services on the DTV spectrum. [ABSTRACT FROM AUTHOR]

A vibrant development is currently taking place in entrepreneurial finance due to the field's digital evolution over recent years. This article aims to assess the interplay between four of the key phenomena that has fuelled this development, namely crowdfunding, blockchain technologies, cryptocurrencies, and initial coin offerings (ICOs). By making use of social media analytics, public discussions on social media concerning crowdfunding, blockchain technologies, cryptocurrencies, and ICOs have been systematically tracked in social media over three time periods between the May 6, 2017, and October 2, 2018. In doing so, a total of 197 770 captured posts across social media platforms have been collected and analyzed. The results illustrate that discussions on blockchain technologies dominated the interplay in the first analyzed time period, that discussions on cryptocurrencies and ICOs dominated the interplay in the second analyzed time period, while discussions concerning blockchain technologies, cryptocurrencies, and ICOs highly converge in the third time period. By illustrating this shift over the analyzed time periods and by offering a systematic exploration of key characteristics of the interplay at hand, this article adds to previous literature on entrepreneurial finance by providing an empirical contribution which details the coevolution of these phenomena in recent years. [ABSTRACT FROM AUTHOR]

The wide applications of virtualization and service-oriented principles in various emerging networking technologies introduce a trend of network cloudification that enables network systems to be realized based on cloud technologies and allows network services to be provisioned following the cloud service model. Network cloudification together with the critical role of networking in the latest cloud/edge computing technologies leads to the convergence of networking and cloud/edge computing, which calls for a holistic vision across the fields of networking and computing that may shape relevant technology developments. in this article, we attempt to sketch a big picture to reflect the current status of on-going research toward network-cloud/edge convergence. We first describe the notion of such convergence and present an architectural framework for converged network-cloud/edge systems. Then, we survey the state of the art of enabling technologies for network-cloud/edge convergence by reviewing recent progress in relevant standardization and technology developments in representative research projects. We also discuss challenges that must be fully addressed for realizing the convergence of networking and cloud/ edge computing and identify some opportunities for future research in this exciting interdisciplinary field. [ABSTRACT FROM AUTHOR]

Over the past few decades, the impact of customer integration on radical new product (RNP) innovation has been extensively investigated. To date, this important topic presents inconsistent empirical findings that must be converged. In this paper, our systematic literature review addresses these inconsistencies by taking a consolidated view of customer integration's effects on the development of RNPs. This extensive review of 153 empirical papers has two main objectives. First, we provide the primary reasons for inconsistent findings by scrutinizing the operationalizations of customer types (i.e., current customers, potential customers, ordinary users, or users with domain-specific skills) and RNPs (i.e., technological innovativeness, or both technological and market innovativeness) used in the studies, as well as the different perspectives on customer integration [i.e., customer-based idea evaluation, participation in direct and indirect idea generation, research and development (R&D) partnerships with customers, having a customer orientation, and disseminating customer knowledge via R&D-marketing collaborations]. Second, we present a synthesized view on factors in the sphere of the innovating company and the customer that influence customer integration's success along three phases of the radical innovation development process (i.e., discovery, incubation, and acceleration). Finally, we present avenues for future research and discuss managerial implications of our synthesized view. [ABSTRACT FROM AUTHOR]

Lifelong topic model (LTM), an emerging paradigm for never-ending topic learning, aims to yield higher-quality topics as time passes through knowledge accumulated from the past yet learned for the future. In this paper, we propose a novel lifelong topic model based on non-negative matrix factorization (NMF), called Affinity Regularized NMF for LTM (NMF-LTM), which to our best knowledge is distinctive from the popular LDA-based LTMs. NMF-LTM achieves lifelong learning by introducing word-word graph Laplacian as semantic affinity regularization. Other priors such as sparsity, diversity, and between-class affinity are incorporated as well for better performance, and a theoretical guarantee is provided for the algorithmic convergence to a local minimum. Extensive experiments on various public corpora demonstrate the effectiveness of NMF-LTM, particularly its human-like behaviors in two carefully designed learning tasks and the ability in topic modeling of big data. A further exploration of semantic relatedness in knowledge graphs and a case study on a large-scale real-world corpus exhibit the strength of NMF-LTM in discovering high-quality topics in an efficient and robust way. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of broadcast and unicast convergence by proposing a PHY/MAC (Physical Layer/Medium Access Control) architecture for 5G New Radio (NR). The solution is based on Power domain Non Orthogonal Multiple Access (P-NOMA). The main PHY/MAC configuration parameters have been analyzed theoretically and their impact on the service configurations is presented in this manuscript. The system concept has been translated into a prototype model and different evaluation tests are presented. First, simulations show that the PHY layer performs better than Time Division Multiplexing/Frequency Division Multiplexing (TDM/FDM) choices of current broadband access systems. Second, performance tests using a network simulation tool are described. The results for capacity, latency and reliability demonstrate that the proposed solution offers an excellent broadcast/unicast convergence choice with significant gain values with respect to legacy PHY/MAC alternatives. [ABSTRACT FROM AUTHOR]

In this paper, the ATSC 3.0 broadcast Radio Access Technology (RAT) is aligned with 3GPP 5G NR RAT in the context of 5G convergence starting in Release 16. The 5G system architecture release 16 includes a new 5G physical layer known as 5G NR “New Radio” and a “Cloud Native” 5G Core (5GC) using cloud computing. The 5GC is agnostic to the type of radio access technology used and is enabler of many types of convergence. A novel shared multi-tenant broadcast core network architecture designed to interwork with 5GC is discussed. With the 3GPP 5G NR unicast and Non-3GPP ATSC 3.0 broadcast synergistically aligned using methods of Release 16. This includes using 3GPP Access Traffic Steering, Switching, Splitting (ATSSS) and a multi-radio dual simultaneous connected User Equipment (UE). This aligns ATSC 3.0, the first forward looking (non-backward-compatible) native IP OFDM broadcast standard, with 3GPP LTE/5G unicast as a converged 5G vertical. The proposed method and architecture are orthogonal to LTE broadcast Release 16 and is synergistic to 5G NR mixed-mode multicast unicast in future. [ABSTRACT FROM AUTHOR]

In order to meet the tremendous requirements on the convergence between broadband and broadcast, this paper proposes to incorporate layered division multiplexing (LDM), an important variant of non-orthogonal multiple access (NOMA) technologies, into Millimeter wave (mmWave) multiple-input multiple-output (MIMO) system with device-to-device (D2D) communications to simultaneously provide broadcast and unicast services. The performance of the proposed full-duplex D2D-aided mmWave MIMO-NOMA with randomly deployed users is then analyzed under practical assumptions. Closed-form expressions of both the outage probability and the ergodic capacity are then derived by using the Laplace transform technique. Finally, detailed numerical results reveal the performance gains of the proposed scheme compared with the LDM without D2D communications and conventional time division multiplexing based scheme. The impacts of the antenna array gain and the transmission power of both the base station and the full-duplex link on system performance are also presented. Besides, we observe that it is vital to optimal the transmission power of the full-duplex link which ensures less self-interference to enhance the system capacity. [ABSTRACT FROM AUTHOR]

The ongoing deployment of 5G cellular systems is continuously exposing the inherent limitations of this system, compared to its original premise as an enabler for Internet of Everything applications. These 5G drawbacks are spurring worldwide activities focused on defining the next-generation 6G wireless system that can truly integrate far-reaching applications ranging from autonomous systems to extended reality. Despite recent 6G initiatives (one example is the 6Genesis project in Finland), the fundamental architectural and performance components of 6G remain largely undefined. In this article, we present a holistic, forward-looking vision that defines the tenets of a 6G system. We opine that 6G will not be a mere exploration of more spectrum at high-frequency bands, but it will rather be a convergence of upcoming technological trends driven by exciting, underlying services. In this regard, we first identify the primary drivers of 6G systems, in terms of applications and accompanying technological trends. Then, we propose a new set of service classes and expose their target 6G performance requirements. We then identify the enabling technologies for the introduced 6G services and outline a comprehensive research agenda that leverages those technologies. We conclude by providing concrete recommendations for the roadmap toward 6G. Ultimately, the intent of this article is to serve as a basis for stimulating more out-of-the-box research around 6G. [ABSTRACT FROM AUTHOR]

We consider the problem of minimizing the disturbance power at the output of the matched filter in a single antenna cognitive radar set-up. The aforementioned disturbance power can be shown to be an expectation of the slow-time ambiguity function (STAF) of the transmitted waveform over range-Doppler bins of interest. The design problem is known to yield a nonconvex quartic function of the transmit radar waveform. This STAF shaping problem becomes even more challenging in the presence of practical constraints on the transmit waveform such as the constant modulus constraint (CMC). Most existing approaches address the aforementioned challenges by suitably modifying or relaxing the design cost function and/or the CMC. In a departure from such methods, we develop a solution that involves direct optimization over the nonconvex complex circle manifold, i.e., the CMC set. We derive a new update strategy [quartic-gradient-descent (QGD)] that computes an exact gradient of the quartic cost and invokes principles of optimization over manifolds toward an iterative procedure with guarantees of monotonic cost function decrease and convergence. Experimentally, QGD can outperform state-of-the-art approaches for shaping the ambiguity function under the CMC while being computationally less expensive. [ABSTRACT FROM AUTHOR]

This paper proposes a new prediction-based dynamic multiobjective optimization (PBDMO) method, which combines a new prediction-based reaction mechanism and a popular regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for solving dynamic multiobjective optimization problems. Whenever a change is detected, PBDMO reacts effectively to it by generating three subpopulations based on different strategies. The first subpopulation is created by moving nondominated individuals using a simple linear prediction model with different step sizes. The second subpopulation consists of some individuals generated by a novel sampling strategy to improve population convergence as well as distribution. The third subpopulation comprises some individuals generated using a shrinking strategy based on the probability distribution of variables. These subpopulations are tailored to form a population for the new environment. The experimental results carried out on a variety of bi- and three-objective benchmark functions demonstrate that the proposed technique has competitive performance compared with some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a combined identification/ Q-function fitting methodology that involves identification of a Takagi–Sugeno model, computation of (sub)optimal controllers from linear matrix inequalities (LMIs), and subsequent data-based fitting of the Q-function via monotonic optimization. The LMI-based initialization provides a conservative solution, but it is a sensible starting point to avoid convergence/local-minima issues in raw data-based fitted Q-iteration or Bellman residual minimization. An inverted-pendulum experimental case study illustrates the approach. [ABSTRACT FROM AUTHOR]

This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed optimization technique addresses the situation where the response of the starting point is far away from the design specifications. We propose to utilize multiple feature parameters to help move the passband of the filter response into the range of design specifications. The pole–zero-based neuro-TF is introduced in this article to help extract the multiple feature parameters when the feature parameters of filter responses are not explicitly identified. Furthermore, we propose to derive new optimization objective functions to involve the multiple feature parameters. A new trust-region updating formulation for the modified optimization objective functions is derived to guarantee the optimization convergence. With the assistance of multiple feature parameters, the proposed surrogate-based EM optimization has a better capability of avoiding local minima and can reach the optimal EM solution faster than the surrogate-based EM optimizations without feature assistance. Three examples of EM optimizations of microwave filters are used to demonstrate the proposed technique. [ABSTRACT FROM AUTHOR]

The use of three-dimensional (3D) thermal simulations of magnetic components for power converters is limited to validation of final designs due to time consumption. A two-step homogenization is proposed to simplify them, while keeping good accuracy. The required equations and some guidelines are described in this paper. It can be applied to solid round wire, litz wire, or foil wound magnetic components. Considerations to extend its use to transformers, including the use of interleaving techniques and insulating tape, are explained. This method can reduce the computation time of 3-D simulations (seven times faster than usual simulations) while the convergence is assured even for complex structures or cases with litz wire. Finally, the method is experimentally validated with DC and AC excitation. [ABSTRACT FROM AUTHOR]

Niching techniques have been widely incorporated into evolutionary algorithms (EAs) for solving multimodal optimization problems (MMOPs). However, most of the existing niching techniques are either sensitive to the niching parameters or require extra fitness evaluations (FEs) to maintain the niche detection accuracy. In this paper, we propose a new automatic niching technique based on the affinity propagation clustering (APC) and design a novel niching differential evolution (DE) algorithm, termed as automatic niching DE (ANDE), for solving MMOPs. In the proposed ANDE algorithm, APC acts as a parameter-free automatic niching method that does not need to predefine the number of clusters or the cluster size. Also, it can facilitate locating multiple peaks without extra FEs. Furthermore, the ANDE algorithm is enhanced by a contour prediction approach (CPA) and a two-level local search (TLLS) strategy. First, the CPA is a predictive search strategy. It exploits the individual distribution information in each niche to estimate the contour landscape, and then predicts the rough position of the potential peak to help accelerate the convergence speed. Second, the TLLS is a solution refine strategy to further increase the solution accuracy after the CPA roughly predicting the peaks. Compared with the other state-of-the-art DE and non-DE multimodal algorithms, even the winner of competition on multimodal optimization, the experimental results on 20 widely used benchmark functions illustrate the superiority of the proposed ANDE algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the joint design of Multiple-Input Multiple-Output (MIMO) Space-Time Transmit Code (STTC) and Space-Time Receive Filter (STRF) to improve the detectability of a moving target embedded in signal-dependent clutter in the presence of uncertainties on the target azimuth angle and Doppler frequency. The Signal to Interference plus Noise Ratio (SINR) is considered as a figure of merit to maximize and multiple restrictions on STTC are forced at the design stage, i.e., similarity and modulus constraints as well as a uniform power requirement among the transmit antennas. To deal with the resulting non-convex design problem, a novel polynomial-time iterative procedure based on Successive Greedy Optimization (SGO) framework ensuring that SINR monotonically increases and converges to a finite value, is developed. Each iteration of the proposed algorithm involves the generalized eigenvalue decomposition to design STRF and a nested iterative technique involving Dinkelbach's procedure and an Alternating Direction Penalty Method (ADPM) to obtain STTC. Numerical results are provided to assess the performance of the proposed algorithm against some counterparts in terms of the optimized SINR value and computational complexity. [ABSTRACT FROM AUTHOR]

Long-term evolution (LTE) and wireless local area network (WLAN) are often presented as opposing technologies. Hence, efficient partitioning of the spectrum resources carries critical importance for achieving the coexistence of these on the unlicensed spectrum band. In this paper, we firstly develop an online spectrum partitioning algorithm, which needs little signal transmission and exchange between coordination manager and networks. Then, we focus on the convergence analysis of the online spectrum partitioning algorithm, which is difficult due to the time-varying wireless channels. To overcome this challenge, we model the algorithm and network dynamics as the stochastic differential equations (SDE) and show that the algorithm convergence is equivalent to the stochastic stability of a virtual stochastic dynamic system constructed by the SDEs. Then, we give the sufficient condition about the algorithm convergence and the upper bound on the tracking error of the spectrum partitioning algorithm under exogenous variations of time-varying channel state information (CSI). Based on the insights of the impact of time-varying CSI on algorithm convergence, an online compensative spectrum partitioning algorithm is developed to offset the tracking error caused by the disturbance of time-varying CSI. Through performance evaluation, we show that the coexistence performance efficiency will come at low expense of algorithm complexity and signal overhead. [ABSTRACT FROM AUTHOR]

Sequences with low autocorrelation sidelobes are needed in a diverse set of active sensing applications, including radar, sonar, communications, and biomedicine. The recently proposed and widely adopted methods of the Cyclic Algorithm-New (CAN) and Periodic CAN (PeCAN) are known to be computationally efficient, particularly as they employ fast Fourier transform (FFT) operations to design unimodular (i.e., unit-modulus) sequences with good autocorrelation properties. However, these cyclic algorithms cannot be directly used to design binary sequences with good autocorrelation properties due to the extremely multimodal nature of the associated optimization objectives. In this paper, we combine the notion of converging functions with the CAN and PeCAN frameworks, to considerably enhance their performances for binary sequence synthesis. Moreover, the convergence of these algorithms are established and their convergence properties are thoroughly analyzed. Numerical examples are provided to show that the proposed algorithms can outperform existing approaches for aperiodic and periodic binary sequence synthesis, especially for long sequence designs. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of radar range-Doppler imaging in the presence of clutter. Specifically, we formulate the range-Doppler imaging problem as that of recovery of a sparse vector contaminated by clutter in addition to noise. We propose a sparse Bayesian learning (SBL)-based algorithm to jointly obtain the range-Doppler image, variance of the noise, and covariance matrix of the clutter. Furthermore, we adapt a simple pruning mechanism that reduces the computational cost and improves the convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, a distributed resource allocation problem is investigated based on a continuous-time algorithm in the presence of time-varying communication delays. The considered delays are allowed to take both “large” and “small” delay values in an alternating manner. The “small” delays refer to a class of delays that guarantee the exponential convergence to the optimal solution; otherwise, they are called “large” delays. The existence of “large” delays may affect the behavior of the optimal allocation solution, and the existing algorithms with delays may not be applicable to this kind of “large” delays. To address “large” delay cases, a switched algorithm is first proposed based on switching techniques, and then, such an algorithm is modeled as switched delay systems. Second, by adopting the Lyapunov functional theory, the upper bound of delays is first derived to guarantee the exponential convergence to the optimal solution under “small” delays, and then, a delay-dependent sufficient condition is obtained to ensure exponential convergence of the proposed algorithm based on some restrictions on “large” delays. Finally, we adopt an IEEE 118-bus practical example to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Nonlinear signal processing is important in various applications, however it usually requires high computational cost. This paper proposes a low-complexity nonlinear adaptive least mean squares (LMS) filter based on decomposable Volterra kernels. The decomposability condition comes from a well-posed approximation problem, which imposes a rank-one structure on the full Volterra model, resulting in a system equivalent to a product of ordinary linear filters. A mean-square estimation problem is posed over such a decomposable Volterra model (DVM) and a solution via a steepest descent algorithm is introduced, subsequently motivating an adaptive implementation. The resulting DVM-LMS filter is nonlinear in the input signal as well as in its parameters, however leading to an exponential decrease in computational complexity, as compared to the full Volterra kernel. The nonlinearity in the parameters may introduce instabilities, which are tackled via normalizing strategies, introduced together with heuristics for proper initialization. Necessary conditions for stability are derived and serve as guidance to design the step-size, while simulations show that the algorithm is unbiased after convergence. Additionally, an expression for the steady-state excess mean square error is derived. Several simulations show the new filter's performance and competitiveness against celebrated methods in the literature. [ABSTRACT FROM AUTHOR]

Transmission–distribution coordinated energy management (TDCEM) is recognized as a promising solution to the challenge of high distributed energy resource (DER) penetration, but there is a lack of a distributed computation method that universally and effectively works for the TDCEM. To bridge this gap, this paper presents a generalized master–slave-splitting (G-MSS) method. This method is based on a general-purpose transmission–distribution coordination model called G-TDCM, which enables the G-MSS to be applicable to most of the central functions of the TDCEM. In this G-MSS method, a basic heterogeneous decomposition (HGD) algorithm is first derived from the heterogeneous decomposition of the coupling constraints in the optimality conditions of the G-TDCM. Its optimality and convergence properties are proved. Then, inspired by the sufficient conditions for convergence, a modified HGD algorithm that utilizes the subsystem's response function is developed and demonstrated to converge faster. The distributed G-MSS method is then demonstrated to successfully solve a series of central functions of the TDCEM, e.g., power flow, contingency analysis, voltage stability assessment, economic dispatch, and optimal power flow. The severe issues of over-voltage and erroneous assessment of the system security that are caused by DERs are thus resolved by the G-MSS method with modest computation cost. [ABSTRACT FROM AUTHOR]

In this paper, we present an observer-based partial integrated guidance and control (PIGC) scheme for missiles to achieve hit-to-kill attack at a given impact angle. This is particularly useful as the lethality of a missile can be maximized if collision occurs at a certain angle. A novel sliding mode observer is proposed to estimate all states of the system in finite time, from which the PIGC is designed to make all states converge to zero and achieve the objective. The observer parameters are automatically updated by adaptive laws, which removes the requirement of knowing the bound of disturbances and hence making the scheme more applicable to real missile systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel joint radio resource allocation and successive interference cancellation (SIC) ordering algorithm for the downlink scenario of a power-domain sparse code multiple access (PSMA) based wireless network. To this end, we formulate a novel joint radio and SIC ordering optimization problem. Our aim is to maximize sum rate over codebook assignment, transmit power, and SIC ordering variables. Since the proposed optimization problem is NP-hard and mathematically intractable, we exploit alternate search method (ASM). Based on this algorithm, the proposed optimization problem is decomposed into three sub-problems, namely, codebook assignment, power allocation, and SIC ordering. To solve the codebook assignment sub-problem, we exploit matching theory and submodularity and compared them from the performance and complexity perspectives. Due to the fact that PD-NOMA-based networks adopt SIC at the receiver side, the proposed SIC ordering algorithm is also studied for this type of networks. Moreover, the proposed algorithms are investigated from the performance, convergence, and computational complexity perspectives. Numerical results show that the proposed SIC ordering method outperforms the conventional one by approximately 48% in the studied NOMA-based networks. [ABSTRACT FROM AUTHOR]

In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

Matrix and tensor completion aim to recover a low-rank matrix / tensor from limited observations and have been commonly used in applications such as recommender systems and multi-relational data mining. A state-of-the-art matrix completion algorithm is Soft-Impute, which exploits the special "sparse plus low-rank" structure of the matrix iterates to allow efficient SVD in each iteration. Though Soft-Impute is a proximal algorithm, it is generally believed that acceleration destroys the special structure and is thus not useful. In this paper, we show that Soft-Impute can indeed be accelerated without comprising this structure. To further reduce the iteration time complexity, we propose an approximate singular value thresholding scheme based on the power method. Theoretical analysis shows that the proposed algorithm still enjoys the fast $O(1/T^2)$O(1/T2) convergence rate of accelerated proximal algorithms. We also extend the proposed algorithm to tensor completion with the scaled latent nuclear norm regularizer. We show that a similar "sparse plus low-rank" structure also exists, leading to low iteration complexity and fast $O(1/T^2)$O(1/T2) convergence rate. Besides, the proposed algorithm can be further extended to nonconvex low-rank regularizers, which have better empirical performance than the convex nuclear norm regularizer. Extensive experiments demonstrate that the proposed algorithm is much faster than Soft-Impute and other state-of-the-art matrix and tensor completion algorithms. [ABSTRACT FROM AUTHOR]

This letter presents a rigorous demonstration of the convergence of the Newton fractional delay filter to the ideal fractional delay filter. The Newton structure is a very efficient implementation of Lagrange interpolation using a variable fractional delay filter structure. Through the developed demonstration, a new approach is proposed to define the ideal fractional delay filter as the limit of any digital filter implementing on Lagrange interpolation. This letter also proves that the Z-transform expression of the ideal fractional delay has a fully defined frequency response on the unit circle. [ABSTRACT FROM AUTHOR]

In this paper, we design constant modulus probing waveforms with good correlation properties for collocated multi-input multi-output radar systems. The main content is as follows: first, we formulate the design problem as a fourth-order polynomial minimization problem with constant modulus constraints. Then, by exploiting introduced auxiliary variables and their inherent structures, the polynomial optimization model is equivalent to a non-convex consensus minimization problem. Second, a customized alternating direction method of multipliers (ADMM) algorithm is proposed to solve the non-convex problem approximately. In the algorithm, all the subproblems can be solved analytically. Moreover, all subproblems except one subproblem can be performed in parallel. Third, we prove that the customized ADMM algorithm is theoretically guaranteed convergent if proper parameters are chosen. Finally, two variant ADMM algorithms, based on stochastic block coordinate descent and accelerated gradient descent, are proposed to reduce computational complexity and speed up the convergence rate. Numerical examples show the effectiveness of the proposed consensus-ADMM algorithm and its variants. [ABSTRACT FROM AUTHOR]

Although massive multiple-input multiple-output (MIMO) promises high spectral efficiency, there are several issues that significantly limit the potential gain of massive MIMO, such as severe inter-cell interference, huge channel state information (CSI) overhead/delay, high cost and power consumption of RF chains, and user fairness. Several precoding schemes have been proposed for massive MIMO but there still lacks a systematic solution to address all of the aforementioned issues especially for the multicell case. In this paper, we propose a randomized two-timescale hybrid precoding (RTHP) scheme for the downlink transmission of multicell massive MIMO systems. RTHP allows time sharing among multiple sets of analog precoders to achieve better tradeoff between sum throughput and fairness. Moreover, the time-sharing factors and analog precoders are adapted to the long-term channel statistics to mitigate inter-cell interference with reduced CSI overhead and RF chains. The digital precoder is used to support multiuser MIMO at each base station based on local low-dimensional effective CSI only. As such, RTHP resolves all of the aforementioned issues. We formulate the optimization of RTHP as a general network utility maximization problem (GNUMP), which is a two-timescale stochastic non-convex problem, and consequently challenging to solve. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop an online algorithm, which is shown to converge to stationary solutions of the approximate GNUMP. Finally, simulation results verify the advantages of the proposed scheme over baseline schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a new guidance law based on integral sliding mode control (ISMC) technique for maneuvering target interception with impact angle constraint. A time-varying function weighted line-of-sight error dynamics, representing the nominal guidance performance, is introduced first. The proposed guidance law is derived by utilizing ISMC to follow the desired error dynamics. The convergence of the guidance law developed is supported by Lyapunov stability. Simulations with extensive comparisons explicitly demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR), as a wideband radar system, is subject to interference by radio frequency systems, such as radio, TV, and cellular networks. Since the narrowband radio frequency interference (RFI) has a stable frequency in a snapshot sequence, it has a low-rank property that can be used to substract RFI from the received signal. The nuclear norm is a common convex relaxation to constrain the rank, but it is optimized by the singular value thresholding (SVT) algorithm, which uses a single threshold to treat all singular values and greatly over-punishes large singular values. Hence, in this paper, we propose two methods, the reweighted nuclear norm (RNN) algorithm and the reweighted Frobenius norm (RFN) algorithm, to separate the RFI and the useful signal. The RNN and RFN minimization problems are the approximations of the real rank function, which can protect large singular values and restrict the rank. As a result, the RFI is accurately extracted and the useful signal is successfully protected. Also, we strictly derive the closed-form solutions of the RNN and RFN minimization problems for complex radar signals, and we also employ downsampling to extract the mainband of the signal spectrum to speed up the convergence. Real SAR data is applied to demonstrate the effectiveness of the proposed methods for RFI suppression. [ABSTRACT FROM AUTHOR]

Endmember extraction (EE) plays an important role in the quantitative analysis of hyperspectral images, as the main step in the decomposition of mixed pixels. At present, scholars have proposed many EE algorithms based on the linear spectral mixture model and the convex geometry principle, such as the pixel purity index (PPI) and the vertex component analysis (VCA). At the same time, many intelligent optimization algorithms, such as the particle swarm optimization (PSO) and the discrete PSO (DPSO), have been applied to EE, which can get promising results for real images. However, PSO and DPSO have theoretical limitations and cannot guarantee the global convergence. The problem of premature convergence will reduce the accuracy of the EE result. The quantum-behaved PSO (QPSO) can theoretically guarantee the convergence of the algorithm by combining the quantum mechanics into the PSO. In order to increase the accuracy of the algorithm, this paper proposes an improved QPSO (IQPSO) algorithm for EE. IQPSO has made innovations in population coding and initialization methods. Besides, the collaborative approach for updating the optimal positions of particles can help to solve the difficulties caused by high dimensions. The experimental results show that IQPSO can extract endmembers efficiently and effectively. [ABSTRACT FROM AUTHOR]

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer network, where a UAV flies at a constant altitude in the sky to provide wireless energy supply for a set of ground nodes with a linear topology. Our objective is to maximize the minimum received energy among all ground nodes by optimizing the UAV’s one-dimensional (1D) trajectory, subject to the maximum UAV flying speed constraint. Different from previous works that only provided heuristic and locally optimal solutions, this paper is the first to present the globally optimal 1D UAV trajectory solution to the considered min-energy maximization problem. Toward this end, we first show that for any given speed-constrained UAV trajectory, we can always construct a maximum-speed trajectory and a speed-free trajectory, such that their combination can achieve the same received energy at all these ground nodes. Next, we transform the UAV-speed-constrained trajectory design problem into an equivalent UAV-speed-free problem, which is then optimally solved via the Lagrange dual method. The optimal 1D UAV trajectory solution follows the so-called successive hover-and-fly structure, i.e., the UAV successively hovers at a finite number of hovering points each for an optimized hovering duration, and flies among these hovering points at the maximum speed. Building upon the optimal UAV trajectory structure, we further present a low-complexity UAV trajectory design by first transforming the original problem into an equivalent non-convex problem with only the UAV hovering locations and durations as optimization variables and then updating the trajectory via the successive convex approximation technique. Our analysis shows that the low-complexity design is guaranteed to converge to a suboptimal solution at a significantly lower complexity irrespective of the geographical network size. Numerical results show that the proposed low-complexity design actually achieves the same performance as the proposed optimal solution, and both of them outperform the benchmark algorithms in prior works under different scenarios. [ABSTRACT FROM AUTHOR]

A new globally exponentially synchronization criterion for complex dynamical networks is proposed in this paper taking account of discrete time communications units and time delay is considered, in which globally exponential convergence is achieved. By introducing larger decision matrices to gain time-delay synchronization criterion, it not only inherits the benefits of full utilization of available information, but also has less conservatism. At the same time, except for original system parameters, no more new decision variables are increased. A time-dependent Lyapunov functional approach is proposed to prove the effectiveness of the criterion. Some simulation results hold the feasibility of the theory. [ABSTRACT FROM AUTHOR]

In multimedia crowdsourcing, the requester’s quality requirements and reward decisions will affect the workers’ task selection strategies and the quality of their multimedia contributions. In this paper, we present a first study on how the workers’ bounded cognitive rationality interacts with and affects the decisions and performance of a multimedia crowdsourcing system. Specifically, we consider a two-stage model, where a requester first determines the reward and the quality requirement for each task, and the workers select the tasks to accomplish accordingly. First, we consider the benchmark case where users are fully rational, and derive the requester’s optimal rewards and quality requirements for the tasks. Furthermore, we focus on the more practical bounded rational case by modeling the workers’ task selection behaviors using the cognitive hierarchy theory. Comparing with the fully rational benchmark, we show that the requester can increase her profit by taking advantage of the workers’ bounded cognitive rationality, especially when the workers’ population is large or the workers’ average cognitive level is low. When the workers’ average cognitive level is very high, however, the equilibrium under the practical bounded rational model converges to that under the benchmark fully rational model. It is because the workers at different levels make decisions sequentially and high cognitive level workers can accurately predict other users’ strategies. Under both the fully and bounded rational models, we show that if workers are heterogeneous but one type of workers (either the high or the low quality) dominates the platform, the requester cannot make a higher profit by setting different quality requirements for different tasks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new synthetic aperture radar (SAR) image despeckling method based on the nonlocal low-rank minimization model. First, some similar image patches are selected for each pixel to construct the patch group matrix (PGM). Then, a new low-rank minimization model, called Fisher–Tippett distribution (FT)-weighted nuclear norm minimization (WNNM), is proposed to recover the underlying low-rank component from the PGM. Specifically, the FT-WNNM is developed by reformulating the despeckling problem as the maximizing a posterior probability problem. The new model consists of a data fidelity term and a regularization term (also called prior term). The data fidelity term is derived from the statistical distribution of SAR images in the logarithm domain, which is known as the Fisher–Tippett distribution, and the regularization term is the recent weighted nuclear norm. Then, the alternating direction method of multipliers (ADMM) is introduced to solve the corresponding optimization problem. Under ADMM framework, the resulting subproblems can be solved efficiently and the convergence can be guaranteed. Extensive experiments on both simulated and real SAR images demonstrate that the proposed method can achieve comparable or even better despeckling performance than some state-of-the-art despeckling algorithms. [ABSTRACT FROM AUTHOR]

To meet the real-time requirements of device-to-device (D2D) communications, we study the scheduling problems of real-time D2D communications to handle the challenges introduced by the stochastic packet arrivals, packet losses, and heterogeneous deadlines. In this paper, we first model the scheduling problem by the Markov Decision Process (MDP), and then propose an $\epsilon$ -optimal scheduling algorithm based on this model. Considering the curse of dimensionality of the MDP-based model, we further model the scheduling problem by the restless multi-armed bandit model. Based on the model, we prove the indexability of the scheduling problem and derive the closed-form Whittle's index. According to the closed-form index, we thus propose an index-based scheduling algorithm that has very low time complexity. Finally, numerous simulations demonstrate the effectiveness of the proposed algorithms. The $\epsilon$ -optimal scheduling algorithm converges very fast to optimal solutions with small-sized networks and small $\epsilon$. The index-based scheduling algorithm outperforms the compared algorithms in terms of the total discounted reward. [ABSTRACT FROM AUTHOR]

A novel multi-function saturated-core fault current limiter (MFCL) with two operating modes is proposed in this paper. MFCL can achieve fault current limitation and power flow management by switching operating mode: current-limiting mode and power-flow-controlling mode, which realizes technology convergence. The MFCL consists of an iron core with rotatable magnetic valve, ac coils, dc coils, and a fault-limiting reactor. The operating principle of the MFCL and the design of the rotatable magnetic valve are analyzed in this paper. The finite-element analysis (FEA) model based on Maxwell is used to analyze the MFCL. Simulation results depict that the MFCL can limit fault current effectively and provide variable impedance for power flow controlling. [ABSTRACT FROM AUTHOR]