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To efficiently trade off system sum-rate and link fairness, this paper is dedicated to maximizing the sum of \alpha-fair utility in spectrum-sharing networks, where multiple interfering links share one channel. In the literature, three special cases, including \alpha=\mbox{0} (sum-rate maximization), \alpha=\mbox{1} (proportional fairness), and \alpha=\infty (max-min fairness), have been investigated; the complexity for cases \mbox{1} < \alpha < \infty and \mbox{0} < \alpha < \mbox{1} is still unknown. In this paper, we prove that the problem is convex when \mbox1 < \alpha < \infty and is NP-hard when \mbox0 < \alpha < \mbox1. To deal with the latter case, we transform the objective function and represent it by the difference of two concave functions (D.C.). Then, a power allocation algorithm is proposed with fast convergence to a local optimal point. Simulation results show that the proposed algorithm can obtain global optimality in two-link cases when \mbox0 < \alpha < \mbox1. In addition, we can get a flexible tradeoff between sum-rate and fairness in terms of Jain's index by adjusting $\alpha$. [ABSTRACT FROM AUTHOR]

Distributed generators are integrated into microgrids or geographically distributed subsystems in active distribution networks and may belong to various entities. Centralized solutions cannot meet future requirements such as high resilience and privacy protection. Therefore, fully distributed economic dispatch (ED) methods are needed and most existing algorithms exhibit linear or sublinear convergence. This paper introduces an efficient fully distributed dynamic ED solution for minimizing photovoltaic power curtailment, based on a parallel primal-dual interior-point algorithm with a quasi-Newton technique. In this method, each area optimizes its own problem with limited information exchanged with its neighbors, and no central coordinator is needed. Based on a peer-to-peer communication paradigm, this method can achieve a global optimum while the privacy of each area is obliquely protected. Numerical tests demonstrated that the algorithm outperforms gradient-based methods such as ADMM and maintains stability under a partial communication failure. [ABSTRACT FROM AUTHOR]

Correct estimation of harmonics and sequence components is essential to monitor the overall performance of three phase power systems. Least mean square (LMS) based adaptive models have simpler structures to estimate the required parameters for a given power quality (PQ) event. However, these models are not very much effective in case of time-varying and short duration disturbances. Least mean fourth (LMF) is comparatively a better approach than LMS to deal with different PQ problems, but high computational complexity limits the filter performance in case of practical applications. LMS/fourth (LMS/F) filter is a compromise between LMS and LMF to improve the error convergence properties. The proposed model explained in this paper is based on the concept of LMS/F with Volterra expansions of input samples. The proposed Volterrra LMS/F filter is tested in case of distorted power signals to estimate harmonics, decaying dc, and sequence components in presence of white Gaussian noise. The detail comparison results are presented using MATLAB simulations to verify the performance of the adaptive model. [ABSTRACT FROM AUTHOR]

Stochastic approximation techniques play an important role in solving many problems encountered in machine learning or adaptive signal processing. In these contexts, the statistics of the data are often unknown a priori or their direct computation is too intensive, and they have thus to be estimated online from the observed signals. For batch optimization of an objective function being the sum of a data fidelity term and a penalization (e.g., a sparsity promoting function), Majorize-Minimize (MM) methods have recently attracted much interest since they are fast, highly flexible, and effective in ensuring convergence. The goal of this paper is to show how these methods can be successfully extended to the case when the data fidelity term corresponds to a least squares criterion and the cost function is replaced by a sequence of stochastic approximations of it. In this context, we propose an online version of an MM subspace algorithm and we study its convergence by using suitable probabilistic tools. Simulation results illustrate the good practical performance of the proposed algorithm associated with a memory gradient subspace, when applied to both nonadaptive and adaptive filter identification problems. [ABSTRACT FROM PUBLISHER]

By employing the finite-time control method, the consensus control algorithm for higher-order multiagent systems is designed in this paper. Under a neighbor-based rule, a higher-order finite-time consensus algorithm is explicitly constructed, which only uses local information. The finite-time consensus control algorithm can guarantee that the state consensus is achieved in a finite time. In addition, for multiagent systems having a leader-following structure, the consensus algorithm is also designed. Finally, two examples are presented to show the effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate \mathcal O(1/\lambda T) , but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain \mathcal O(\sqrt\log (1/\delta)/T) with $\delta$ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately \mathcal {O}(\log (\log (T)/\delta)/\lambda T) . [ABSTRACT FROM AUTHOR]

In this paper, a novel algorithm “firework optimization” (FO) is introduced, as well as its hybridization with generating set search (GSS). FO is an exploration algorithm, which intends to explore a vast region within the search space in order to determine regions that may contain a regional optimum. As the regions with potential are defined, GSS, the exploitation algorithm is applied to determine whether regions determined by FO contain a local or a global optimum. The validity of FO and GSS hybridization algorithm is verified using Branin and Six-Hump Camel test functions, and the hybridization algorithm is applied to an interior permanent magnet synchronous motor, in order to minimize torque ripple. [ABSTRACT FROM AUTHOR]

In this paper, a novel global search algorithm, implementing principal component optimization (PCO) in combination with mesh adaptive direct search (MADS) is proposed. The PCO is based on principal component analysis, a widely used multivariate technique in statistics, while MADS is a highly effective local search algorithm, implemented to find local optima. Their integration reduces computation time and improves convergence reliability to a global optimum. The proposed algorithm is verified via application of a test function and comparison to conventional hybrid algorithms such as particle swarm optimization with MADS. Finally, the algorithm is applied to the optimal design of an interior permanent magnet synchronous machine for minimization of total harmonic distortion and torque ripple via finite element analysis. [ABSTRACT FROM AUTHOR]

This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit–receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of nonoverlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R, which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R \le \text{2} at the LLs, the stability of the entire network can be assured, even when each user only has partial network information. For practical implementation, we propose each LL to use R=\text{2}$ as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed, and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput. [ABSTRACT FROM PUBLISHER]

In this paper, we study the energy-efficient resource allocation problem for device-to-device (D2D) communication underlaying cellular networks, which aims to maximize the minimum weighted energy efficiency of D2D links while guaranteeing minimum data rates for cellular links. We first characterize the optimal power allocation of the cellular links to transform the original resource allocation problem into the joint subchannel and power allocation problem for D2D links. We then propose three resource allocation algorithms with different complexity levels, namely, dual-based, branch-and-bound (BnB), and relaxation-based rounding (RBR) algorithms. While the dual-based algorithm solves the problem by using the dual decomposition method, the BnB and RBR algorithms tackle the problem by employing the relaxation approach. We establish the strong performance guarantees for the proposed algorithms through theoretical analysis. Extensive numerical studies demonstrate that the proposed algorithms achieve superior performance and significantly outperform a conventional algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel memetic algorithm, which is explorative particle swarm optimization (ePSO), combined with mesh adaptive direct search and apply it to the design of a permanent magnet synchronous machine (PMSM). The ePSO, which is modified from the PSO, drastically improves search time and iteration number at an exploration search stage. Unlike the existing methods, the proposed rule of start point selection takes an advantage of minimizing the search time. By applying the proposed algorithm to PMSM, we clarify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A multiband-structured subband adaptive filter (MSAF) algorithm was introduced to achieve a fast convergence rate for the correlated input signal. The convergence analysis of the adaptive filter algorithm is an important concept because it provides a guideline to design the adaptive filter. However, the convergence analysis of the MSAF algorithm has not been researched as extensively as that of the normalized least-mean-square algorithm. Therefore, it needs to be researched. In this paper, we present a new approach to the mean-square deviation (MSD) analysis of the MSAF algorithm by using the persistently exciting input and the practical assumption that the stopband attenuation of the prototype filter is high. Unlike the previous analysis, the proposed analysis is possible to be applied to the long-length adaptive filter such as the acoustic echo cancellation. The proposed analysis is also applied to a non-stationary model with a random walk of the optimal weight vector. The simulation results match with the theoretical results in both the transient-state and steady-state MSD. [ABSTRACT FROM AUTHOR]

This paper aims to address a new class of multiscale coordination control problems for multivehicle systems that are subject to external disturbances. Each vehicle is measured by an independent scale due to the constraints of physical environments. Based on the nearest neighbor-interaction rules, three algorithms of different convergence rates are presented such that the vehicles are guaranteed to achieve consensus on a common quantity for all but of their own scales. Simulation tests are performed to validate the effectiveness and robustness of these consensus algorithms. By introducing a Lyapunov potential function, the consensus analysis is further achieved for the three multiscale algorithms, regardless of them with an exponential, finite-time, or fixed-time convergence rate and in the presence of bounded disturbances. [ABSTRACT FROM PUBLISHER]

In space communication environments, most studies in delay tolerant networks (DTNs) adopt experimental approaches and they have shown that the utilization efficiency of link bandwidth can be affected by changing packet sizes at the bundle layer and the convergence layer. However, the lack of theoretical work makes it difficult to find the optimal packet sizes in general scenarios. In this paper, we focus on this important issue by performing a theoretical analysis to solve the packet size optimization problems in both one-hop and multihop DTNs. As our analysis is based on the widely used DTN architecture with Bundle Protocol (BP) and Licklider Transmission Protocol (LTP), the theoretical results can be applied to any DTN scenario using this architecture. We formulate packet size optimization problems in DTNs as nonconvex optimization problems. Furthermore, a goodput enhancement algorithm (GEA) is also proposed to find the optimal packet sizes in order to maximize goodput in DTNs. We conduct numerical analysis as well as simulations to validate our theoretical analysis and study the factors that determine the optimal packet sizes for BP and LTP. Numerical results show that GEA achieves significant performance improvement compared with schemes where packet size optimization on BP and LTP is not jointly considered. [ABSTRACT FROM PUBLISHER]

In this paper, we study the distributed spectrum sensing in cognitive radio networks. Existing distributed consensus-based fusion algorithms only ensure equal gain combining of local measurements, whose performance may be incomparable to various centralized soft combining schemes. Motivated by this fact, we consider practical channel conditions and link failures, and develop new weighted soft measurement combining without a centralized fusion center. Following the measurement by its energy detector, each secondary user exchanges its own measurement statistics with its local one-hop neighbors, and chooses the information exchanging rate according to the measurement channel condition, e.g., the signal-to-noise ratio (SNR). We rigorously prove the convergence of the new consensus algorithm, and show all secondary users hold the same global decision statistics from the weighted soft measurement combining throughout the network. We also provide distributed optimal weight design under uncorrelated measurement channels. The convergence rate of the consensus iteration is given under the assumption that each communication link has an independent probability to fail, and the upper bound of the iteration number of the $ \epsilon$ -convergence is explicitly given as a function of system parameters. Simulation results show significant improvement of the sensing performance compared to existing consensus-based approaches, and the performance of the distributed weighted design is comparable to the centralized weighted combining scheme. [ABSTRACT FROM PUBLISHER]

Langasite (LGS) is a promising material for SAW applications at high temperature. However, the temperature coefficients of LGS material constants are not accurate enough to perform reliable simulations, and therefore to make good use of available design tools, above 300°C. In the first part of the paper, we describe a new possible way to derive these coefficients in a wider temperature range. The method is based on Simulated Annealing, a well-known optimization algorithm. The algorithm converges toward a set of optimized temperature coefficients of the stiffness constants which are used to perform accurate simulations up to at least 800°C. In the second part, a deeper analysis of the algorithm outputs demonstrates some of its strengths but also some of its main limitations. Possible solutions are described to predict and then improve the accuracy of the optimized coefficient values. In particular, one solution making use of additional BAW target curves is tested. A promising solution to extend the optimization to the temperature coefficients of piezoelectric constants is also discussed. [ABSTRACT FROM AUTHOR]