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Mobile Network Operator (MNO)s avail the benefit of providing an isolated service with the network slice in a variety of forms like enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communications (mMTC). However, they face challenges in handling situations of overload, congestion, and scaling with the arrival of unexpected control plane User Service Requests (USRs) leading to the dropping of USRs, and hence disturbing the slice’s High Availability (HA). This paper presents a novel High Availability supportive self Reliant NEtwork Slicing System (HARNESS) for 5G Core, powered by intelligent and autonomous Self Organizing Network (SON) paradigm. In HARNESS, we propose algorithms to intelligently schedule and serve the significant portion of control plane USRs for both delay tolerant and delay sensitive slices, to ensure their uninterrupted HA service provision. Along with efficient resource utilization, the HARNESS shows improvements over the traditional HA provisioning methods, in preventing the occluding of crucial control plane USRs by 60% and reducing the average response time for these USRs by 50%. We developed the HARNESS framework in a 5G test-bed system using eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) mechanism for coherent and optimized end-to-end working of it. With this additional gear, HARNESS productively contributes to achieving 3.2% better slice service HA in the minimal two nodes active/active cluster configuration. [ABSTRACT FROM AUTHOR]

In this article, a spatial-temporally varying generalized Pareto intensity distribution (STV-GPID) model is presented to characterize large-scene sea clutter in high-resolution maritime surveillance radars, and a multiscan recursive Bayesian bipercentile (MSRB-BiP) estimation method is proposed to implement the outlier-robust estimation of parameters in the STV-GPID model. Considering that sea clutter characteristics are affected by sea states and the viewing geometry of a radar, the large scene is segmented into clutter map cells based on an empirical backscattering coefficient model of sea surface to predict radar cross section (RCS) of sea surface per unit physical area. Sea clutter intensities on each clutter map cell are modeled by a GPID. In the parameter estimation, the data of previous scans are transformed into the prior information on the parameters to reduce the storage burden of radar systems. The MSRB-BiP estimator updates the parameters of the STV-GPID model recursively by a mixed sample set with the returns of the present scan and simulated data using the prior information. The mixture ratio adjusts the forgetting rate of data to adapt to temporally varying characteristics of sea clutter. At least, it brings three merits: low storage requirement, outlier robustness, and mitigation of spatial small sample size of a single scan. The convergence and robustness of the estimation method are verified by simulated data. The experimental results on two measured radar datasets verify the effectiveness of the MSRB-BiP estimators, and the errors at the steady state are reduced at least 17.7% and 66.7%, respectively. [ABSTRACT FROM AUTHOR]

Ultra-reliable low latency communication (URLLC) requires the packet error rate to be on the order of $10^{-9}$ - $10^{-5}$. Determining the appropriate transmission rate to satisfy this ultra-reliability constraint requires deriving the statistics of the channel in the ultra-reliable region and then incorporating these statistics into the rate selection. In this paper, we propose a framework for determining the rate selection for ultra-reliable communications based on the extreme value theory (EVT). We first model the wireless channel at URLLC by estimating the parameters of the generalized Pareto distribution (GPD) best fitting to the tail distribution of the received powers, i.e., the power values below a certain threshold. Then, we determine the maximum transmission rate by incorporating the Pareto distribution into the rate selection function. Finally, we validate the selected rate by computing the resulting error probability. Based on the data collected within the engine compartment of Fiat Linea, we demonstrate the superior performance of the proposed methodology in determining the maximum transmission rate compared to the traditional extrapolation-based approaches. [ABSTRACT FROM AUTHOR]

In this paper, we present a sequential decoding metric function, which leads to significantly improved computational complexity while maintaining the superiority of polarization-adjusted convolutional (PAC) codes’ error-correction performance. With the proposed metric function, the PAC codes’ decoding computational complexity is comparable to the computational complexity of sequential decoding of conventional convolutional codes (CCs). Moreover, simulation results show an improvement in the error-correction performance of low rate PAC codes when using the proposed metric function. Simulation results also show that using the proposed metric, the upper bound on the PAC codes’ computational complexity has a Pareto distribution. To reduce the worst-case latency of PAC sequential decoder, we limit the number of searches performed by sequential decoder. The results show that for PAC codes of length 128, search-limited sequential decoding can achieve an error-correction performance close to the error-correction performance of polar codes with successive cancellation list decoding with list size 64 and CRC length 11 with considerably less computational complexity. [ABSTRACT FROM AUTHOR]

It is a common sense that people harbors the belief that stragglers exert huge influence upon the performance conducted by the analysis systems of big data for the reason of poor performance made by some computing nodes, data skew and so on. Accordingly, stragglers have been billed as an indispensable bottleneck in Map-Reduce framework processing. However, existing studies on stragglers identification are targeting coarse-grained detection, schedule level optimization and off-line log based cause analysis. Accuracy identifying the stragglers in time for each job, however, is an extremely tough because (1) Auite a number of root causes for stragglers in data analytics frameworks;(2) The number of key parameters affecting stragglers identification; and (3) The different clusters configurations, and their impact on the stragglers detection, vary among different job types and sizes. Either existing solutions adopt a “tweak-and-pray” manual tuning approach, which is complex, time-consuming and error-prone, or only most of them fix theirs eyes upon coarse-grained straggler detection. In this paper, we systematically conduct the exploration on the fundamental problem of automatic, adaptive straggler identification on big data analytics platform. Under the inspiration of the recent triumphs over implementing Reinforcement Learning (RL) techniques for solving complex online optimal problems, we conducted investigation that Reinforcement learning are reasonably employed to adaptively opt the optimal parameters to identify stragglers free of the intervention of human beings. Specifically, we propose Hawkeye, a general adaptive speculative execution system which identifies stragglers by reinforcement are learning to launch speculative tasks on heterogeneous cluster at runtime. In accordance with the experimental conclusions, Hawkeye manages to cut down the job completion time over the distinct type applications. An instance is that it reveals as many as nearly 37% decrease average job completion time based on an improvement of 23% on the preciseness of the present resolutions to the heterogeneous cluster.

A proper channel modeling methodology that characterizes the statistics of extreme events is key in the design of a system at an ultra-reliable regime of operation. The strict constraint of ultra-reliability corresponds to the packet error rate (PER) in the range of 10−9−10−5 within the acceptable latency on the order of milliseconds. Extreme value theory (EVT) is a robust framework for modeling the statistical behavior of extreme events in the channel data. In this paper, we propose a methodology based on EVT to model the extreme events of a non-stationary wireless channel for the ultra-reliable regime of operation. This methodology includes techniques for splitting the channel data sequence into multiple groups concerning the environmental factors causing non-stationarity, and fitting the lower tail distribution of the received power in each group to the generalized Pareto distribution (GPD). The proposed approach also consists of optimally determining the time-varying threshold over which the tail statistics are derived as a function of time, and assessing the validity of the derived Pareto model. Finally, the proposed approach chooses the best model with minimum complexity that represents the time variation behavior of the non-stationary channel data sequence. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate the capability of the proposed methodology in providing the best fit to the extremes of the non-stationary data. The proposed approach significantly outperforms the channel modeling approach using the stationary channel assumption in characterizing the extreme events. [ABSTRACT FROM AUTHOR]

In this article, the optimal operational control problem is considered for complex industrial processes with stochastic disturbances. The performance index is optimized by set points reselection on the operational control layer together with controllers design on the loop control layer. First, the operational indices are obtained through some optimization algorithms. Second, the controllers are designed in the ideal situation to ensure that the controlled variables can track desired set points. To minimize the performance deterioration caused by non-Gaussian stochastic noises or disturbances, a novel Pareto distribution estimation (Pareto DE)-based intelligent set-points reselection approach is proposed to optimize entropy and expectation simultaneously. In the proposed method, entropy is formulated in a recursive way basing on joint PDFs which are obtained through multivariate kernel density and bandwidth selection. Meanwhile, both the controller structure and controller parameters are fixed for whatever disturbances acting on the system. Finally, simulations are given to illustrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The estimation of covariance matrices is a core problem in many modern adaptive signal processing applications. For matrix- and array-valued data, e.g., MIMO communication, EEG/MEG (time versus channel), the covariance matrix of vectorized data may belong to the non-convex set of Kronecker product structure. In addition, the Kronecker factors can also exhibit an additional linear structure. Taking this prior knowledge into account during the estimation process drastically reduces the amount of unknown parameters, and then improves the estimation accuracy. On the other hand, the broad class of complex elliptically symmetric distributions, as well as the related complex angular elliptical distribution, are particularly suited to model heavy-tailed multivariate data. In this context, we first establish novel robust estimators of scatter and shape matrices (both related to a covariance matrix), having a Kronecker product structure with linearly structured Kronecker factors. Then, we conduct a theoretical analysis of their asymptotic performance (i.e., consistency, asymptotic distribution and efficiency), in matched and mismatched scenarios, i.e., when misspecifications between the true and assumed models occur. Finally, numerical results illustrate the theoretical analysis and assess the usefulness of the proposed estimators. [ABSTRACT FROM AUTHOR]

In this work, we address optimization problems where the objective function is a nonlinear function of an expected value, i.e., compositional stochastic programs. We consider the case where the decision variable is not vector-valued but instead belongs to a Reproducing Kernel Hilbert Space (RKHS), motivated by risk-aware formulations of supervised learning. We develop the first memory-efficient stochastic algorithm for this setting, which we call Compositional Online Learning with Kernels (COLK). COLK, at its core a two time-scale stochastic approximation method, addresses the facts that (i) compositions of expected value problems cannot be addressed by stochastic gradient method due to the presence of an inner expectation; and (ii) the RKHS-induced parameterization has complexity which is proportional to the iteration index which is mitigated through greedily constructed subspace projections. We provide, for the first time, a non-asymptotic tradeoff between the complexity of a function parameterization and its required convergence accuracy for both strongly convex and non-convex objectives under constant step-sizes. Experiments with risk-sensitive supervised learning demonstrate that COLK consistently converges and performs reliably even when data is full of outliers, and thus marks a step towards overfitting. Specifically, we observe a favorable tradeoff between model complexity, consistent convergence, and statistical accuracy for data associated with heavy-tailed distributions. [ABSTRACT FROM AUTHOR]

Global navigation satellite system multipath channel modeling is important for signal simulation and error mitigation in urban scenarios. However, there have not been sufficient studies on channel statistical models for dynamic land vehicles in eastern Asian cities. This paper presents both MULTIPATH and non-line-of-sight (NLOS) channel models for GPS L1CA and BDS B1I signals. They are based on an extensive field dataset collected in the Shanghai-Lujiazui area. In addition, a method is developed to estimate multipath components and NLOS signal parameters in a dynamic channel environment. It is found that the Gamma function best fits the distribution of MULTIPATH channel delays while the exponential function is better suited for the distribution of NLOS channel delays. Both MULTIPATH and NLOS channel signals follow a linear decline average power-delay trend and a zero-mean Gaussian distribution power scattering model. The Doppler fading frequency distribution for MULTIPATH and NLOS channels can be modeled by the Gaussian function except for the MULTIPATH channel signal at the low velocity range (0∼3 km/h), where an absolute exponential distribution is a better fit. The lifetime distribution for MULTIPATH and NLOS channels is represented by the generalized Pareto distribution function. These findings offer insights into the GNSS signal models in urban propagation channels and are essential to the development of accurate urban navigation systems. [ABSTRACT FROM AUTHOR]

In the reverse Stackelberg mechanism, by considering a decision function for the leader rather than a decision value in the conventional Stackelberg game, the leader can explore a wider decision space. This flexibility can result in realizing the globally optimal solution of the leader’s objective function, while controlling the reaction function of the followers, simultaneously. We consider an aggregator who purchases energy from the wholesale energy market. The aggregator acts as the leader for a group of plugged in electric vehicles (PEVs) and determines the price of energy versus consumption at each hour a day as its decision function. In the followers level, since the optimal charging strategies of the PEVs are coupled through the electricity price, the PEVs in a group are considered to cooperate in finding their Nash–Pareto-optimal charging strategy, by minimizing a social cost function. For a large number of PEVs, the cooperative cost minimization of PEVs can be modeled as a cooperative mean-field (MF) game. We propose a decentralized MF optimal control algorithm and prove that the algorithm converges to leader–follower MF $\varepsilon _{N}$ -Nash equilibrium point of the game. Furthermore, a decentralized reverse Stackelberg algorithm is implemented to achieve the optimal linear price function of the leader. Simulation results and comparison with benchmark methods are performed to demonstrate the advantages of the proposed method. Note to Practitioners—The effect of a large population of PEVs on the power grid such as overload and voltage drop is inevitable. Motivated by this, there are many research articles which propose different centralized and decentralized charging coordination solutions to address this problem. The core idea in the most of literature is: “How to IMPLEMENT a demand response (DR) program appropriately for charging coordination problem to avoid high peak load?” However, none of them propose a practical algorithm on “How to DESIGN a DR optimally by having limited information from the clients?” We propose a bilevel optimization algorithm to both design a DR program (i.e., price function) and also implement DR program to flatten the demand curve, accordingly. Another advantage of the proposed method is that the information structure of the problem is close to reality. There is no information exchange among the clients and also the utility company does not need to know the private information of the clients. The Utility Company only knows the charging profile of the PEVs in each day and broadcasts the price signal to the clients for the next day. The method is illustrated in an IEEE 5-bus system that supports our claims. [ABSTRACT FROM AUTHOR]

Although quality indicators play a crucial role in benchmarking evolutionary multiobjective optimization algorithms, their properties are still unclear. One promising approach for understanding quality indicators is the use of the optimal distribution of objective vectors that optimizes each quality indicator. However, it is difficult to obtain the optimal distribution for each quality indicator, especially, when its theoretical property is unknown. Thus, optimal distributions for most quality indicators have not been well investigated. To address these issues, first, we propose a problem formulation of finding the optimal distribution for each quality indicator on an arbitrary Pareto front. Then, we approximate the optimal distributions for nine quality indicators using the proposed problem formulation. We analyze the nine quality indicators using their approximated optimal distributions on eight types of Pareto fronts of three-objective problems. Our analysis demonstrates that uniformly distributed objective vectors over the entire Pareto front are not optimal in many cases. Each quality indicator has its own optimal distribution for each Pareto front. We also examine the consistency among the nine quality indicators. [ABSTRACT FROM AUTHOR]

This study proposes a multiobjective beamforming power control scheme to achieve the robust quality of service (QoS), the optimal signal-to-interference-plus-noise ratio (SINR) target tracking and the minimum average power consumption simultaneously in multicell multiuser MIMO (multicell MU-MIMO) wireless system with imperfect channel. The advantages of the proposed multiobjective beamforming power control optimization method are threefold. First, the proposed power control scheme can simultaneously achieve three objectives as follows: 1) the optimal $H_{\infty }$ robust SINR target tracking performance, 2) the optimal $H_{2}$ SINR target tracking performance, and 3) the minimum average power consumption in the multicell MU-MIMO wireless system. The proposed multiobjective optimization problem (MOP) is equivalently derived as a MOP by minimizing their corresponding upper bounds under the constraint of a set of linear matrix inequalities (LMIs). Second, without the requirement of statistical information and the norm-bounded condition of the channel uncertainty, the effect of inter-cell interference, intra-cell interference, channel uncertainty, round trip delay, and noise can be efficiently attenuated by the proposed robust SINR target tracking method. Third, through the proposed LMIs-constrained multiobjective evolution algorithm (MOEA), the designer can obtain Pareto optimal solutions efficiently with a good distribution along the Pareto front to achieve the proposed multiobjective power control design. Several simulation examples with the proposed multiobjective control scheme are produced with favourable results under the guarantee of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Confidence evaluation algorithms for the analysis of binary phase-shift keying (BPSK) signals are discussed. The phase of the product of the observed signal and constructed reference signal is extracted, and its spectrum is utilized to verify confidence. Two extreme statistics of the phase spectrum are developed for the confidence evaluation, which depend on goodness-of-fit tests based on the generalized extreme value distribution and generalized Pareto distribution respectively. Via extensive simulations, we verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Fragmentation of expensive resources, e.g., the spectrum for wireless services, between providers can introduce inefficiencies in resource utilization and worsen overall system performance. In such cases, resource pooling between independent service providers can be used to improve performance. However, for providers to agree to pool their resources, the arrangement has to be mutually beneficial. The traditional notion of resource pooling, which implies complete sharing, need not have this property. For example, under full pooling, one of the providers may be worse off and hence has no incentive to participate. In this paper, we propose partial resource sharing models as a generalization of full pooling, which can be configured to be beneficial to all participants. We formally define and analyze two partial sharing models between two service providers, each of which is an Erlang- $B$ loss system with the blocking probabilities as the performance measure. We show that there always exist partial sharing configurations that are beneficial to both providers, irrespective of the load and the number of circuits of each of the providers. A key result is that the Pareto frontier has at least one of the providers sharing all its resources with the other. Furthermore, full pooling may not lie inside this Pareto set. The choice of the sharing configurations within the Pareto set is formalized based on the bargaining theory. Finally, large system approximations of the blocking probabilities in the quality-efficiency-driven regime are presented. [ABSTRACT FROM AUTHOR]

We consider a parallel-server system with homogeneous servers where incoming tasks, arriving at rate $\lambda $ , are dispatched by $n$ dispatchers, each of them balancing a fraction $1/n$ of the load to $K/n$ servers. Servers are first-come-first-served (FCFS) queues and dispatchers implement size interval task assignment policy with equal load (SITA-E), a size-based policy such that the servers are equally loaded. We compare the performance of a system with $n>1$ dispatchers and a single dispatcher. We show that the performance of a system with $n$ dispatchers, $K$ servers, and arrival rate $\lambda $ coincides with that of a system with one dispatcher, $K/n$ servers, and arrival rate $\lambda /n$. We define the degradation factor as the ratio between the performance of a system with $K$ servers and arrival rate $\lambda $ and the performance of a system with $K/n$ servers and arrival rate $\lambda /n$. We establish a partial monotonicity on $n$ for the degradation factor and, therefore, the degradation factor is lower bounded by one. We then investigate the upper bound of the degradation factor for particular distributions. We consider two continuous service time distributions: uniform and bounded Pareto and a discrete distribution with two values, which is the distribution that maximizes the variance for a given mean. We show that the performance degradation is small for uniformly distributed job sizes but that for Bounded Pareto and two points distributions it can be unbounded. We have investigated the degradation using the distribution obtained from real traces. [ABSTRACT FROM AUTHOR]

Parallelism has become a recurrent tool to support computational intelligence and, particularly, evolutionary algorithms in the solution of very complex optimization problems, especially in the multiobjective case. However, the selection of parallel evolutionary designs often represents a difficult question due to the multiple variables that must be considered to attain an accurate exploitation of hardware resources, along with their influence in solution quality. This work looks into this issue by conducting a comparative performance analysis of intra-algorithm parallel multiobjective evolutionary algorithms running on shared-memory configurations. We consider different design trends including A) generational approaches based on measurements of solution quality plus diversity, B) generational approaches based on measurements of solution quality exclusively, and C) non-generational approaches. Following these trends, a total of six representative algorithms are applied to tackle a challenging bioinformatics problem as a case study, phylogenetic reconstruction. Experimentation on real-world scenarios point out the main advantages and weaknesses of each design, outlining guidelines for the selection of methods according to the characteristics of the employed hardware, evolutionary properties, and the parallelism exploitation capabilities of the evaluated approaches. [ABSTRACT FROM AUTHOR]

In this article, we target approximate computing for arithmetic circuits, focusing on the most complex and power-hungry units: hardware multipliers. Driven by the lack of a clear solution on the energy-error efficiency of existing approximate multiplication techniques, we present a new, efficient, and easily applied approximation design, as well as explore the current state-of-the-art design space. We show that the proposed approximation scheme can be equally applied at design time to enable synthesis of customized approximate multiplier circuits and at runtime to support dynamic approximation tuning scenarios. We achieve significant gains-up to 69-percent energy and 64-percent area savings with respect to accurate designs-by proposing hybrid approximation performed by two independent techniques that reduce both the depth (through perforation) and the width (through rounding) of the partial products accumulation tree. The corresponding runtime approximation solution delivers energy gains of up to 47 percent, introducing negligible area. More importantly, we show that design solutions configured through the proposed approach form the Pareto frontier of the energy-error space when considering direct quantitative comparisons with existing state of the art. [ABSTRACT FROM AUTHOR]

Radar detection in the presence of spatially correlated Pareto distributed clutter is examined. It is shown how an order-statistic-based sliding window detection process must have its threshold set in order to achieve a constant false alarm rate. A particular multivariate Pareto distribution is constructed, based upon a compound Gaussian model with inverse gamma texture, whose pairwise marginal distributions have a general correlation. A specific example is then used for performance analysis. [ABSTRACT FROM AUTHOR]

The Pareto distribution has been validated as a suitable model for X-band maritime surveillance radar clutter returns, and consequently there has been much interest in developing radar detection algorithms under such a clutter model assumption. Recent research has shown that it is possible to apply a transformation approach to adapt the traditional constant false alarm rate (CFAR) detectors, designed to operate in exponentially distributed clutter, to the Pareto setting. However, it was found that this approach resulted in the decision rule requiring a priori knowledge of the Pareto scale parameter. It is shown here that this shortcoming can be rectified by application of a complete sufficient statistic to the transformed detector. Consequently, new decision rules are derived and it is shown that they not only achieve the CFAR property but in some instances can improve the performance of the decision rules from which they are derived. [ABSTRACT FROM AUTHOR]

A dual order statistic detector is examined and shown to have the constant false alarm rate property when the clutter is modelled by a certain class of distributions. For the case of Pareto distributed clutter, mathematical analysis establishes some rules of thumb that can be used to select appropriate order statistic indices. The performance of this detector is then examined in such clutter, including an analysis of the effects of interfering targets and clutter transitions. [ABSTRACT FROM AUTHOR]

Thanks to the recent improvements in renewable energy technologies throughout the world, distributed energy sources are now playing an undeniable role in supplying the electricity in distribution networks. This paper studies the impacts of utilizing distributed generation units on the task of network reconfiguration in distribution systems. Considering the importance of reducing voltage drops and voltage sags in distribution systems, network reconfiguration is formulated as a multiobjective optimization problem in this study to minimize these two objective functions. A Pareto-based metaheuristic optimization algorithm is proposed to identify a Pareto frontier representing the alternative high-quality suboptimal configurations. The proposed optimization method is tested on a 69-bus distribution system to demonstrate the performance of the algorithm. [ABSTRACT FROM PUBLISHER]

Visualization of population in a high-dimensional objective space throughout the evolution process presents an attractive feature that could be well exploited in designing many-objective evolutionary algorithms (MaOEAs). In this paper, a new visualization method is proposed. It maps individuals from a high-dimensional objective space into a 2-D polar coordinate plot while preserving Pareto dominance relationship, retaining shape and location of the Pareto front, and maintaining distribution of individuals. From it, a decision-maker can observe the evolution process, estimate location, range, and distribution of Pareto front, assess quality of the approximated front and tradeoff between objectives, and easily select preferred solutions. Furthermore, its applications can be scalable to any dimensions, handle a large number of individuals on front, and simultaneously visualize multiple fronts for comparison. Based on this visualization tool, a performance metric, named polar-metric, is designed. The convergence of the approximate front is measured by radial values of all population members on that front. Meanwhile, the diversity performance is mainly determined by niche count of each subregion in a high-dimensional objective space. Experimental results show that it can provide a comprehensive and reliable comparison among MaOEAs. [ABSTRACT FROM PUBLISHER]

Achieving high spectrum efficiency (SE) and energy efficiency (EE) is of primary importance for the sustainability of future cellular networks, but a key challenge is on balancing the tradeoff that arises when maximizing both of these performance metrics simultaneously. This paper develops a framework for analyzing the SE and EE of a two-tier heterogeneous cellular network consisting of macrocell and femtocell base stations (BSs) operating under a shared-spectrum scenario. It is shown that both the SE and the EE can be significantly enhanced with the overlaid deployment of the femto tier. However, the performance gain achievable is found to be strongly dependent on the load level and the BS power consumption attributes. A multiobjective optimization problem that maximizes the SE and the EE subject to quality-of-service (QoS) constraints is formulated and solved to give the Pareto-optimal operational regime. The novelty of this work is the quantification of the SE–EE tradeoff as a Lebesgue measure, which is defined by the Pareto-optimal regime. The developed framework is useful for studying the impact of the load on the SE–EE tradeoff, based on a strategy that exploits the varying load conditions to achieve good balance in the SE–EE tradeoff is formulated. Numerical results show that, while the improvement achieved in minimizing the SE–EE performance gap is marginal under high-load conditions, it is feasible to significantly increase the SE and the EE during low-load conditions and satisfy the users' QoS requirements by optimally adapting the density of the femto-tier BSs accessing the shared spectrum. [ABSTRACT FROM AUTHOR]

Delivering reliable service offerings to clients remain a challenging aspect in today's cloud infrastructure. A broad number of research studies have undertaken the service evaluation process from one side; that is, the infrastructure's perspective. Conversely, clients’ assessment to the service has been mostly neglected. In this paper, we propose a client-side service evaluation approach which mainly relies on the clients’ assessment of infrastructure's service offerings. The proposed approach utilizes the strength of the Social Network Analysis (SNA) principles in conjunction with the Generalized Extreme Value Theorem (EVT) to converge to a precise Quality of Service (QoS) model. Our goal in this research is to build precise QoS models to predict the performance of clients that exhibit similar behaviors. Thus, we develop a novel SNA-based clustering algorithm that analyzes the strength of the interconnection links between clients and cluster related clients in communities of similar behaviors. The proposed approach is effective in providing Infrastructure as a Service (IaaS) providers with a better assessment tool to evaluate and improve their service offerings. The experimental results of the proposed approach on GENI's SEATTLE platform demonstrate its ability to enhance the prediction process of the performance of IaaS service offerings. [ABSTRACT FROM PUBLISHER]

It is known that the major difficulty of sea clutter simulation is the controlled generation of a continuous correlated non-Gaussian random process. In particular, for sea clutter at low grazing angles and over a long time scale, the spiky and nonstationary nature makes the simulation more difficult. This paper proposes a novel procedure for the simulation of continuous correlated low-grazing-angle sea clutter with prescribed statistic and correlation characteristics. The Pareto distribution is utilized to describe the statistics of the sea clutter intensity. In addition, different correlation characteristics for sea clutter over both short and long time scales are considered in this paper. First, the memoryless nonlinear transform is adopted to simulate the intensities of the correlated sea clutter. Second, the Doppler spectra of different range bins are generated separately. In particular, for the sea clutter over long time scales, successive time-varying Doppler spectra are modeled to characterize the nonstationarity of the sea clutter series. Then, the phases of the sea clutter are retrieved by constraining to the desired Doppler spectra and given magnitudes. Different methods are adopted for the phase retrieval of sea clutter on short and long time scales. Finally, simulation results show that the proposed procedure can generate continuous low-grazing-angle sea clutter with prescribed statistic and correlation characteristics. In particular, for the sea clutter over a long time scale, the simulated sea clutter series enjoys the time-varying Doppler spectrum while maintaining continuity. In addition, the proposed method is also suitable for other models, such as the K-distribution, the Weibull model, and so on. [ABSTRACT FROM PUBLISHER]

Accurate estimation of the position of network nodes is essential, e.g., in localization, geographic routing, and vehicular networks. Unfortunately, typical positioning techniques based on ranging or on velocity and angular measurements are inherently limited. To overcome the limitations of specific positioning techniques, the fusion of multiple and heterogeneous sensor information is an appealing strategy. In this paper, we investigate the fundamental performance of linear fusion of multiple measurements of the position of mobile nodes, and propose a new distributed recursive position estimator. The Cramér-Rao lower bounds for the parametric and a-posteriori cases are investigated. The proposed estimator combines information coming from ranging, speed, and angular measurements, which is jointly fused by a Pareto optimization problem where the mean and the variance of the localization error are simultaneously minimized. A distinguished feature of the method is that it assumes a very simple dynamical model of the mobility and therefore it is applicable to a large number of scenarios providing good performance. The main challenge is the characterization of the statistical information needed to model the Fisher information matrix and the Pareto optimization problem. The proposed analysis is validated by Monte Carlo simulations, and the performance is compared to several Kalman-based filters, commonly employed for localization and sensor fusion. Simulation results show that the proposed estimator outperforms the traditional approaches that are based on the extended Kalman filter when no assumption on the model of motion is used. In such a scenario, better performance is achieved by the proposed method, but at the price of an increased computational complexity. [ABSTRACT FROM PUBLISHER]

In evolutionary multiobjective optimization, it is very important to be able to visualize approximations of the Pareto front (called approximation sets) that are found by multiobjective evolutionary algorithms. While scatter plots can be used for visualizing 2-D and 3-D approximation sets, more advanced approaches are needed to handle four or more objectives. This paper presents a comprehensive review of the existing visualization methods used in evolutionary multiobjective optimization, showing their outcomes on two novel 4-D benchmark approximation sets. In addition, a visualization method that uses prosection (projection of a section) to visualize 4-D approximation sets is proposed. The method reproduces the shape, range, and distribution of vectors in the observed approximation sets well and can handle multiple large approximation sets while being robust and computationally inexpensive. Even more importantly, for some vectors, the visualization with prosections preserves the Pareto dominance relation and relative closeness to reference points. The method is analyzed theoretically and demonstrated on several approximation sets. [ABSTRACT FROM AUTHOR]

A statistical model for high-resolution sea clutter, which we have called the compound inverse Gaussian (CIG) distribution, is proposed. The model is a mixture of the Rayleigh distribution and the inverse Gaussian (IG) distribution to model the speckle and the texture components, respectively. The CIG probability density function (pdf) is generalized to account for the additive thermal noise to achieve a good match to real data. The overall pdf is given in an integral form as a function of three parameters which are estimated from the recorded data based on the parametric curve-fitting estimation (PCFE) method of the complementary cumulative distributed function (CCDF). The Nelder-Mead (N-M) simplex algorithm is used to provide the best estimates of the pdf parameters. Using the IPIX backscatter database, the fitting of the CIG pdfs and the cumulative distributed functions (cdfs) are assessed and compared with the fitted Weibull, log-normal, Rician inverse Gaussian (RiIG), K plus noise, compound log-normal (CLN), and Pareto plus noise distributions. Experimental fitting results show that the sea-clutter amplitudes obey the proposed CIG model in most cases. [ABSTRACT FROM AUTHOR]

We consider coherent and mixed systems with exchangeable components whose lifetimes have positive and finite variances. We present sharp lower and upper bounds on the variance of the system lifetime, expressed in terms of the system signature and the variance of a single component. The bounds are attained for the power and Pareto distributions of the component lifetimes. [ABSTRACT FROM AUTHOR]

The problem of parameter estimation from large noisy data is considered. If the observation size N is large, the calculation of efficient estimators is computationally expensive. Further, memory can be a limiting factor in technical systems where data is stored for later processing. Here we follow the idea of reducing the size of the observation by projecting the data onto a subspace of smaller dimension M ≪ N, but with the highest possible informative value regarding the estimation problem. Under the assumption that a prior distribution of the parameter is available and the output size is fixed to M, we derive a characterization of the Pareto-optimal set of linear transformations by using a weighted form of the Bayesian Cramér-Rao lower bound (BCRLB) which stands in relation to the expected value of the Fisher information measure. Satellite-based positioning is discussed as a possible application. Here N must be chosen large in order to compensate for low signal-to-noise ratios (SNR). For different values of M, we visualize the information-loss and show by simulation of the MAP estimator the potential accuracy when operating on the reduced data. [ABSTRACT FROM AUTHOR]

Many of the burst assembly algorithms employed in optical burst switching (OBS) networks preserve the IP traffic self-similarity property in the burst traffic. We introduce a mathematical model for performance evaluation of an OBS core node employing either no, a partial or a full wavelength conversion strategy. The model assumes long-range dependent (LRD) traffic arrivals to the OBS intermediate node whose inter-arrival times are accurately modeled by a Pareto distribution, whereas exponential holding times are assumed. In our proposed model, each output port in the node is modeled as a GI/M/w/w queue with partial server accessibility. An imbedded Markov chain approach is used to derive the limiting state probability distribution for the number of bursts currently served by an output port as seen by arriving bursts. Next, the average burst loss probability is evaluated from steady-state occupancy probabilities. In addition, the results of our mathematical model are validated via simulation. Furthermore, the results of the model are compared with those when assuming short-range dependent Poisson arrivals. Comparison shows that traditional Poisson traffic models yield over-optimistic performance measures compared to the LRD Pareto traffic models, especially for light traffic scenarios. Furthermore, we study the impact of varying different traffic parameters, such as the average arrival rate and the Hurst parameter, on the burst loss probability. Finally, the impact of varying the wavelength conversion capability on the burst loss probability is studied, where we compare two strategies for contention resolution: adding new channels (wavelengths) or adding wavelength converters, while taking the cost into consideration. [ABSTRACT FROM PUBLISHER]

We express the joint entropy of progressively Type-II censored order statistics in terms of an incomplete integral of the hazard function, and use it to develop a simple estimate of the joint entropy of progressively Type-II censored data, considered earlier by Balakrishnan , IEEE Trans. Reliability, vol. 56, pp. 349–356. We then construct a goodness-of-fit test statistic based on the Kullback-Leibler information for Pareto, log-normal, and Weibull distributions by using maximum likelihood estimates and approximate maximum likelihood estimates of the model parameters. Finally, we use Monte Carlo simulations to evaluate the power of the proposed test for several alternatives under different sample sizes and progressive censoring schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider a series system with two s-independent components, each of which has a Pareto distributed lifetime. We assume the lifetime data from the system are masked, and the masking probability is dependent. Noninformative prior, and conjugate prior are considered; and Gibbs sampling method is used to obtain the estimators of the unknown parameters, the reliability function, and the hazard function. A numeric example demonstrates how to apply the theoretical results. [ABSTRACT FROM AUTHOR]

Modeling user preference is one of the challenging issues in intelligent information systems. Extensive research has been performed to automatically analyze user preference and to utilize it. One problem still remains: The representation of preference, usually given by measure of vector similarity or probability, does not always correspond to common sense of preference. This problem gets worse in the case of negative preference. To overcome this problem, this paper presents a preference model using mutual information in a statistical framework. This paper also presents a method that combines information of joint features and alleviates problems arising from sparse data. Experimental results, compared with the previous recommendation models, show that the proposed model has the highest accuracy in recommendation tests. [ABSTRACT FROM AUTHOR]

This paper defines a narrow sense (NS) Pareto optimality and shows that the NS Pareto optimality is equivalent to a Nash equilibrium for electricity producers. It claims that a vector optimization is a generalized multi-objective optimization, which can be solved using the Kuhn-Tucker vector optimization theorem (KTVOT). The paper establishes a mathematical relationship between the Pareto optimality, the Nash-Cournot gaming, and KTVOT, based on a gaming model for deregulated electricity markets. [ABSTRACT FROM PUBLISHER]