average rate of downlink heterogeneous cellular networks over generalized fading channels a stochastic geometry approach in this paper we introduce an analytical framework to compute the average rate of downlink heterogeneous cellular networks the framework leverages recent application of stochastic geometry to other cell interference modeling and analysis the heterogeneous cellular network is modeled as the superposition of many tiers of base stations bss having different transmit power density path loss exponent fading parameters and distribution and unequal biasing for flexible tier association a long term averaged maximum biased received power tier association is considered the positions of the bss in each tier are modeled as points of an independent poisson point process ppp under these assumptions we introduce a new analytical methodology to evaluate the average rate which avoids the computation of the coverage probability pcov and needs only the moment generating function mgf of the aggregate interference at the probe mobile terminal the distinguishable characteristic of our analytical methodology consists in providing a tractable and numerically efficient framework that is applicable to general fading distributions including composite fading channels with small and mid scale fluctuations in addition our method can efficiently handle correlated log normal shadowing with little increase of the computational complexity the proposed mgf based approach needs the computation of either a single or a two fold numerical integral thus reducing the complexity of pcov based frameworks which require for general fading distributions the computation of a four fold integral

on a generalization of mittag leffler function and its properties abstract let s and z be complex variables s the gamma function and s s s for any complex the generalized pochhammer symbol the principal aim of the paper is to investigate the function e q z n 0 q n n z n n where c re 0 re 0 re 0 and q 0 1 n this is a generalization of the exponential function exp z the confluent hypergeometric function z the mittag leffler function e z the wiman s function e z and the function e z defined by prabhakar for the function e q z its various properties including usual differentiation and integration laplace transforms euler beta transforms mellin transforms whittaker transforms generalised hypergeometric series form mellin barnes integral representation with their several special cases are obtained and its relationship with laguerre polynomials fox h function and wright hypergeometric function is also established

model based online learning with kernels new optimization models and algorithms for online learning with kernels olk in classification regression and novelty detection are proposed in a reproducing kernel hilbert space unlike the stochastic gradient descent algorithm called the naive online reg minimization algorithm norma olk algorithms are obtained by solving a constrained optimization problem based on the proposed models by exploiting the techniques of the lagrange dual problem like vapnik s support vector machine svm the solution of the optimization problem can be obtained iteratively and the iteration process is similar to that of the norma this further strengthens the foundation of olk and enriches the research area of svm we also apply the obtained olk algorithms to problems in classification regression and novelty detection including real time background substraction to show their effectiveness it is illustrated that based on the experimental results of both classification and regression the accuracy of olk algorithms is comparable with traditional svm based algorithms such as svm and least square svm ls svm and with the state ofthe art algorithms such as kernel recursive least square krls method and projectron method while it is slightly higher than that of norma on the other hand the computational cost of the olk algorithm is comparable with or slightly lower than existing online methods such as above mentioned norma krls and projectron methods but much lower than that of svm based algorithms in addition different from svm and ls svm it is possible for olk algorithms to be applied to non stationary problems also the applicability of olk in novelty detection is illustrated by simulation results

an information theoretic approach of designing sparse kernel adaptive filters this paper discusses an information theoretic approach of designing sparse kernel adaptive filters to determine useful data to be learned and remove redundant ones a subjective information measure called surprise is introduced surprise captures the amount of information a datum contains which is transferable to a learning system based on this concept we propose a systematic sparsification scheme which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters nonlinear regression short term chaotic time series prediction and long term time series forecasting examples are presented

learning nonlinear generative models of time series with a kalman filter in rkhs this paper presents a novel generative model for time series based on the kalman filter algorithm in a reproducing kernel hilbert space rkhs using the conditional embedding operator the end result is a nonlinear model that quantifies the hidden state uncertainty and propagates its probability distribution forward as in the kalman algorithm the embedded dynamics can be described by the estimated conditional embedding operator constructed directly from the training measurement data using this operator as the counterpart of the state transition matrix we reformulate the kalman filter algorithm in rkhs for the state model the hidden states are the estimated embeddings of the measurement distribution while the measurement model serves to connect the estimated measurement embeddings with the current mapped measurements in the rkhs this novel algorithm is applied to noisy time series estimation and prediction and simulation results show that it outperforms other existing algorithms in addition improvements are proposed to reduce the size of the operator and reduce the computation complexity

unified framework for the effective rate analysis of wireless communication systems over miso fading channels this paper proposes a unified framework for the effective rate analysis over arbitrary correlated and not necessarily identical multiple input single output miso fading channels which uses the moment generating function mgf based approach and h transform representation the proposed framework has the potential to simplify the cumbersome analysis procedure compared with the probability density function based approach moreover the effective rates over two specific fading scenarios are investigated namely independent but not necessarily identical distributed i n i d miso hyper fox s h fading channels and arbitrary correlated generalized k fading channels the exact analytical representations for these two scenarios are also presented by substituting corresponding parameters the effective rates in various practical fading scenarios such as rayleigh nakagami m weibull gamma and generalized k fading channels are readily available in addition asymptotic approximations are provided for the proposed h transform and mgf based approach as well as for the effective rate over i n i d miso hyper fox s h fading channels simulations under various fading scenarios are also presented which support the validity of the proposed method

kernel recursive least squares tracker for time varying regression in this paper we introduce a kernel recursive least squares krls algorithm that is able to track nonlinear time varying relationships in data to this purpose we first derive the standard krls equations from a bayesian perspective including a sensible approach to pruning and then take advantage of this framework to incorporate forgetting in a consistent way thus enabling the algorithm to perform tracking in nonstationary scenarios the resulting method is the first kernel adaptive filtering algorithm that includes a forgetting factor in a principled and numerically stable manner in addition to its tracking ability it has a number of appealing properties it is online requires a fixed amount of memory and computation per time step incorporates regularization in a natural manner and provides confidence intervals along with each prediction we include experimental results that support the theory as well as illustrate the efficiency of the proposed algorithm

gaussian class multivariate weibull distributions theory and applications in fading channels ascertaining on the suitability of the weibull distribution to model fading channels a theoretical framework for a class of multivariate weibull distributions originated from gaussian random processes is introduced and analyzed novel analytical expressions for the joint probability density function pdf moment generating function mgf and cumulative distribution function cdf are derived for the bivariate distribution of this class with not necessarily identical fading parameters and average powers two specific distributions with arbitrary number of correlated variates are considered and studied with exponential and with constant correlation where their pdfs are introduced both cases assume equal average fading powers but not necessarily identical fading parameters for the multivariate weibull distribution with exponential correlation useful corresponding formulas as for the bivariate case are derived the presented theoretical results are applied to analyze the performance of several diversity receivers employed with selection equal gain and maximal ratio combining mrc techniques operating over correlated weibull fading channels for these diversity receivers several useful performance criteria such as the moments of the output signal to noise ratio snr including average output snr and amount of fading and outage probability are analytically derived moreover the average symbol error probability for several coherent and noncoherent modulation schemes is studied using the mgf approach the proposed mathematical analysis is complemented by various evaluation results showing the effects of the fading severity as well as the fading correlation on the diversity receivers performance

on the efficient simulation of the distribution of the sum of gamma gamma variates with application to the outage probability evaluation over fading channels the gamma gamma distribution has recently emerged in a number of applications ranging from modeling scattering and reverberation in sonar and radar systems to modeling atmospheric turbulence in wireless optical channels in this respect assessing the outage probability achieved by some diversity techniques over this kind of channels is of major practical importance in many circumstances this is related to the difficult question of analyzing the statistics of a sum of gamma gamma random variables answering this question is not a simple matter this is essentially because outage probabilities encountered in practice are often very small and hence the use of classical monte carlo methods is not a reasonable choice this lies behind the main motivation of this paper in particular this paper proposes a new approach to estimate the left tail of the sum of gamma gamma variates more specifically we propose robust importance sampling schemes that efficiently evaluates the outage probability of diversity receivers over gamma gamma fading channels the proposed estimators satisfy the well known bounded relative error criterion for both maximum ratio combining and equal gain combining cases we show the accuracy and the efficiency of our approach compared with naive monte carlo via some selected numerical simulations

capacity of mrc on correlated rician fading channels a new exact explicit expression is derived for the ergodic capacity of maximal ratio combining mrc schemes over arbitrarily correlated rician fading channels this is used to study the effects of channel correlation on the ergodic capacity numerical results reveal that both the phase and the magnitude of correlation have an impact on the ergodic capacity of rician fading channels this is in contrast to correlated rayleigh fading where the phase of the correlation has no effect on the ergodic capacity it is also observed that negatively correlated branches in rician fading may lead to an increase in ergodic capacity beyond that obtained by uncorrelated branches

on the calculation of the incomplete mgf with applications to wireless communications the incomplete moment generating function imgf has paramount relevance in communication theory since it appears in a plethora of scenarios when analyzing the performance of communication systems we here present a general method for calculating the imgf of any arbitrary fading distribution then we provide exact closed form expressions for the imgf of the very general kappa mu shadowed fading model which includes the popular kappa mu eta mu rician shadowed and other classical models as particular cases we illustrate the practical applicability of this result by analyzing several scenarios of interest in wireless communications 1 physical layer security in the presence of an eavesdropper 2 outage probability analysis with interference and background noise 3 channel capacity with side information at the transmitter and the receiver and 4 average bit error rate with adaptive modulation when the fading on the desired link can be modeled by any of the aforementioned distributions

a class of multivanate failure time distributions summary a class of continuous multivariate lifetime distributions is proposed the dependence between individuals in a group is modelled by a group specific quantity which can be interpreted as an unobserved covariate common to the individuals in the group and assumed to follow a positive stable distribution it is possible to include covariates in the model and discuss whether the dependence is still present after specific covariates are taken into account if the conditional hazards are proportional then the hazards in the marginal distributions are also proportional but with different constants of proportionality also the hazard for the minimum in a group is proportional to the marginal hazards if the conditional distributions given the group quantity are weibull then the marginal distributions are also weibull this class can be used to test the hypothesis of independence of litter mates in the proportional hazards model

quantized kernel least mean square algorithm in this paper we propose a quantization approach as an alternative of sparsification to curb the growth of the radial basis function structure in kernel adaptive filtering the basic idea behind this method is to quantize and hence compress the input or feature space different from sparsification the new approach uses the redundant data to update the coefficient of the closest center in particular a quantized kernel least mean square qklms algorithm is developed which is based on a simple online vector quantization method the analytical study of the mean square convergence has been carried out the energy conservation relation for qklms is established and on this basis we arrive at a sufficient condition for mean square convergence and a lower and upper bound on the theoretical value of the steady state excess mean square error static function estimation and short term chaotic time series prediction examples are presented to demonstrate the excellent performance

exact sinr statistics in the presence of heterogeneous interferers we derive new results for the higher order moments of signal to interference plus noise ratio sinr in the presence of an arbitrary poisson point process ppp based heterogeneous interference field the analysis leverages on a moment generating function mgf methodology which only requires the statistics of intended signal and aggregate interference thus eliminating the need for the exact distribution of sinr we extend the existing results on interference statistics by deriving a generalized closed form expression of the interference mgf considering nakagami m fading channels with exclusion region in certain special cases explicit expressions for the averages of different functions of sinr are found which also lead to closed form solutions for the probability distributions of aggregate interference reciprocal and signal to interference ratio we prove that in such cases the effect of total ppp based interference power on useful transmission is mathematically equivalent to the severe fluctuations from a one sided gaussian fading channel as an application example the proposed methodology is used together with stochastic geometry theory to characterize the average sinr and rate in heterogeneous cellular networks the validity of our analytical derivations is confirmed via monte carlo simulations for various system settings we show that with cellular network densification there exists a tradeoff between the average sinr and rate performance

performance analysis of dual selection diversity in correlated weibull fading channels ascertaining the importance of the dual selection combining sc receivers and the suitability of the weibull model to describe mobile fading channels we study the performance of a dual sc receiver over correlated weibull fading channels with arbitrary parameters exact closed form expressions are derived for the probability density function the cumulative distribution function and the moments of the output signal to noise ratio snr important performance criteria such as average output snr amount of fading outage probability and average bit error probability for several modulation schemes are studied furthermore for these performance criteria novel closed form analytical expressions are derived the proposed analysis is complemented by various performance evaluation results including the effects of the input snr s unbalancing fading severity and fading correlation on the overall system s performance computer simulation results have verified the validity and accuracy of the proposed analysis

kernel based least squares temporal difference with gradient correction a least squares temporal difference with gradient correction ls tdc algorithm and its kernel based version kernel based ls tdc kls tdc are proposed as policy evaluation algorithms for reinforcement learning rl ls tdc is derived from the tdc algorithm attributed to tdc derived by minimizing the mean square projected bellman error ls tdc has better convergence performance the least squares technique is used to omit the size step tuning of the original tdc and enhance robustness for kls tdc since the kernel method is used feature vectors can be selected automatically the approximate linear dependence analysis is performed to realize kernel sparsification in addition a policy iteration strategy motivated by kls tdc is constructed to solve control learning problems the convergence and parameter sensitivities of both ls tdc and kls tdc are tested through on policy learning off policy learning and control learning problems experimental results as compared with a series of corresponding rl algorithms demonstrate that both ls tdc and kls tdc have better approximation and convergence performance higher efficiency for sample usage smaller burden of parameter tuning and less sensitivity to parameters

fully bayesian analysis of arma time series models abstract statistical analysis of autoregressive moving average arma models is an important non standard problem no classical approach is widely accepted legitimacy for most classical approaches is based solely on asymptotic grounds while small sample sizes are common the only obstacle to the bayesian approach are designing a structure through which prior information can be incorporated and designing a practical computational method the objective of this work is to overcome these two obstacles in addition to the standard results the bayesian approach gives a different method of determining the order of the arma model that is p q

symbol error rate of mpsk over egk channels perturbed by a dominant additive laplacian noise the laplacian noise has received much attention during the recent years since it affects many communication systems we consider in this paper the probability of error of an m ary phase shift keying psk constellation operating over a generalized fading channel in presence of a dominant additive laplacian noise in this context the decision regions of the receiver are determined using the maximum likelihood and the minimum distance detectors once the decision regions are extracted the resulting symbol error rate expressions are computed and averaged over an extended generalized k fading distribution generic closed form expressions of the conditional and the average probability of error are obtained in terms of the fox s h function simplifications for some special cases of fading are presented and the resulting formulas end up being often expressed in terms of well known elementary functions finally the mathematical formalism is validated using some selected analytical based numerical results as well as monte carlo simulation based results

bayesian analysis of long memory and persistence using arfima models abstract this paper provides a bayesian analysis of autoregressive fractionally integrated moving average arfima models we discuss in detail inference on impulse responsens and show how bayesian methods can be used to i test arfima models against arima alternatives and ii take model uncertainty into account when making inferences on quantities of interest our methods are then used to investigate the persistence properties of real u s gnp

online selective kernel based temporal difference learning in this paper an online selective kernel based temporal difference osktd learning algorithm is proposed to deal with large scale and or continuous reinforcement learning problems osktd includes two online procedures online sparsification and parameter updating for the selective kernel based value function a new sparsification method i e a kernel distance based online sparsification method is proposed based on selective ensemble learning which is computationally less complex compared with other sparsification methods with the proposed sparsification method the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary in addition based on local validity a selective kernel based value function is proposed to select the best samples from the sample dictionary for the selective kernel based value function approximator the parameters of the selective kernel based value function are iteratively updated by using the temporal difference td learning algorithm combined with the gradient descent technique the complexity of the online sparsification procedure in the osktd algorithm is o n in addition two typical experiments maze and mountain car are used to compare with both traditional and up to date o n algorithms gtd gtd2 and tdc using the kernel based value function and the results demonstrate the effectiveness of our proposed algorithm in the maze problem osktd converges to an optimal policy and converges faster than both traditional and up to date algorithms in the mountain car problem osktd converges requires less computation time compared with other sparsification methods gets a better local optima than the traditional algorithms and converges much faster than the up to date algorithms in addition osktd can reach a competitive ultimate optima compared with the up to date algorithms

quantized kernel recursive least squares algorithm in a recent paper we developed a novel quantized kernel least mean square algorithm in which the input space is quantized partitioned into smaller regions and the network size is upper bounded by the quantization codebook size number of the regions in this paper we propose the quantized kernel least squares regression and derive the optimal solution by incorporating a simple online vector quantization method we derive a recursive algorithm to update the solution namely the quantized kernel recursive least squares algorithm the good performance of the new algorithm is demonstrated by monte carlo simulations

kernel based least squares policy iteration for reinforcement learning in this paper we present a kernel based least squares policy iteration klspi algorithm for reinforcement learning rl in large or continuous state spaces which can be used to realize adaptive feedback control of uncertain dynamic systems by using klspi near optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants in klspi mercer kernels are used in the policy evaluation of a policy iteration process where a new kernel based least squares temporal difference algorithm called klstd q is proposed for efficient policy evaluation to keep the sparsity and improve the generalization ability of klstd q solutions a kernel sparsification procedure based on approximate linear dependency ald is performed compared to the previous works on approximate rl methods klspi makes two progresses to eliminate the main difficulties of existing results one is the better convergence and near optimality guarantee by using the klstd q algorithm for policy evaluation with high precision the other is the automatic feature selection using the ald based kernel sparsification therefore the klspi algorithm provides a general rl method with generalization performance and convergence guarantee for large scale markov decision problems mdps experimental results on a typical rl task for a stochastic chain problem demonstrate that klspi can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration lspi algorithm furthermore the klspi method was also evaluated on two nonlinear feedback control problems including a ship heading control problem and the swing up control of a double link underactuated pendulum called acrobot simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems it is also demonstrated that klspi can be applied to online learning control by incorporating an initial controller to ensure online performance

a durbin levinson regularized estimator of high dimensional autocovariance matrices summarythe autocovariance matrix of a stationary random process plays a central role in prediction theory and time series analysis when the dimension of the matrix is of the same order of magnitude as the number of observations the sample autocovariance matrix gives an inconsistent estimator in the nonparametric framework recent proposals have concentrated on banding and tapering the sample autocovariance matrix we introduce an alternative approach via a modified durbin levinson algorithm that receives as input the banded and tapered sample partial autocorrelations and returns a consistent and positive definite estimator of the autocovariance matrix we establish the convergence rate of our estimator and characterize the properties of the optimal linear predictor obtained from it the computational complexity of the latter is of the order of the square of the banding parameter which renders our method scalable for high dimensional time series

effective capacity of l_p norm diversity receivers over generalized fading channels under adaptive transmission schemes this paper presents novel moment generating function mgf and characteristic function chf based frameworks for the ec performance analysis of a generic l p norm diversity combining scheme under adaptive transmission policies the considered system operates over generalized fading channels a maximum delay constraint and under various channel state information csi conditions depending upon the operational csi four policies are studied namely i constant power with optimal rate adaptation ora ii optimal power and rate adaptation opra iii channel inversion with fixed rate cifr and iv truncated cifr tifr the l p norm diversity is a generic diversity structure which includes as special cases various well known diversity schemes such as equal gain combining egc maximal ratio combining mrc and amplify and forward af relaying under the ora and opra policies we derive single integral expressions for evaluating the ec of l p norm diversity reception directly from the mgf or the incomplete mgf of the signal to noise ratio snr at the receiver respectively for the ec performance evaluation of the egc and af relaying systems operating under the opra policy a chf based approach which is computationally more efficient as compared to the mgf based approach is further presented it is shown that the ec for the cifr and tifr policies can be directly evaluated from the mgf or the chf of the snr at the receiver respectively for the ora policy a novel analytical approach for the asymptotic ec performance analysis is also developed and evaluated revealing how important system operation parameters affect the overall system performance the mathematical formalism is validated with selected numerical and equivalent simulation performance evaluation results thus confirming the correctness of the proposed analytical methodology

multiple multiindex mittag leffler functions and relations to generalized fractional calculus the classical mittag leer m l functions have already proved their eciency as solutions of fractional order differential and integral equations and thus have become important elements of the fractional calculus theory and applications in this paper we introduce analogues of these functions depending on two sets of multiple m tuple m 2i s an integer indices the hint for this comes from a paper by dzrbashjan izv an arm ssr 13 3 1960 21 63 related to the case m 2 we study the basic properties and the relations of the multiindex m l functions with the operators of the generalized fractional calculus corresponding generalized operators of integration and dierention of the so called gelfond leontiev type as well as borel laplace type integral transforms are also introduced and studied c 2000 elsevier science b v all rights reserved msc 26a33 33c60 33e30 44a15 44a30

an analysis of temporal difference learning with function approximation we discuss the temporal difference learning algorithm as applied to approximating the cost to go function of an infinite horizon discounted markov chain the algorithm we analyze updates parameters of a linear function approximator online during a single endless trajectory of an irreducible aperiodic markov chain with a finite or infinite state space we present a proof of convergence with probability one a characterization of the limit of convergence and a bound on the resulting approximation error furthermore our analysis is based on a new line of reasoning that provides new intuition about the dynamics of temporal difference learning in addition to proving new and stronger positive results than those previously available we identify the significance of online updating and potential hazards associated with the use of nonlinear function approximators first we prove that divergence may occur when updates are not based on trajectories of the markov chain this fact reconciles positive and negative results that have been discussed in the literature regarding the soundness of temporal difference learning second we present an example illustrating the possibility of divergence when temporal difference learning is used in the presence of a nonlinear function approximator

positive dependence of a class of multivariate exponential distributions the positive dependence of a subclass of multivariate exponential distributions is examined this class is characterized by an index vector k and a parameter vector lambda which are used as an ordering to yield degrees of positive dependence the results presented have a direct implication on the reliability function of a system and the survival probability function of a shock model and consequently on the optimal assembly of systems

a unified mgf based capacity analysis of diversity combiners over generalized fading channels unified exact ergodic capacity results for l branch coherent diversity combiners including equal gain combining egc and maximal ratio combining mrc are not known this paper develops a novel generic framework for the capacity analysis of l branch egc mrc over generalized fading channels the framework is used to derive new results for the gamma shadowed generalized nakagami m fading model which can be a suitable model for the fading environments encountered by high frequency 60 ghz and above communications the mathematical formalism is illustrated with some selected numerical and simulation results confirming the correctness of our newly proposed framework

product of two envelopes taken from alpha mu kappa mu and eta mu distributions this paper presents exact novel formulations for the probability density function and cumulative distribution function for the product of two independent and non identically distributed alpha mu kappa mu and eta mu variates the expressions are given in terms of both 1 generalized fox h function and 2 easily computable series expansions the formulations derived can be directly used to explore the performance of a number of wireless communication processes including multihop systems cascaded channels radar communications multiple input multiple output links and others due to the high flexibility of the above mentioned distributions the results presented here comprise a substantial number of useful product distributions as application examples performance metrics for the cascaded fading channel are derived the validity of the expressions is confirmed via monte carlo simulation it is noteworthy that because any composite multipath shadowing fading model is obtained as a particular case of the product of two fading variables the results given here provide a plethora of composite multipath shadowing fading scenarios

n ast nakagami a novel stochastic model for cascaded fading channels a generic and novel distribution referred to as nakagami constructed as the product of n statistically independent but not necessarily identically distributed nakagami m random variables rvs is introduced and analyzed the proposed distribution turns out to be a very convenient tool for modelling cascaded nakagami m fading channels and analyzing the performance of digital communications systems operating over such channels the moments generating probability density cumulative distribution and moments functions of the n nakagami distribution are developed in closed form using the meijer s g function using these formulas generic closed form expressions for the outage probability amount of fading and average error probabilities for several binary and multilevel modulation signals of digital communication systems operating over the n nakagami fading and the additive white gaussian noise channel are presented complementary numerical and computer simulation performance evaluation results verify the correctness of the proposed formulation the suitability of the n nakagami fading distribution to approximate the lognormal distribution is also being investigated using kolmogorov smirnov tests the rate of convergence of the central limit theorem as pertaining to the multiplication of nakagami m rvs is quantified

a novel unified expression for the capacity and bit error probability of wireless communication systems over generalized fading channels analysis of the average binary error probabilities abep and average capacity ac of wireless communications systems over generalized fading channels have been considered separately in past years this paper introduces a novel moment generating function mgf based unified expression for the abep and ac of single and multiple link communications with maximal ratio combining in addition this paper proposes the hyper fox s h fading model as a unified fading distribution of a majority of the well known generalized fading environments as such the authors offer a generic unified performance expression that can be easily calculated and that is applicable to a wide variety of fading scenarios the mathematical formulism is illustrated with some selected numerical examples that validate the correctness of the authors newly derived results

statistical analysis and characterization of the indoor propagation channel this paper presents statistical analysis of data obtained by measuring narrowband path loss at dect digital enhanced cordless telecommunications frequency 1 89 ghz in an indoor environment specific goodness of fit tests are applied to the data the tests assess whether the data generating source belongs to a known family of random variables results obtained support that local path loss distribution can be represented as weibull or nakagami in most environments the close resemblance between such distributions and rice distribution with rayleigh as a special case confirms that rice rayleigh description of fading can be applied to indoor environments that similarity leads to a general simple approximate expression for the cumulative distribution function of the signal to interference ratio the expression can be used without limitations to the number and the parameters of the rice interferers

a note on enforcing stationarity in autoregressive moving average models summary a simple reparameterization is given that can implicitly restrict the autoregressivemoving average parameters to the stationary and invertible region

least squares policy iteration we propose a new approach to reinforcement learning for control problems which combines value function approximation with linear architectures and approximate policy iteration this new approach is motivated by the least squares temporal difference learning algorithm lstd for prediction problems which is known for its efficient use of sample experiences compared to pure temporal difference algorithms heretofore lstd has not had a straightforward application to control problems mainly because lstd learns the state value function of a fixed policy which cannot be used for action selection and control without a model of the underlying process our new algorithm least squares policy iteration lspi learns the state action value function which allows for action selection without a model and for incremental policy improvement within a policy iteration framework lspi is a model free off policy method which can use efficiently and reuse in each iteration sample experiences collected in any manner by separating the sample collection method the choice of the linear approximation architecture and the solution method lspi allows for focused attention on the distinct elements that contribute to practical reinforcement learning lspi is tested on the simple task of balancing an inverted pendulum and the harder task of balancing and riding a bicycle to a target location in both cases lspi learns to control the pendulum or the bicycle by merely observing a relatively small number of trials where actions are selected randomly lspi is also compared against q learning both with and without experience replay using the same value function architecture while lspi achieves good performance fairly consistently on the difficult bicycle task q learning variants were rarely able to balance for more than a small fraction of the time needed to reach the target location

sparse on line gaussian processes we develop an approach for sparse representations of gaussian process gp models which are bayesian types of kernel machines in order to overcome their limitations for large data sets the method is based on a combination of a bayesian on line algorithm together with a sequential construction of a relevant subsample of the data that fully specifies the prediction of the gp model by using an appealing parameterization and projection techniques in a reproducing kernel hilbert space recursions for the effective parameters and a sparse gaussian approximation of the posterior process are obtained this allows for both a propagation of predictions and bayesian error measures the significance and robustness of our approach are demonstrated on a variety of experiments

ber performance of dual predetection egc in correlative nakagami m fading in this letter an approach to the evaluation of the error performance in dual predetection equal gain combining egc systems over correlated nakagami m fading channels is presented deriving an infinite series representation for the characteristic function of the sum of two correlated nakagami m variables a closed form formula is extracted for binary phase shift keying and coherent binary frequency shift keying while several other modulation schemes are studied capitalizing on a parsevals s theorem based approach previously published numerical results and simulations are also presented to illustrate the proposed mathematical analysis and to point out the effect of the input signal to noise ratio unbalancing the fading severity and the fading correlation on the system s error performance

new representations for the gaussian class multivariate weibull distribution with constant correlation and applications novel single integral representations for the multivariate probability density functions pdfs and cumulative distribution functions cdfs of the gaussian class weibull distribution are derived the solutions are expressed in terms of familiar mathematical functions which are available in common mathematical software the well known equal constant correlation model is considered a special linear transformation of independent gaussian random variables is used to generate correlated weibull random variables the advantage of the new representation is that only a single integral computation is needed to compute a n dimensional distribution the new representation of the cdf is used for the performance evaluation of a selection diversity combiner operating in equally correlated weibull fading channels the new pdf representation is also used for an analysis of the moments of the output signal to noise ratio of an equal gain diversity combiner operating in equally correlated weibull fading channels