estimation and control of systems with unknown covariance and multiplicative noise the problem of estimation and control for systems with multiplicative noise and unknown second order statistics is considered conditions are found for the existence of a solution based on game theoretic ideas the conditions for the existence of a saddle point for the time invariant filtering problem are necessary and sufficient whereas for all other cases only necessary the central idea of the solution is to convert the stochastic problem to a deterministic optimal control problem whose minimax point is sought with respect to the control filter and unknown statistics parameters the results that are derived show that the problem of estimation for systems with unknown covariances depends on the costate matrix which in turn is a function of the performance measure thus the filter loses one of its best known properties that of independence of the performance functional this property holds not only for the classical kalman filter but also for multiplicative systems

the optimal projection equations for reduced order state estimation the singular measurement noise case the optimal projection equations for reduced order state estimation are generalized to allow for singular i e colored measurement noise the noisy and noise free measurements serve as inputs to dynamic and static estimators respectively the optimal solution is characterized by necessary conditions which involve a pair of oblique projections corresponding to reduced estimator order and singular measurement noise intensity

influences of large height differences and overhangs on the dynamic scaling behavior of discrete models abstract in order to investigate the influences of large height differences and overhangs on the dynamic scaling behavior of discrete models meanwhile reducing the finite size effects the etching model is modified to reduce large height differences and the overhangs in ballistic deposition surfaces are removed under certain principles numerical simulations are carried out for the modified models and the results show that the modified surfaces lead to good dynamic scaling behavior even on small system length scales the values of the dynamic scaling exponents are in excellent agreement with theoretical predictions of the kardar parisi zhang equation in 1 1 dimensions

a random rule model of surface growth abstract stochastic models of surface growth are usually based on randomly choosing a substrate site to perform iterative steps as in the etching model mello et al 2001 5 in this paper i modify the etching model to perform sequential instead of random substrate scan the randomicity is introduced not in the site selection but in the choice of the rule to be followed in each site the change positively affects the study of dynamic and asymptotic properties by reducing the finite size effect and the short time anomaly and by increasing the saturation time it also has computational benefits better use of the cache memory and the possibility of parallel implementation

block transmission over dispersive channels transmit filter optimization and realization and mmse dfe receiver performance optimal transmit filters for packet based data transmission on dispersive gaussian noise linear time invariant channels are derived by maximizing the mutual information subject to a fixed input power budget a quasi stationary approximation to the optimal nonstationary input covariance process is derived and shown to exhibit negligible mutual information loss from the optimal case for situations of most practical interest moreover this quasi stationary approximation results in efficiently computed lattice or pole zero implementations of the transmit filter by considering the popular finite impulse response minimum mean square error decision feedback equalizer fir mmse dfe as a receiver structure we show that transmitter optimization results in an appreciable improvement in the decision point signal to noise ratio finally we show that as the output blocklength becomes infinite the optimum finite dimensional nonstationary input covariance process converges to a stationary process whose power spectrum obeys the well known water pouring distribution

adaptive noncoherent dfe for mdpsk signals transmitted over isi channels a novel noncoherent decision feedback equalization ndfe scheme for m ary differential phase shift keying signals transmitted over intersymbol interference channels is presented a suboptimum version with lower computational complexity and a noncoherent linear equalizer nle are derived from the original ndfe scheme furthermore the relation of the novel nle to a previously proposed nle is investigated in contrast to known ndfe schemes the novel scheme can approach the performance of coherent minimum mean squared error decision feedback equalization for adaptation of the feedforward and feedback filters efficient novel modified least mean square and recursive least squares algorithms are presented finally it is shown that the proposed adaptive ndfe scheme is robust against frequency offset

error rate evaluation of linear equalization and decision feedback equalization with error propagation we propose applying an approximate fourier series to evaluate efficiently the bit error rate ber performance of finite length linear equalization le and decision feedback equalization dfe by extending the fourier series we enable ber calculations for quadrature phase shift keying qpsk transmission on complex channels with in phase and crosstalk intersymbol interference isi the ber calculation is based on determining the residual isi samples and background gaussian noise variance at the equalizer output for static channels or for realizations of quasi static fading channels a simple bound on the series error magnitude in terms of the fourier series parameters ensures the required accuracy and precision improved state transition probability estimates are derived and verified by simulation for an approximate markov model of the dfe error propagation for the case in which residual isi exists even when the previous decisions stored in the feedback filter fbf are correct we demonstrate the ease and widespread applicability of our approach by producing results which elucidate a variety of equalization tradeoffs our analysis includes symbol spaced and fractionally spaced minimum mean square error mmse le zero forcing zf le and mmse dfe with and without error propagation on static isi channels and multipath channels with quasi static rayleigh fading a comparison between suboptimum and optimum receiver filtering in conjunction with equalization and an assessment of the accuracy of some widely used equalization ber approximations and bounds

roughness fluctuations roughness exponents and the universality class of ballistic deposition abstract in order to estimate roughness exponents of interface growth models we propose the calculation of effective exponents from the roughness fluctuation in the steady state we compare the finite size behavior of these exponents and the ones calculated from the average roughness w 2 for two models in the 2 1 dimensional kardar parisi zhang kpz class and for a model in the 1 1 dimensional villain lai das sarma vlds class the values obtained from provide consistent asymptotic estimates eventually with smaller finite size corrections for the vlds nonlinear molecular beam epitaxy class we obtain 0 93 0 01 improving previous estimates we also apply this method to two versions of the ballistic deposition model in two dimensional substrates in order to clarify the controversy in terms of its universality class raised by numerical results and a recent derivation of its continuous equation effective exponents calculated from suggest that both versions are in the kpz class additional support for this conclusion is obtained by a comparison of the full roughness distributions of these models and the distribution of other discrete kpz models

surface and bulk properties of ballistic deposition models with bond breaking abstract we introduce a new class of growth models with a surface restructuring mechanism in which impinging particles may dislodge suspended particles previously aggregated on the same column in the deposit the flux of these particles is controlled through a probability p these systems present a crossover for small values of p from random to correlated kpz growth of surface roughness which is studied through scaling arguments and monte carlo simulations on one and two dimensional substrates we show that the crossover characteristic time t scales with p according to t p y with y n 1 and that the interface width at saturation w s a t scales as w s a t p with n 1 2 where n is either the maximal number of broken bonds or of dislodged suspended particles this result shows that the sets of exponents y 1 and 1 2 or y 2 and 1 found in all previous works focusing on systems with this same type of crossover are not universal using scaling arguments we show that the bulk porosity p of the deposits scales as p p y for small values of p this general scaling relation is confirmed by our numerical simulations and explains previous results present in literature

interior point least squares estimation transient convergence analysis and application to mmse decision feedback equalization in many communication systems training sequences are used to help the receiver identify and or equalize the channel the amount of training data required depends on the convergence properties of the adaptive filtering algorithms used for equalization in this paper we propose the use of a new adaptive filtering method called interior point least squares ipls for adaptive equalization first we show that ipls converges exponentially fast in the transient phase then we use the ipls algorithm to update the weight vector for a minimum mean square error decision feedback equalizer mmse dfe in a cdma downlink scenario numerical simulations show that when training sequences are short ipls consistently outperforms rls in terms of system bit error rate and packet error rate as the training sequence gets longer ipls matches the performance of the rls algorithm

robust static and dynamic output feedback stabilization deterministic and stochastic perspectives three parallel gaps in robust feedback control theory are examined sufficiency versus necessity deterministic versus stochastic uncertainty modeling and stability versus performance deterministic and stochastic output feedback control problems are considered with both static and dynamic controllers the static and dynamic robust stabilization problems involve deterministically modeled bounded but unknown measurable time varying parameter variations while the static and dynamic stochastic optimal control problems feature state control and measurement dependent white noise general sufficiency conditions for the deterministic problems are obtained using lyapunov s direct method while necessary conditions for the stochastic problems are derived as a consequence of minimizing a quadratic performance criterion the sufficiency tests are then applied to the necessary conditions to determine when solutions of the stochastic optimization problems also solve the deterministic robust stability problems as an additional application of the deterministic result the modified riccati equation approach of petersen and hollot is generalized in the static case and extended to dynamic compensation

estimation of co channel signals with linear complexity to increase wireless system capacity using co channel signals and multiple receiver antennas we develop the partitioned viterbi algorithm pva the pva estimation complexity increases linearly with each additional co channel signal rather than exponentially as it does with joint maximum likelihood sequence estimation mlse the estimation problem involves multiple signals simultaneously transmitted and observed through slow fading frequency selective channels although transmission is assumed to be in bursts according to a time division multiple access scheme more than one signal can occupy the same time and frequency slot these signals are referred to as co channel signals separation and estimation of the symbol bursts is accomplished by exploiting channel differences pva estimation consists of a set of viterbi detectors one per signal that operate in parallel with cross coupling to allow approximate interference cancellation by means of tentative decisions the forward filter of a decision feedback equalizer dfe is used to prefilter received signals prior to pva estimation prefiltering delays the energy of interfering signals so that tentative decisions become reliable enough to use simulation results show pva performance remains near optimal with respect to the performance of joint mlse

compensatability and optimal compensation of systems with white parameters the optimal compensation problem is considered in the case of linear discrete time systems with stationary white parameters and quadratic criteria a generalization of the notion of mean square stabilizability namely mean square compensatability is introduced it is shown that suitable mean square compensatability and detectability conditions are sufficient and necessary in general for the existence of a unique optimal mean square stabilizing compensator tests are given to determine whether or not a system is mean square compensatable it is indicated how to calculate numerically the tests and the optimal mean square stabilizing compensator the results are illustrated with examples

classification of 2 1 dimensional growing surfaces using schramm loewner evolution statistical behavior and scaling properties of isoheight lines in three different saturated two dimensional grown surfaces with controversial universality classes are investigated using ideas from schramm loewner evolution text sle ensuremath kappa we present some evidence that the isoheight lines in the ballistic deposition bd eden and restricted solid on solid rsos models have conformally invariant properties all in the same universality class as the self avoiding random walk saw equivalently text sle 8 3 this leads to the conclusion that all these discrete growth models fall into the same universality class as the kardar parisi zhang kpz equation in two dimensions

robust design of adaptive equalizers although equalizers promise to improve the signal to noise energy ratio zero forcing equalizers are derived classically in a deterministic setting minimizing intersymbol interference while minimum mean square error mmse equalizer solutions are derived in a stochastic context based on quadratic wiener cost functions in this paper we show that it is possible and in our opinion even simpler to derive the classical results in a purely deterministic setup interpreting both equalizer types as least squares solutions this in turn allows the introduction of a simple linear reference model for equalizers which supports the exact derivation of a family of iterative and recursive algorithms with robust behavior the framework applies equally to multiuser transmissions and multiple input multiple output mimo channels a major contribution is that due to the reference approach the adaptive equalizer problem can equivalently be treated as an adaptive system identification problem for which very precise statements are possible with respect to convergence robustness and l2 stability robust adaptive equalizers are much more desirable as they guarantee a much stronger form of stability than conventional in the mean square sense convergence even some blind channel estimation schemes can now be included in the form of recursive algorithms and treated under this general framework

a smoothing algorithm for systems with multiplicative noise a fixed point smoothing algorithm is derived for linear time varying systems with multiplicative noise in the state channels of the plant and the measurements as well as additive noise as in systems without multiplicative noise the smoothed estimates depend on the filtered estimates the steady state behavior of the algorithm is examined if the multiplicative part is absent then the results coincide with known results for systems with additive noise

longest increasing subsequence as expectation of a simple nonlinear stochastic partial differential equation with a low noise intensity we report some observations concerning the statistics of longest increasing subsequences lis we argue that the expectation of lis its variance and apparently the full distribution function appears in statistical analysis of some simple nonlinear stochastic partial differential equation in the limit of very low noise intensity

boundedness behavior of the spectral factorization for polynomial data in the wiener algebra spectral factorization is of fundamental importance in many areas of signal processing this paper investigates the boundedness behavior of the spectral factorization mapping in the wiener algebra thereby the focus lies on the factorization of polynomial spectral densities with a finite degree n since such spectra are especially important for practical applications the paper presents a lower and an upper bound on the boundedness behavior which will show that the boundedness constant of the spectral factorization mapping gets worse as the degree n of the spectra increases therewith one obtains independently the known result that the spectral factorization mapping is unbounded on the wiener algebra

maximum entropy type lyapunov functions for robust stability and performance analysis we present two lyapunov functions that ensure the unconditional stability and robust performance of a modal system with uncertain damped natural frequency each lyapunov function involves the sum of two matrices the first being the solution to the so called maximum entropy equation and the second being a constant auxiliary portion the significant feature of these lyapunov functions is that the guaranteed robust stability region is independent of the weighting matrix while the performance bounds are relatively tight compared to alternative approaches thus these lyapunov functions are less conservative than standard bounds that tend to be highly sensitive to the choice of state space basis kevwords maximum entropy function robust stability robust performance

rate of convergence in approximating the spectral factor of regular stochastic sequences common methods for the calculation of the spectral factorization rely on an approximation of the given spectral density by a polynomial and a subsequent factorization of this polynomial it is known that the regularity of the stochastic sequence determines the achievable approximation rate of its spectrum however since the approximative polynomial should be factorized it has to be positive it is shown that this restriction on the approximation polynomial implies a limitation on the approximation rate for linear methods whereas for nonlinear methods the optimal approximation rate can still be achieved this has also consequences for the rate of convergence of the spectral factor which is investigated in the second part there a lower and an upper bound for the error in the spectral factor is derived which shows the dependency on the approximation degree and on the regularity of the stochastic sequence finally if the spectral density is given only on a finite set of sampling points no linear approximation method exists such that the error in the spectral factor can be controlled by the approximation degree

robust stabilization with positive real uncertainty beyond the small gain theory in many applications of feedback control phase information is available concerning the plant uncertainty for example lightly damped flexible structures with colocated rate sensors and force actuators give rise to positive real transfer functions closed loop stability is thus guaranteed by means of negative feedback with strictly positive real compensators in this paper the properties of positive real transfer functions are used to guarantee robust stability in the presence of positive real but otherwise unknown plant uncertainty these results are then used for controller synthesis to address the problem of robust stabilization in the presence of positive real uncertainty one of the principal motivations for these results is to utilize phase information in guaranteeing robust stability in this these sense results go beyond the usual limitations the small gain theorem and quadratic lyapunov functions which may be conservative when phase information is available the results of this paper are based upon a riccati equation formulation of the positive real lemma and thus are in the spirit of recent riccati based approaches to bounded real h control

controller design of systems with multiplicative noise general linear continuous stochastic systems are considered with multiplicative noise in the control and state channels and stabilizing optimal inputs are synthesized for both time varying and time invariant situations in the nonstationary case a set of nonlinear matrix differential equations has to be solved in the stationary problem a set of nonlinear matrix algebraic equations provides the solution the separation principle is not valid and both the state filtering and the control synthesis problem should be treated simultaneously

universality and corrections to scaling in the ballistic deposition model in order to analyze some controversies on the equivalence between ballistic deposition bd and the kardar parisi zhang kpz theory we simulated the bd model in one and two dimensions effective exponents betal were obtained in the growth regions which were rigorously determined for various lengths l effective exponents alphal were obtained from saturation widths in the steady state regimes in d 1 we found betal beta al lambda and alphal alpha bl delta with asymptotic exponents consistent with the kpz values beta 1 3 and alpha 1 2 and correction to scaling exponents 0 2 or approximately lambda or approximately 0 4 and 0 6 or approximately delta or approximately 0 8 these strong finite size corrections explain the previous discrepancies between numerical estimates for bd and the exact kpz results in d 2 we could only obtain reliable estimates of alphal which are consistent with kpz values if finite size corrections with delta approximately 0 4 are considered

on the distribution of surface extrema in several one and two dimensional random landscapes we study here a standard next nearest neighbor nnn model of ballistic growth on one and two dimensional substrates focusing our analysis on the probability distribution function p m l of the number m of maximal points i e local peaks of growing surfaces our analysis is based on two central results i the proof presented here of the fact that uniform one dimensional ballistic growth process in the steady state can be mapped onto rise and descent sequences in the ensemble of random permutation matrices and ii the fact established in ref g oshanin and r voituriez j phys a math gen 37 6221 2004 that different characteristics of rise and descent patterns in random permutations can be interpreted in terms of a certain continuous space hammersley type process for one dimensional system we compute p m l exactly and also present explicit results for the correlation function characterizing the enveloping surface for surfaces grown on 2d substrates we pursue similar approach considering the ensemble of permutation matrices with long ranged correlations determining exactly the first three cumulants of the corresponding distribution function we define it in the scaling limit using an expansion in the edgeworth series and show that it converges to a gaussian function as l

the influence of uncertainty on stability and compensatability in this paper the influence of uncertainty of linear discrete time systems modelled with white parameters on the properties of mean square stability and compensatability is considered a measure for the uncertainty in the system matrices is introduced this measure is also a measure for the uncertainty in the system in the sense that stability and compensatability decreases if this system uncertainty increases it is indicated how to calculate numerically the mean square stability and compensatability the results are illustrated with examples

the optimal projection equations with petersen hollot bounds robust stability and performance via fixed order dynamic compensation for systems with structured real valued parameter uncertainty a feedback control design problem involving structured real valued plant parameter uncertainties is considered a quadratic lyapunov bound suggested by recent work of i r petersen and c v hollot 1986 is utilized in conjunction with the guaranteed cost approach of s s l chang and t k c peng 1972 to guarantee robust stability with robust performance bound necessary conditions that generalize the optimal projection equations for fixed order dynamic compensation are used to characterize the controller that minimizes the performance bound the design equations thus effectively serve as sufficient conditions for synthesizing dynamic output feedback controllers that provide robust stability and performance

computing the minimum phase filter using the ql factorization we investigate the ql factorization of a time invariant convolutive filtering matrix and show that this factorization not only provides the finite length equivalent to the minimum phase filter but also gives the associated all pass filter the convergence properties are analyzed and we derive the exact convergence rate and an upper bound for a simple single input single output system with filter length finally this upper bound is used to derive an approximation of the convergence rate for systems of arbitrary length implementation wise the method has the advantage of being numerically stable and straight forward to extend to the multiple input multiple output case furthermore due to the existence of fast ql factorization methods it is possible to compute the filters efficiently

equal diagonal qr decomposition and its application to precoder design for successive cancellation detection in multiple input multiple output mimo multiuser detection theory the qr decomposition of the channel matrix h can be used to form the back cancellation detector in this paper we propose an optimal qr decomposition which we call the equal diagonal qr decomposition or briefly the qrs decomposition we apply the decomposition to precoded successive cancellation detection where we assume that both the transmitter and the receiver have perfect channel knowledge we show that for any channel matrix h there exists a unitary precoder matrix s such that hs qr where the nonzero diagonal entries of the upper triangular matrix r in the qr decomposition of hs are all equal to each other the precoder and the resulting successive cancellation detector have the following properties a the minimum euclidean distance between two signal points at the channel output is equal to the minimum euclidean distance between two constellation points at the precoder input up to a multiplicative factor that equals the diagonal entry in the r factor b the superchannel hs naturally exhibits an optimally ordered column permutation i e the optimal detection order for the vertical bell labs layered space time v blast detector is the natural order c the precoder s minimizes the block error probability of the qr successive cancellation detector d a lower and an upper bound for the free distance at the channel output is expressible in terms of the diagonal entries of the r factor in the qr decomposition of a channel matrix e the precoder s maximizes the lower bound of the channel s free distance subject to a power constraint f for the optimal precoder s the performance of the qr detector is asymptotically at large signal to noise ratios snrs equivalent to that of the maximum likelihood detector mld that uses the same precoder further we consider two multiplexing schemes time division multiple access tdma and orthogonal frequency division multiplexing ofdm we d

finite size effects on the growth models of das sarma and tamborenea and wolf and villain abstract the original versions of the limited mobility growth models of das sarma and tamborenea dt and of wolf and villain wv were simulated in one dimensional substrates of lengths l 256 and 512 respectively the growth regime may be separated in two regions giving effective growth exponents l finite size estimates of the roughness exponent l and of the dynamical exponents z l are obtained from data in the steady states the behavior of the effective exponents rules out the description by the fourth order linear theory of interface growth in the continuum limit for the dt model l shows a crossover to 1 3 and extrapolations of l considering finite size corrections with the local roughness exponent give 1 both consistent with the fourth order nonlinear theory while z l shows a slower crossover for the wv model extrapolations of l and z l are consistent with the values of the edwards wilkinson theory 0 5 and z 2 these results improve previous ones for the original dt and wv models from which we conclude that considering certain systematic data extrapolation methods the theoretically predicted universality classes can be inferred without using noise reduction schemes

brief paper optimal reduced order compensation of time varying discrete time systems with deterministic and white parameters the finite horizon optimal compensation problem is considered in the case of linear time varying discrete time systems with deterministic and white stochastic parameters and quadratic criteria the dimensions of the compensator are a priori fixed and may be time varying also the dimensions of the system may be time varying strengthened discrete time optimal projection equations sdope are developed which within the class of minimal compensators are equivalent to the first order necessary optimality conditions based on the sdope and their associated boundary conditions two numerical algorithms are presented to solve the two point boundary value problem one is a homotopy algorithm while the second iterates the sdope repeatedly forward and backward in time the latter algorithm is much more efficient and constitutes a generalization of the single iteration of the control and estimation riccati equations associated with the full order problem for systems with deterministic parameters the algorithms are illustrated with a numerical example the case of systems with deterministic parameters will be treated as a special case of systems with white parameters

convergence properties of adaptive equalizer algorithms in this paper we provide a thorough stability analysis of two well known adaptive algorithms for equalization based on a novel least squares reference model that allows to treat the equalizer problem equivalently as system identification problem while not surprising the adaptive minimum mean square error mmse equalizer algorithm behaves l2 stable for a wide range of step sizes the even older zero forcing zf algorithm however behaves very differently we prove that the zf algorithm generally does not belong to the class of robust algorithms but can be convergent in the mean square sense we furthermore provide conditions on the upper step size bound to guarantee such mean squares convergence we specifically show how noise variance of added channel noise and the channel impulse response influences this bound simulation examples validate our findings

joint optimization of fir prefilter and channel estimate for sequence estimation we provide simple analytical results for the coefficients of a finite impulse response fir prefilter and the effective channel impulse response ir for use in cellular communication systems we show that using a fir filter with both causal and anticausal filter taps it is possible to find the jointly optimized impulse response such that the signal to noise ratio is maximized in the least squares sense we show via computer simulation for 8 ary phase shift keying in enhanced data rates for global evolution edge that the joint optimization of the prefilter and ir produces results similar to the minimum mean square error decision feedback equalizer prefilter in thermal noise but yields gain in colored noise

robust stability and performance via fixed order dynamic compensation two robust control design problems are considered the robust stabilization problem in volves deterministically modeled bounded butunknown time varying parameter variations while the robust performance problem includes in addition a quadratic performance criterion averaged over stochastic disturbances and maximized over the admissible parameter variations for both problems the design goal is a fixed order i e reduced or full order dynamic strictly proper feedback compensator a sufficient condition for solving the robust stabilization problem is given by means of a quadratic lyapunov function parameterized by the compensator gains for the robust performance problem the lyapunov function provides an upper bound for the closed loop performance this leads to consideration of the auxiliary minimization problem minimize the performance bound over the class of fixed order controllers subject to the lyapunovofunction constraint necessary conditions for optimality in the auxiliary problem thus serve as sufficient conditions for robust stability and performance in the original problem two particular bounds are considered for constructing the quadratic lyapunov function the first corresponds to a right shift multi plicative white noise model while the second was suggested by recent work of petersen and hollot the main result is an extended version ofthe optimal projection equations for fixed order dynamic compensation whose solutions are guaranteed to provide both robust stability and robust performance

skewness and kurtosis of height distribution of thin films simulated by larger curvature model with noise reduction techniques abstract time varying skewness s and kurtosis q of height distribution of 2 1 dimensional larger curvature lc model with and without noise reduction techniques nrts are investigated in both transient and steady state regimes in this work effects of the multiple hit nrt m 1 nrt and the long surface diffusion length nrt l 1 nrt on the surface morphologies and characteristics of s and q are studied in the early growth time plots of s and q versus time of the m 1 morphologies show pronounced oscillation indicating the layer by layer growth our results show that s 0 and q 0 at every half layer while s 0 and q 0 at every complete layer the results are confirmed by the same plots of the results from the das sarma tamborenea dt model the l 1 lc model on the other hand has no evidence of the layer by layer growth mode due to the rapidly damped oscillation of s and q in the steady state the m 1 and l 1 nrts affect weakly on the values of s and q and the mounded morphologies of the film this lead to the evidence of universality of s and q in the steady state of the lc models with various m and l the finite size effect on the values of s and q is found to be very weak in the lc model by extrapolating to l we obtain s l 0 05 and q l 0 62 which are in agreement with the nrts results

relaxation to steady states and dynamical exponents in deposition models abstract considering some deposition models with limited mobility we show that the typical decay of the interface width to its saturation value is exponential which defines the crossover or saturation time we present a method to calculate a characteristic time 0 proportional to and estimate the dynamical exponent z in one dimensional substrates of lengths l 2048 the method is applied to the family model the restricted solid on solid rsos model and the ballistic deposition effective exponents z l converge to asymptotic values consistent with the corresponding continuum theories for the two dimensional family model the expected dynamic scaling hypothesis suggests a particular definition of 0 that leads to z 2 improving previous calculations based on data collapse methods for the two dimensional rsos model we obtain z 1 6 and

reduced complexity decoding of space time trellis codes in the frequency selective channel an iterative approach is developed for decoding space time trellis codes sttcs in frequency selective multiple input multiple output mimo channels the method uses the partitioned viterbi algorithm pva as an equaliser for the mimo system it is used iteratively with a sttc decoder that provides soft outputs using the soft output viterbi algorithm sova it is shown through simulation that the iterative procedure achieves a performance within 2 0 db of maximum likelihood ml decoding at a fer of 10 2 but suffers a small diversity loss it is also shown that the complexity of the iterative approach is lower than that of ml decoding

the optimal projection equations for reduced order discrete time state estimation for linear systems with multiplicative white noise the optimal projection equations obtained in 2 3 for reduced order discrete time state estimation are generalized to include the effects of state and measurement dependent noise to provide a model of parameter uncertainty in contrast to the single matrix riccati equation arising in the full order kalman filter case the optimal steady state reduced order discrete time estimator is characterized by three matrix equations one modified riccati equation and two modified lyapunov equations coupled by both an oblique projection and stochastic effects

matrix criterion robust linear quadratic control problem this paper studies a robust linear quadratic lq control problem with multiple quadratic performance indices specifically a linear model is assumed to be available for each of a prespecified set of operating points of a system a vector valued quadratic control performance index is given at each operating point it is then desired to find the constant feedback gains of a linear controller to obtain satisfactory control performance over all the operating points this is a realistic case involving the minimization of a vector criterion of vector criteria i e a matrix valued criterion the matrix valued criterion gives insight into standard robust lq design and leads to several related vector minimization problems and to natural procedures for computing a satisfactory non inferior solution to the matrix valued minimization problem

mimo equalization with state space channel models state space models are proposed to represent multiple input multiple output mimo frequency selective wireless channels with the motivation of better model approximation and more robust channel equalization performance when the order of the channel model is lower that of the true channel we develop a simple framework under which the equalizers for state space channel models can be designed using the existing methods for designing equalizers for finite impulse response fir models in particular a mimo minimum mean squared error decision feedback equalizer is developed for state space models when only estimates of the channel are available to the receiver the equalization performance is affected by the channel estimation accuracy because reduced order state space models can provide lower h 2 channel estimation error than reduced length fir models state space based equalizers typically exhibit significantly smaller symbol error rate than fir based ones thus state space channel models can be a more robust choice than fir models in the presence of model order selection error numerical simulation also shows that adaptive state space based receivers have slower but comparable convergence rates compared to fir based adaptive equalizers

prefiltered space time m bcjr equalizer for frequency selective channels this paper addresses the problem of soft equalization for space time coded transmissions over frequency selective fading channels the structure of the space time code is embedded in the channel impulse response for efficient joint equalization and decoding the proposed equalization decoding approach uses a prefilter to concentrate the effective channel power in a small number of taps followed by a reduced complexity maximum a posteriori probability map equalizer decoder to produce soft decisions the prefilter introduces residual intersymbol interference which degrades the performance of map when applied to the trellis of the shortened channel however the shape of the overall shortened channel impulse response allows the m algorithm to approximate the prefiltered map performance with a small number of states based on this general framework we investigate several enhancements such as using different prefilters for the forward and backward recursions concatenating two trellis steps during decoding and temporal oversampling the performance is evaluated through simulations over the edge typical urban channel

realizable linear and decision feedback equalizers properties and connections recently there has been renewed interest in the use of infinite impulse response iir linear equalizers les for digital communication channels as a means for both improving performance and blindly initializing decision feedback structures dfes theoretical justification for such an approach is usually given assuming unconstrained filters which are not causal and therefore not implementable in practice we present an analysis of realizable i e causal stable and of finite degree minimum mean square error mmse equalizers for single input multiple output channels both in the le and dfe cases focusing on their structures and filter orders as well as the connections between them the dfe resulting from rearranging the mmse le within a decision feedback loop is given special attention it is shown that although this dfe does not in general coincide with the mmse dfe it still enjoys certain optimality conditions the main tools employed are the wiener theory of minimum variance estimation and kalman filtering theory which show interesting properties of the mmse equalizers not revealed by previous polynomial approaches

fast techniques for computing finite length mimo mmse decision feedback equalizers multiple input multiple output mimo digital communication systems have received great attention due to their potential of increasing the overall system throughput in such systems mimo decision feedback equalization dfe schemes are often used to mitigate intersymbol interference isi resulting from channel multipath propagation in this context the existing computationally efficient methods for exact estimation of the dfe filters under minimum mean square error criteria mmse dfe rely on fast cholesky decomposition and backsubstitution or levinson techniques these methods may still present several difficulties in implementation as the demand on higher transmission rates increases and thus a simple solution is necessary in this paper new procedures for fast computation of the mimo mmse dfe are presented the new algorithms are obtained from a simple observation namely that the optimal feedforward filter fff is related to the well known kalman gain matrix commonly encountered in fast recursive least squares adaptive algorithms for which fast recursions exist and are readily applicable moreover the feedback filter can be easily computed via stable fast mimo convolution techniques as a result the proposed method is less complex more structured and can be as reliable in finite precision as known approaches in the literature

scaling cumulant ratios and height distribution of ballistic deposition in 3 1 and 4 1 dimensions we investigate the origin of the scaling corrections in ballistic deposition models in high dimensions using the method proposed by alves et al phys rev e 90 052405 2014 in d 2 1 dimensions where the intrinsic width associated with the fluctuations of the height increments during the deposition processes is explicitly taken into account in the present work we show that this concept holds for d 3 1 and 4 1 dimensions we have found that growth and roughness exponents and dimensionless cumulant ratios are in agreement with other models presenting small finite time corrections to the scaling that in principle belong to the kardar parisi zhang kpz universality class in both d 3 1 and 4 1 our results constitute further evidence that the upper critical dimension of the kpz class if it exists is larger than 4

optimum equalization of multicarrier systems a unified geometric approach this paper presents a new iterative equalization algorithm that maximizes the capacity for discrete multitone dmt systems the research modifies a previously proposed criterion and applies an appropriate transformation to map the objective function and the constraint set into a canonical region the resulting constraint set exhibits an identifiable geometric characteristic using the gradient projection method in conjunction with projection onto convex sets pocs provides us with an iterative search algorithm that facilitates the gradient descent method we also generalize the approach to two important subclasses of equalizers namely linear phase and unit tap filters we also derive a fundamental limit on the performance of the proposed approach in comparison with the previous methods the proposed equalization algorithm is less computationally complex and more geometrically intuitive simulation experiments confirm the validity of the proposed method for equalization of dmt systems

the optimal projection equations for static and dynamic output feedback the singular case oblique projections have been shown to arise naturally in both static and dynamic optimal design problems for static controllers an oblique projection was inherent in the early work of levine and athans while for dynamic controllers an oblique projection was developed by hyland and bernstein this note is motivated by the following natural question what is the relationship between the oblique projection arising in optimal static output feedback and the oblique projection arising in optimal fixed order dynamic compensation we show that in nonstrictly proper optimal output feedback there are indeed three distinct oblique projections corresponding to singular measurement noise singular control weighting and reduced compensator order moreover we unify the levine athans and hyland bernstein approaches by rederiving the optimal projection equations for combined static dynamic nonstrictly proper output feedback in a form which clearly illustrates the role of the three projections in characterizing the optimal feedback gains even when the dynamic component of the nonstrictly proper controller is of full order the controller is characterized by four matrix equations which generalize the standard lqg result

throughput maximizing fir transmit filters for linear dispersive channels optimum finite impulse response transmit filters for symbol by symbol transmission on linear dispersive additive gaussian noise channels are derived by maximizing the channel throughput subject to a fixed average input energy constraint this maximized throughput is compared to that achievable with water pour and flat transmit filters the effect of transmit filter optimization on the receiver performance is investigated by considering the popular minimum mean square error decision feedback equalizer receiver structure

mmse decision feedback equalizers and coding i equalization results the minimum mean squared error decision feedback equalizer mmse dfe has properties that suggest that it is a canonical equalization structure for systems that combine equalization with coded modulation the structure and performance of the mmse dfe are succinctly derived using linear estimation theoretic principles in this first part of this two part paper the front end of the mmse dfe called the mean square whitened matched filter ms wmf is preferable in some ways to a matched filter or a whitened matched filter as a canonical receiver front end in a coded system the feedback filter of the mmse dfe may be implemented in the transmitter using precoding the mmse dfe can perform significantly better than a zero forcing decision feedback equalizer particularly at moderate to low snr s and on severe isi channels the mmse dfe is biased the optimum unbiased mmse dfe is the mmse dfe with the bias removed removing bias improves error probability but reduces the snr to snr sub mmse dfe u snr sub mmse dfe 1 it is shown that this snr relationship is a particular case of a very general result and that snr sub mmse dfe u gives a more realistic estimate of snr the results are extended to partial response equalization and to equalization with correlated inputs in an appendix

mmse decision feedback equalizers finite length results this paper extends a number of results on the infinite length minimum mean square error decision feedback equalizer mmse dfe reported by cioffi dudevoir eyuboglu and forney see ieee trans commun 1995 to the finite length case cholesky factorization and displacement structure theory are demonstrated to be two powerful analytical tools for analyzing the finite length mmse dfe our objective throughout the paper is to establish finite length analogs of the well known infinite length mmse dfe results similarities and differences between the two cases are examined and delineated finally convergence of our derived finite length results to their well established infinite length counterparts is shown

minimum mean squared error impulse response shortening for discrete multitone transceivers we reformulate the minimum mean squared error mmse solution for time domain equalization teq in multitone transceivers we show that the maximum shortening signal to noise ratio mssnr and minimum intersymbol interference min isi teqs are particular cases of our new formulation with good channel estimation the mmse teq achieves near optimal rate performance our algorithm is less sensitive to channel estimation errors than other methods

robust optimal parametric lq control with a guaranteed cost bound and applications for an optimal parametric linear quadratic lq control problem a design objective is to determine a controller of constrained structure such that the closed loop system is asymptotically stable and an associated performance measure is optimized in the presence of system uncertainty the system via a parametric lq design is further required to be robust in terms of maintaining the closed loop stability with a guaranteed cost bound this problem is referred to as robust optimal parametric lq control with a guaranteed cost bound and is addressed in this work a new design method is proposed to find an optimal controller for simultaneously guaranteeing robust stability and performance over a specified range of parameter variations the results presented generalize some previous work in this area a versatile numerical algorithm is also given for computing the robust optimal gains the usefulness of the design method is demonstrated by numerical examples and a design of the robust control of a vtol helico

doubly selective channel estimation using exponential basis models and subblock tracking three versions of a novel adaptive channel estimation approach exploiting the over sampled complex exponential basis expansion model ce bem is presented for doubly selective channels where we track the bem coefficients rather than the channel tap gains since the time varying nature of the channel is well captured in the ce bem by the known exponential basis functions the time variations of the unknown bem coefficients are likely much slower than those of the channel and thus more convenient to track we propose a subblockwise tracking scheme for the bem coefficients using time multiplexed tm periodically transmitted training symbols three adaptive algorithms including a kalman filtering scheme based on an assumed autoregressive ar model of the bem coefficients and two recursive least squares rls schemes not requiring any model for the bem coefficients are investigated for bem coefficient tracking simulation examples illustrate the superior performance of our approach over several existing doubly selective channel estimators

adaptive linear equalization combined with noncoherent detection for mdpsk signals a novel noncoherent receiver for m ary differential phase shift keying signals transmitted over intersymbol interference channels is presented the noncoherent receiver consists of a linear equalizer and a decision feedback differential detector a significant performance gain over a previously proposed noncoherent receiver can be observed for an infinite number of feedback symbols the optimum equalizer coefficients can be calculated analytically and the performance of the proposed receiver approaches that of a coherent linear minimum mean squared error equalizer moreover a modified least mean square and a modified recursive least squares algorithm for adaptation of the equalizer coefficients are discussed

optimal projection equations for discrete time fixed order dynamic compensation of linear systems with multiplicative white noise the optimal projection equations for discrete time reduced order dynamic compensation are generalized to include the effects of state control and measurement dependent noise in addition the discrete time static output feedback problem with multiplicative disturbances is considered for both problems the design equations are presented in a concise unified manner to facilitate their accessibility for developing numerical algorithms for practical applications

time varying versus time invariant finite length mmse dfe on stationary dispersive channels a time varying structure and a time invariant structure for the finite length minimum mean square error decision feedback equalizer mmse dfe are presented and their performances are compared on a stationary channel impaired by intersymbol interference isi and additive gaussian noise the time varying structure has an innovations error sequence but incurs a throughput loss on isi channels because of its block processing nature conditions under which the time invariant structure exhibits near optimal performance are described both structures converge to the canonical mmse dfe of cioffi et al see ibid vol 43 p 2582 94 1995 and vol 43 p 2595 2604 1995 as their filters lengths become infinite

on the robustness of meop design versus asymptotic lqg synthesis an analysis is presented of generalized lqg linear quadratic gaussian type algorithms with regard to robustness characteristics comparisons are made of the maximum entropy optimal projection meop methodology versus the linear quadratic gaussian with loop transfer recovery lqg ltr technique and the parametric robust linear quadratic gaussian synthesis procedure the performance of a reduced order direct controller design is compared with the full order designs on a example representative of typical flexible structure dynamics

robust controller synthesis using the maximum entropy design equations this note presents an application of the optimality conditions obtained in 1 for dynamic compensation in the presence of state control and measurement dependent noise by solving these equations which represent a fundamental generalization of standard steady state lqg theory a series of increasingly robust control designs is obtained for the example considered in 2

the optimal projection equations for fixed order dynamic compensation first order necessary conditions for quadratically optimal steady state fixed order dynamic compensation of a linear time invariant plant in the presence of disturbance and observation noise are derived in a new and highly simplified form in contrast to the pair of matrix riccati equations for the full order lqg case the optimal steady state fixed order dynamic compensator is characterized by four matrix equations two modified riccati equations and two modified lyapunov equations coupled by a projection whose rank is precisely equal to the order of the compensator and which determines the optimal compensator gains the coupling represents a graphic portrayal of the demise of the classical separation principle for the reduced order controller case

application of fast subspace decomposition to signal processing and communication problems the authors previously described a class of fast subspace decomposition fsd algorithms though these algorithms can be applied to solve a variety of signal processing and communication problems with significant computational reduction they focus their discussion on two typical applications i e sensor array processing and time series analysis in many cases replacing the usual eigenvalue decomposition evd or singular value decomposition svd by the fsd is quite straightforward however the fsd approach can exploit more structure of some special problems to further simplify the implementation they first discuss the implementation details of the fsd such as how to choose an optimal starting vector how to handle correlated noise and how to exploit additional matrix structure for further computational reduction then they describe an fsd approach targeted at data matrices rectangular n spl times m n spl ges m which requires only o nmd flops where d denotes the signal subspace dimension versus a regular o nm sup 2 m sup 3 svd the computational reduction is substantial in typical scenarios i e d spl lt m spl les n in the spectrum estimation problems the data matrix has additional structure such as toeplitz or hankel they finally show how the fsd can exploit such structure for further computational reduction

computational methods for parametric lq problems a survey iterative methods for finding the optimal constant feedback gains for parametric lq problems notably for optimal constant output feedback problems are surveyed the connections of several methods to loss function expansions are discussed with important implications to the understanding of their convergence properties especially the descent anderson moore method levine athans like methods and the newton method are considered convergence results are also included the initialization problem and the output feedback stabilization problem are also discussed furthermore it is shown that the concepts and methods surveyed in this paper are useful in solving many realistic generalized parametric lq problems as well notably so called robust parametric lq problems

finite size correction for the diffusion front roughness exponent abstract the width of the diffusion front originated by the random motion of particles under a concentration gradient is evaluated by means of monte carlo simulations it is found that the roughness exponent of the front exhibits a systematic dependence on the sample size that can be rationalized in terms of a finite size correction extrapolation to the thermodynamic limit allows us to evaluate the actual roughness exponent in excellent agreement with theoretical predictions linking the diffusion system to the percolation problem

discrete growth models on deterministic fractal substrate abstract the growth of the modified family model and the etching model on the sierpinski carpet is studied by means of numerical simulations the evolving interface of the aggregates is described by the well established family vicsek dynamic scaling approach the results of the modified family model prove the universality of the fractional langevin equation introduced by lee and kim s b lee j m kim phys rev e 80 2009 021101 the etching model also shows good scaling behavior we conjecture that the systematic deviations of the data found in the ballistic deposition c m horowitz f roma e v albano phys rev e 78 2008 061118 may be due to the finite size effects of the ballistic deposition model

multiple models multiplicative noise and linear quadratic control algorithmic aspects numerical solution of certain linear quadratic lq control problems for robust design is addressed an iterative method is suggested and analysed for solving a minimax multiple model lq control problem a convergent iterative method is studied for finding the constant feedback gains of a given linear controller so as to solve a spectral radius functional minimization problem for multiple plants furthermore a convergent iterative method is proposed for solving the maximum entropy parametric lq control problem with multiplicative noise

iterative decoding of coded thp with quantized output this paper proposes an improved receiver for the tomlinson harashima precoder thp in this paper we demonstrate that most of the capacity loss associated with conventional thp is due to the suboptimal receiver which is a simple modulo operation we then demonstrate how this loss can can be avoided with the optimization of the receiver in the suboptimal receiver the modulo operation ignores the dependency between the implicitly added sequence of symbols removed by it we then implement a thp with quantized outputs the quantized outputs enable to implement a maximum a posteriori probability map receiver that exploits the dependency between the received symbols the map receiver is named enhanced modulo operation emod and operates as an inner decoder within an iterative scheme either with outer low density parity check ldpc code or outer turbo code simulation results show considerable gains relative to coded conventional thp and also relative to the turbo equalizer te which does not use a precoder in the transmitter

low complexity equalization of time varying channels with precoding this correspondence deals with time varying tv single input multiple output simo channels which are both frequency selective due to high data rate and time selective due to mobility a complex exponential basis expansion model ce bem is used to model the channel we consider a block transmission system where on the transmit side a precoder is employed to enable the maximum available diversity for a ce bem channel after direct decoding on the receive side the resulting channel resembles a finite impulse response fir filter on both block and symbol level we therefore propose an equalizer that bears a structure analogous to the effective channel in comparison with a standard block minimum mean square error decision feedback equalizer bmmse dfe that involves the inversion of a large size matrix the proposed parametric equalizer renders a similar performance but at a lower computational cost if there are multiple outputs present another contribution of this correspondence is a semiblind algorithm to estimate this equalizer when the channel state information is not available the equalizer taps and the information symbol estimates are refined recursively by means of normalized least mean squares nlms adaptation

simulation of finite size effects of the fiber bundle model abstract in theory the macroscopic fracture of materials should correspond with the thermodynamic limit of the fiber bundle model however the simulation of a fiber bundle model with an infinite size is unrealistic to study the finite size effects of the fiber bundle model fiber bundle models of various size are simulated in detail the effects of system size on the constitutive behavior critical stress maximum avalanche size avalanche size distribution and increased step number of external load are explored the simulation results imply that there is no feature size or cut size for macroscopic mechanical and statistical properties of the model the constitutive curves near the macroscopic failure for various system size can collapse well with a simple scaling relationship simultaneously the introduction of a simple extrapolation method facilitates the acquisition of more accurate simulation results in a large limit system which is better for comparison with theoretical results