dct algorithms for composite sequence lengths this paper presents fast algorithms for the type ii and iii discrete cosine transforms of composite sequence length in particular a radix q algorithm where q is an odd integer is derived for uniform or mixed radix decomposition of the discrete cosine transform by combining the radix q and radix 2 algorithms a general decomposition method for any composite length is developed reduction of computational complexity can be achieved for many sequence lengths compared with that needed by the well known radix 2 algorithm furthermore both the proposed and chan and siu s 1993 mixed radix algorithms achieve the same computational complexity for n 3 2 sup p and 9 2 sup p however our algorithm uses a simpler decomposition approach and provides a wider range of choices of sequence lengths

pilot symbol assisted and code aided phase error estimation for high order qam transmission in this paper we present a novel phase error estimation scheme for low density parity check ldpc coded high order quadrature amplitude modulation qam transmission systems and we provide a theoretical analysis of its performance the scheme uses known pilot symbols which are assumed to be placed at equally spaced positions and phase errors occurred at these pilot symbol positions are first estimated they are then interpolated over the other positions by using a wiener filter wf and estimation accuracy is next refined by using an ldpc code aided phase locked loop pll we present an efficient implementation of this two stage pilot symbol assisted and code aided psa ca scheme and evaluate its performance by means of power spectrum analysis we determine the mean square error at each step of the psa ca scheme and we find an optimal value of the noise equivalent loop bandwidth of the pll under the assumption that local oscillator phase noise can be modeled as a single pole single zero model we show through simulations that the psa ca scheme has a very high phase noise tolerance comparable to that of state of the art phase estimation schemes and provides a computationally efficient alternative

block processing soft input soft output demodulator for coded psk using dct based phase noise estimation this paper considers the detection of coded phaseshift keying signals subjected to additive white gaussian noise and oscillator phase noise we propose a detector that partitions the received frame into smaller blocks and models the unknown phasor variations over each block as a truncated discrete cosine transform dct expansion detection and decoding are iteratively performed between a soft input soft output siso demodulator a siso demapper and a siso decoder based on the sum product algorithm and the factor graph framework whereas the expectation maximization algorithm is used in the demodulator for the dct coefficients estimation the resulting demodulator is shown to have an excellent performance complexity tradeoff and to be well suited for parallel processing on multiple cores

squeezing operations in fock space and beyond abstract combining the signal and the idler modes we show that each nondegenerate squeezing quadratic hamiltonian may be transformed into the form of a degenerate squeezing hamiltonian if one uses the smeared field formalism for the case of infinitely many photon modes we discuss the existence of squeezing quadratic hamiltonians in fock space this gives a limitation on the squeezing parameters which guarantees that all squeezed vacua are representable as vectors in fock space if this condition is not satisfied the case of strong squeezing then all squeezed vacua are outside the fock space and mutually inequivalent

general glauber coherent states on the weyl algebra and their phase integrals abstract glauber s original definition of coherence in quantum optics is extended to an operator algebraic version where now the coherent states are given on the weyl algebra and the factorization function appearing in the coherence condition is replaced by a linear form on the associated pre hilbert space this allows to consider more general cases than the fock representation different kinds of factorization of the correlation functionals are used to classify coherent states quasi free coherent states gauge invariant coherent states and their relationship are analyzed

classification and construction of quantum communication systems we consider quantum communication systems qcs s introduced by davies 10 on trace class operators on general state spaces these are different from the usual quantum information theory as they deal with input and output being continuous in time we introduce the concept of a refinement of a qcs offering the possibility of distinguishing particles for instance according to their phase or location and classify refinements of general qcs we then introduce the notions of a bounded interaction rate and a bounded modulation and give in special cases a classification of qcs s satisfying both conditions as this classification shows the existence of a class of qcs s being completely different from the examples studied by davies we are able to discuss the cases of an independent detector and a linear modulation

pilot aided carrier recovery in the presence of phase noise the paper deals with carrier recovery based on pilot symbols in single carrier systems the system model considered in the paper includes the channel additive white noise and the phase noise that affects the local oscillators used for up down conversion wiener s method is used to determine the optimal filter in estimation of phase noise assuming that a sequence of equally spaced pilot symbols is available our analysis allows to capture the cyclostationary performance of the estimate a phenomenon that is not considered in the previous literature in the paper closed form formulas for the transfer function of the optimal filter and for the mean square phase error are derived for the case where the phase noise is modelled as random phase walk for this case a suboptimal filter is proposed numerical results are presented to substantiate the analysis

joint iterative detection and decoding in the presence of phase noise and frequency offset we present a new algorithm for joint detection and decoding of iteratively decodable codes transmitted over channels affected by a time varying phase noise pn and a constant frequency offset the proposed algorithm is obtained as an application of the sum product algorithm to the factor graph representing the joint a posteriori distribution of the information symbols and the channel parameters given the channel output the resulting algorithm employs the soft output information on the coded symbols provided by the decoder and performs forward backward recursions taking into account the joint probability distribution of phase and frequency offset we present simulation results for high order coded modulation schemes based on low density parity check codes and serially concatenated convolutional codes showing that despite its low complexity the algorithm is able to cope with a strong pn and a significant uncompensated frequency offset thus avoiding the use of complicated data aided frequency estimation schemes operating on a known preamble the robustness of the algorithm in the presence of a time varying frequency offset is also discussed

fast algorithms for type iii dct of composite sequence lengths this correspondence presents an odd factor algorithm for the type iii discrete cosine transform dct for uniform or mixed radix decomposition by jointly using the old factor and the existing radix 2 algorithms a general decomposition method for arbitrarily composite sequence length is developed a reduction of computational complexity can be achieved compared with that needed by other reported algorithms for m 2 sup m the decomposition approach has a simple computational structure and supports a wider range of choices for different sequence lengths

algorithms for joint phase estimation and decoding for mimo systems in the presence of phase noise and quasi static fading channels in this paper we derive the maximum a posteriori map symbol detector for a multiple input multiple output system in the presence of wiener phase noise due to noisy local oscillators and quasi static fading channels as in single antenna systems the computation of the optimum receiver is an analytically intractable problem and is unimplementable in practice in this purview we propose three suboptimal low complexity algorithms for approximately implementing the map symbol detector which involve joint phase noise estimation and data detection our first algorithm is obtained by means of the sum product algorithm where we use the multivariate tikhonov canonical distribution approach in our next algorithm we derive an approximate map symbol detector based on the smoother detector framework wherein the detector is properly designed by incorporating the phase noise statistics from the smoother the third algorithm is derived based on the variational bayesian framework by simulations we evaluate the performance of the proposed algorithms for both uncoded and coded data transmissions and observe that the proposed techniques significantly outperform the other important algorithms from prior works which are considered herein

multi objective optimization over convex disjunctive feasible sets using reference points abstract in this paper we consider the multiple objective optimization problem moop where concave functions are to be maximized over a feasible set represented as a union of compact convex sets to solve this problem we consider two auxiliary scalar optimization problems which use reference points the first one contains only continuous variables it has higher dimensionality however it is convex the second scalar problem is a mixed integer programming problem the solutions of both scalar problems determine nondominated points some other properties of these problems are also discussed

on generalized quantum stochastic counting processes we study counting processes introduced by davies 11 on general state spaces the concept of a refinement of a counting process cp corresponding to the possibility of distinguishing particles for instance according to their energy or phase is introduced and refinements of general cp s are classified then cp s with bounded interaction rate are classified on general state spaces and sufficient conditions are given in order that the operators characterizing the interaction rate can be formulated in the schrodinger picture for cp s with unbounded interaction rates it is shown that analogous to the case of bounded interaction rates there is a family of operators characterizing the interaction rate commutation relations for such processes are derived for constructions of cp s with unbounded interaction rate it is shown that it essentially suffices to solve the semigroup perturbation problem finally refinements of these cp s are characterized by measures e j e on the set of different particles where eachj e in an unbounded operator

on phase locked loops and kalman filters driessen 1994 and christiansen 1994 independently showed that for a specific dynamic model the proportional integral phase locked loop pll has the same structure as the kalman filter in this paper closed form expressions of the corresponding kalman gain values are derived both in acquisition and tracking modes of the pll

message passing algorithms for phase noise tracking using tikhonov mixtures phase noise poses a serious challenge for high speed digital communications systems mainly when going to higher and higher carrier frequencies such as in satellite communications traditionally phase noise estimation was performed separately from the decoding task and it was shown recently that there is much to be gained from joint estimation and decoding particularly when using ldpc low density parity check turbo codes however jointly estimating phase noise and decoding is a very complex and computationally demanding task in this paper we propose several algorithms based on the sum and product algorithm spa for low complexity joint decoding and estimation of coded information in strong phase noise channels these algorithms are based on a novel approximation of spa messages as tikhonov mixtures of a given order since mixture based bayesian inference such as spa creates an exponential increase in mixture order for consecutive messages a mixture reduction scheme is a must therefore in this paper we propose a low complexity mixture reduction algorithm which provably satisfies an upper bound on the kullback leibler kl divergence between the mixture and the reduced mixture we then reduce the complexity even further including limiting the model order and reducing the clustering effort to simple component selection as an extreme case it is even possible to reduce the number of modes to one we show the relation between the simplified algorithm to the phase locked loop pll finally we show simulation results and complexity analysis for the proposed algorithms which show superior performance over other state of the art low complexity algorithms

use of reference points for solving monlp problems abstract the paper discusses a scalar problem that uses a reference point for solving the multiobjective nonlinear programming monlp problem some properties of the solution of this scalar problem are described in order to improve the achieved value of one criterion only the corresponding component of the reference point has to be changed

use of reference points for molp problems analysis abstract two scalar problems intended for analysis of multiple objective linear programming molp problems are considered in the paper these problems use reference points and it is shown how to change the reference point in order to improve the attained value of one criterion

using cutting planes in an interactive reference point approach for multiobjective integer linear programming problems we propose an interactive approach for multiple objective integer linear programming moilp problems that combines the use of the tchebycheff metric with cutting plane techniques at each interaction the method computes the nondominated solution for the moilp problem that is closest to a reference point according to the tchebycheff metric the information provided by the decision maker in each dialogue phase is used to adjust the next reference point through a sensitivity analysis stage cutting plane techniques enable the method to take advantage of computations performed at previous iterations to solve the next scalarizing integer program we address both theoretical issues and the computational implementation

soft metrics and their performance analysis for optimal data detection in the presence of strong oscillator phase noise in this paper we address the classical problem of maximum likelihood ml detection of data in the presence of random phase noise we consider a system where the random phase noise affecting the received signal is first compensated by a tracker estimator then the phase error and its statistics are used for deriving the ml detector specifically we derive an ml detector based on a gaussian assumption for the phase error probability density function pdf further without making any assumptions on the phase error pdf we show that the actual ml detector can be reformulated as a weighted sum of central moments of the phase error pdf we present a simple approximation of this new ml rule assuming that the phase error distribution is unknown the ml detectors derived are also the aposteriori probabilities of the transmitted symbols and are referred to as soft metrics then using the detector developed based on gaussian phase error assumption we derive the symbol error probability sep performance and error floor analytically for arbitrary constellations finally we compare sep performance of the various detectors metrics in this work and those from literature for different signal constellations phase noise scenarios and snr values

a multidimensional phase locked loop for blind multiuser detection this paper concerns the problem of blind multiuser detection a special case of the blind source separation problem in which the source signals have finite alphabets specifically we address the problem of identifying and resolving the n spl times n unitary matrix ambiguity u that results from whitening the receiver observations where n is the number of sources we propose the multidimensional phase locked loop mpll as a generalization of a scalar decision directed pll to vector valued signals the mpll adapts an estimate of u according to the recursion u spl circ sub k 1 u spl circ sub k r sub k where r sub k is an n dimensional householder like rotation depending on only the kth receiver observation the o n sup 2 complexity of an efficient implementation of the algorithm is extremely low nevertheless simulation results demonstrate good convergence properties and superior steady state performance when compared with prior techniques the algorithm is also able to accommodate large alphabets and shaped alphabets

timing recovery with frequency offset and random walk cramer rao bound and a phase locked loop postprocessor we consider the problem of timing recovery for bandlimited baud rate sampled systems with intersymbol interference and a timing offset that can be modeled as a combination of a frequency offset and a random walk we first derive the crameacuter rao bound crb which is a lower bound on the estimation error variance for any timing estimator conventional timing recovery is based on a phase locked loop pll we compare the conventional timing recovery method with the crb for realistic timing parameters for the magnetic recording channel and observe a 7 db signal to noise ratio gap between the two next we propose a pll postprocessor based on the maximum a posteriori estimation principle that performs to within 1 5 db of the crb this postprocessor performs time invariant filtering and time varying scaling of the pll timing estimates the refined timing estimates from the postprocessor are then used to get refined samples by interpolating the samples taken at the pll s timing estimates finally we present suboptimal implementations that allow a performance complexity tradeoff

an overview of techniques for solving multiobjective mathematical programs multiobjective mathematical programming has been one of the fastest growing areas of or ms during the last 15 years this paper presents some reasons for the rapidly growing increase in interest in multiobjective mathematical programming a discussion of the advantages and disadvantages of the three general approaches articulation of the decision maker s preference structure over the multiple objectives prior to during or after the optimization towards multiobjective mathematical programming a nontechnical overview of many of the specific solution techniques for multiobjective mathematical programming and a discussion of important areas for further research the overview concentrates on those techniques which require an articulation of the decision maker s preference structure either during or after the optimization since these are the areas where most of the recent research has been conducted it differs from previous overviews in that in addition to the timing of the elicited preference informa

reduced complexity decoding of ldpc codes various log likelihood ratio based belief propagation llr bp decoding algorithms and their reduced complexity derivatives for low density parity check ldpc codes are presented numerically accurate representations of the check node update computation used in llr bp decoding are described furthermore approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check node update or symbol node update or both in particular two main approaches for simplified check node updates are presented that are based on the so called min sum approximation coupled with either a normalization term or an additive offset term density evolution is used to analyze the performance of these decoding algorithms to determine the optimum values of the key parameters and to evaluate finite quantization effects simulation results show that these reduced complexity decoding algorithms for ldpc codes achieve a performance very close to that of the bp algorithm the unified treatment of decoding techniques for ldpc codes presented here provides flexibility in selecting the appropriate scheme from performance latency computational complexity and memory requirement perspectives