CiteULike: dcastro's sampling

Design and practical implementation of multifrequency RF front ends using direct RF sampling

ML Psiaki — 2008-05-07T06:08:30-00:00

Microwave Theory and Techniques, IEEE Transactions on, Vol. 53, No. 10. (2005), pp. 3082-3089.

The use of direct RF sampling has been explored as a means of designing multifrequency RF front ends. Such front ends will be useful to multifrequency RF applications such as global navigation satellite system receivers that use global positioning system (GPS) L1, L2, and L5 signals and Galileo signals. The design of a practical multifrequency direct RF sampling front end is dependent on having an analog-to-digital converter whose input bandwidth accommodates the highest carrier frequency and whose maximum sampling frequency is more than twice the cumulative bandwidth about the multiple carrier signals. The principle of direct RF sampling is used to alias all frequency bands of interest onto portions of the Nyquist bandwidth that do not overlap. This paper presents a new algorithm that finds the minimum sampling frequency that avoids overlap. This design approach requires a multifrequency bandpass filter for the frequency bands of interest. A prototype front end has been designed, built, and tested. It receives a GPS coarse/acquisition code at the L1 frequency and GPS antispoofing precision code at both L1 and L2. Dual-frequency signals with received carrier-to-noise ratios in excess of 52 dB-Hz have been acquired and tracked using this system.

Nonlinear importance sampling techniques for efficient simulation of communication systems

HJ Schlebusch — 2008-04-02T06:41:56-00:00

Communications, 1990. ICC 90, Including Supercomm Technical Sessions. SUPERCOMM/ICC '90. Conference Record., IEEE International Conference on (1990), pp. 631-635 vol.2.

The use of nonlinear biasing techniques in importance sampling (IS) simulations is discussed. For tail probability estimation, two new nonlinear IS (NLIS) approaches are presented: shift of absolute values (SAV) and sample elimination (SE). In the case of linear systems with Gaussian input, the SAV method is shown to be uniformly more efficient than the linear techniques and very robust with respect to suboptimal parameterization. With respect to suboptimal choice of its parameter, the efficiency of the SE method is more sensitive than that of all other IS techniques discussed. Both NLIS methods are found to be easily implementable alternatives to the standard linear IS(LIS) techniques. The estimation of very-low-interval probabilities is considered as a new field for the application of IS techniques. The author presents both an LIS and an NLIS approach for this problem and provides performance analyses. A uniform bound on the required sample size is obtained for both techniques, thus emphasizing their high efficiency. Both methods are shown to be very robust with respect to suboptimal parameterization

Carrier and sampling frequency offset estimation and correction in multicarrier systems

M Sliskovic — 2008-03-11T10:25:00-00:00

Global Telecommunications Conference, 2001. GLOBECOM '01. IEEE, Vol. 1 (2001), pp. 285-289 vol.1.

In this paper, we present a pilot-aided sampling and carrier frequency offset estimator in orthogonal frequency-division multiplexing (OFDM) systems. The proposed algorithm enables joint carrier and sampling frequency-offset estimation from a pilot whose duration is only two symbol periods. Furthermore, we propose time-domain signal processing algorithms for carrier and sampling frequency offset correction, which do not require time-consuming signal interpolation, and with which fixed free-running oscillators can be used. The performance of the proposed algorithms is studied through simulations, and compared to the performance of the other algorithms described in the literature

Efficient importance sampling techniques for simulation of multiuser communication systems

GC Orsak — 2007-11-14T09:26:41-00:00

Communications, IEEE Transactions on, Vol. 40, No. 6. (1992), pp. 1111-1118.

The problem of simulating error rates in direct sequence spread-spectrum code division multiple-access (CDMA) systems is examined. Due to the computational complexity required to simulate these systems, an importance sampling technique is developed based upon previous work by the authors. A conditional weighting function is derived such that the linear shift class of biasing densities can be employed. Results are given for a variety of detector structures and background noise distributions. It is shown that this biasing scheme can dramatically reduce the run time of realistic multiple-access simulations

A composite importance sampling technique for digital communication system simulation

NC Beaulieu — 2007-11-14T09:24:56-00:00

Communications, IEEE Transactions on, Vol. 38, No. 4. (1990), pp. 393-396.

A new composite importance sampling technique is presented and investigated. An exact analysis is used to optimize the sample size saving and to investigate its robustness with respect to threshold settings. The composite technique offers a sample size savings of ~2.5 compared to the translation technique of D. Lu and K. Yao (1988) for memoryless systems. Applications as well as some questions for further research are discussed

On importance sampling in digital communications. II. Trellis-coded modulation

JC Chen — 2007-11-14T09:23:58-00:00

Selected Areas in Communications, IEEE Journal on, Vol. 11, No. 3. (1993), pp. 300-308.

For Pt.I see ibid., vol.11, no.3, p.289-99 (1993). A simulation algorithm design strategy based on event simulation, conditional importance sampling, and mean translation biasing of Gaussian noise distribution is applied to systems with trellis-coded modulation using the error event simulation. Suboptimal Viterbi decoding in the presence of weak intersymbol interference (ISI) is discussed. To illustrate the utility of the technique, some numerical results for a nonlinear channel model with intersymbol interference (ISI) are presented

An Improved Importance Sampling Method for Digital Communication System Simulations

B Davis — 2007-11-14T09:23:17-00:00

Communications, IEEE Transactions on [legacy, pre - 1988], Vol. 34, No. 7. (1986), pp. 715-719.

The use of importance sampling techniques can substantially reduce the variance of probability of error estimates obtained by Monte Carlo type simulations of digital communication systems. However, the advantages of importance sampling diminish as the memory length of the system becomes greater. This paper presents a modified importance sampling method which achieves substantial variance reduction for systems with memory.

Inference from Iterative Simulation Using Multiple Sequences

Andrew Gelman — 2007-11-13T23:47:01-00:00

Statistical Science, Vol. 7, No. 4. (1992), pp. 457-472.

The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rather than for researchers interested in the probability theory underlying the iterative simulations themselves. Our recommended strategy is to use several independent sequences, with starting points sampled from an overdispersed distribution. At each step of the iterative simulation, we obtain, for each univariate estimand of interest, a distributional estimate and an estimate of how much sharper the distributional estimate might become if the simulations were continued indefinitely. Because our focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normality after transformations and marginalization, we derive our results as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations. The methods are illustrated on a random-effects mixture model applied to experimental measurements of reaction times of normal and schizophrenic patients.

Sequential Monte Carlo Methods for Dynamic Systems

Jun Liu — 2006-07-12T10:58:25-00:00

Journal of the American Statistical Association, Vol. 93, No. 443. (1998), pp. 1032-1044.

We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide applications. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We provide guidelines on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of Rao-Blackwellization to improve performance. Examples from econometrics and engineering are presented to demonstrate the importance of Rao-Blackwellization and to compare different Monte Carlo procedures.

Importance sampling simulation for evaluating the lower-bound BER of the Bayesian DFE

Sheng Chen — 2007-11-13T23:46:11-00:00

Communications, IEEE Transactions on, Vol. 50, No. 2. (2002), pp. 179-182.

An importance sampling (IS) simulation technique, originally derived by Iltis (1995) for Bayesian equalizers, is extended to evaluate the lower-bound bit error rate of the Bayesian decision feedback equalizer (under the assumption of correct decisions being fed back). Using a geometric translation approach, it is shown that the two subsets of opposite-class channel states are always linearly separable. A design procedure is presented, which chooses appropriate bias vectors for the simulation density to ensure asymptotic efficiency of the IS simulation

Efficient evaluation of the error probabilities of spread-spectrum multiple-access communications

KB Letaief — 2007-01-26T15:21:36-00:00

Communications, IEEE Transactions on, Vol. 45, No. 2. (1997), pp. 239-246.

We consider the performance of code-division multiple-access (CDMA) communications. In particular, we illustrate the use of a semianalytical approach which is combined with importance sampling for the efficient evaluation of the average bit-error rates (BERs) of asynchronous direct-sequence (DS) based CDMA systems employing binary phase shift keying (BPSK) modulation along with specific signature sequences for a variety of system parameters

An experimental investigation of conventional and efficient importance sampling

MC Jeruchim — 2007-01-25T12:01:58-00:00

Communications, IEEE Transactions on, Vol. 37, No. 6. (1989), pp. 578-587.

Importance sampling is a technique that can significantly reduce computer run-time in the estimation of bit error rate (BER). However, in the conventional implementation (CIS), the improvement reduces markedly for systems with long memory. An approach to recover the full improvement for such systems has been previously suggested, and is called `efficient' important sampling (EIS). A report is presented on an extensive series of simulation-based experiments with CIS and EIS, both to compare theoretical predictions to experimental observations, as well as to gain insight into the conditions of applicability, especially for EIS

On importance sampling in digital communications. I. Fundamentals

JC Chen — 2007-01-26T15:25:12-00:00

Selected Areas in Communications, IEEE Journal on, Vol. 11, No. 3. (1993), pp. 289-299.

A simulation algorithm design strategy based on the combination of event simulation, conditional importance sampling, and asymptotically optimal biasing of Gaussian noise inputs is discussed. The utility of this approach is illustrated by presenting numerical results for a satellite channel model that includes uplink and downlink noise sources, a travelling-wave tube amplifier (TWTA) nonlinearity, and intersymbol interference (ISI) from both uplink and downlink filtering. An overview and comparison of various simulation design strategies and some results on the optimization of general mean translation and variance scaling biasing schemes for nonlinear systems are presented

Quick simulation: a review of importance sampling techniques in communications systems

PJ Smith — 2007-01-26T15:19:33-00:00

Selected Areas in Communications, IEEE Journal on, Vol. 15, No. 4. (1997), pp. 597-613.

Importance sampling (IS) is a simulation technique which aims to reduce the variance (or other cost function) of a given simulation estimator. In communication systems, this usually, but not always, means attempting to reduce the variance of the bit error rate (BER) estimator. By reducing the variance, IS estimators can achieve a given precision from shorter simulation runs; hence the term “quick simulation.” The idea behind IS is that certain values of the input random variables in a simulation have more impact on the parameter being estimated than others. If these “important” values are emphasized by sampling more frequently, then the estimator variance can be reduced. Hence, the basic methodology in IS is to choose a distribution which encourages the important values. This use of a “biased” distribution will, of course, result in a biased estimator if applied directly in the simulation. However, there is a simple procedure whereby the simulation outputs are weighted to correct for the use of the biased distribution, and this ensures that the new IS estimator is unbiased. Hence, the “art” of designing quick simulations via IS is entirely dependent on the choice of biased distribution. Over the last 50 years, IS techniques have flourished, but it is only in the last decade that coherent design methods have emerged. The outcome of these developments is that at the expense of increasing technical content, modern techniques can offer substantial run-time saving for a very broad range of problems. We present a comprehensive history and survey of IS methods. In addition, we offer a guide to the strengths and weaknesses of the techniques, and hence indicate which techniques are suitable for various types of communications systems. We stress that simple approaches can still yield useful savings, and so the simulation practitioner as well as the technical researcher should consider IS as a possible simulation tool

Developments in the Theory and Application of Importance Sampling

P Hahn — 2007-01-25T11:59:14-00:00

Communications, IEEE Transactions on [legacy, pre - 1988], Vol. 35, No. 7. (1987), pp. 706-714.

The assessment of bit error rate (BER) performance of a digital communication system via computer simulation has traditionally been done using the Monte Carlo method. For very low BER, this method requires excessive computer time. This time can be substantially reduced by using extrapolation based on importance sampling (IS). In applying IS to a complex system, many considerations must be addressed, chief among which is the reliability (variance) of the estimator as a function of the system particulars. We discuss a number of these considerations and, specifically, derive a number of expressions for the variance. We find that the variance improvement may be severely limited by the dimensionality (or memory) of the system. We describe a means for circumventing this limitation through the definition of a statistically equivalent impulse response. For a linear system, this amounts to the ordinary impulse response. The simulation can be structured to estimate the equivalent impulse response using statistical regression. This new approach has been implemented and found to yield significant runtime improvement over conventional importance sampling for linear systems of large dimensionality. We believe this technique will work also for mildly nonlinear systems, as might be encountered in typical satellite Communications.

Improved importance sampling technique for efficient simulation of digital communication systems

D Lu — 2007-11-13T23:44:16-00:00

Selected Areas in Communications, IEEE Journal on, Vol. 6, No. 1. (1988), pp. 67-75.

An improved importance sampling technique for the efficient simulation of digital communication systems is proposed. Evaluation of low-probability error events by direct use of classical Monte Carlo (MC) simulation techniques usually involves a very large number of runs. Importance-sampling techniques make the low-probability events occur more frequently. The technique proposed here is based on optimized translations of the original probability densities. The only approximation needed in the optimizations is that of replacing the <e1>Q </e1> function by the simpler exponential expression. Detailed analytical evaluations of the estimation variances of the classical MC and the conventional and improved importance-sampling approaches for systems with memories and signals are presented and compared, showing the superior performance of the latter. Detailed numerical and simulation results are given

On the Application of Importance Sampling to BER Estimation in the Simulation of Digital Communication Systems

Qiang Wang — 2007-11-13T23:43:51-00:00

Communications, IEEE Transactions on [legacy, pre - 1988], Vol. 35, No. 11. (1987), pp. 1231-1233.

Importance sampling (IS) is a useful technique for reducing the number of Monte Carlo trials in BER estimation. Two important aspects of recent research work in this area are to find more applications of IS BER estimation and to seek a good bias scheme in the implementation of the estimator. This correspondence presents a general and rigorous mathematical description of these aspects of this problem which, we hope, will be useful for further research. We also present a general result on how to choose a good bias scheme which may provide some insight into the problem.

The Shannon sampling theorem—Its various extensions and applications: A tutorial review

AJ Jerri — 2007-10-31T10:25:22-00:00

Proceedings of the IEEE, Vol. 65, No. 11. (1977), pp. 1565-1596.

It has been almost thirty years since Shannon introduced the sampling theorem to communications theory. In this review paper we will attempt to present the various contributions made for the sampling theorems with the necessary mathematical details to make it self-contained. We will begin by a clear statement of Shannon's sampling theorem followed by its applied interpretation for time-invariant systems. Then we will review its origin as Whittaker's interpolation series. The extensions will include sampling for functions of more than one variable, random processes, nonuniform sampling, nonband-limited functions, implicit sampling, generalized functions (distributions), sampling with the function and its derivatives as suggested by Shannon in his original paper, and sampling for general integral transforms. Also the conditions on the functions to be sampled will be summarized. The error analysis of the various sampling expansions, including specific error bounds for the truncation, aliasing, jitter and parts of various other errors will be discussed and summarized. This paper will be concluded by searching the different recent applications of the sampling theorems in other fields, besides communications theory. These include optics, crystallography, time-varying systems, boundary value problems, spline approximation, special functions, and the Fourier and other discrete transforms.