Spatial Channel Models (SCM) used in wireless system simulations are determined by Large-Scale Parameters (LSP). LSPs include delay spread, shadow fading, angle of arrival and departure spread, and Rice K for line-of-sight channels. LSPs are modeled as a spatially correlated random field over the geometry of the network scenario being simulated. The general method is to generate an LSP map on a discrete lattice (or grid) and induce spatial correlation via spatial filtering as determined by the LSP's correlation distance. This step represents a large part of the simulator's computation load, which leads to the use of a coarsely spaced lattice and interpolation from nearest lattice points to arbitrary UE locations on the x-y plane. We investigate the computation vs. fidelity trade associated with LSP generation. We will show that when a coarse grid is employed, there can be significant nonstationary distortion of the LSP correlation due to the interpolation step. We investigate three different interpolators having varying correlation fidelity and computation load.

Pilot interference cancellation (IC) has recently received consideration for use in third generation (3G) CDMA cellular receivers. In this paper, we derive an expression for evaluating the bit error rate (BER) performance of both the RAKE and pilot IC receivers, under conventional random spreading code assumptions. The approach can provide results for a wide variety of transmitter, channel, and receiver assumptions, and can easily take into account the effects of channel estimation, as used for determining the RAKE combining coefficients and for reconstructing pilot interference. The analytical results are compared to detailed link-level simulations of the 3G commercial wideband CDMA system developed by 3GPP, with excellent agreement found.

Common pilot cancellation in mobile receivers has recently gained attention for CDMA cellular networks. This paper presents system simulation results showing that the network capacity gains achievable from both ideal and hardware resource limited pilot cancellation receivers are significant. We show that the highest gains are achievable from both ideal and hardware resource limited pilot cancellation receivers are significant. We show that the highest gains are achieved for networks populated with high rate data users, rather than voice users. We also pay special attention to the interaction between pilot cancellation and soft handoff in a power controlled network. We show that pilot cancellation allows the network to operate with fewer mobiles in soft handoff, hence reducing network signaling overhead and infrastructure costs.

Code-division multiple access (CDMA) has found widespread applications in various applications such as advanced wireless technologies and digital cellular communications. We consider the error-rate performance of asynchronous direct-sequence CDMA in a wireless radio communications environment that is characterized by Rayleigh multipath fading channels. Because of the system complexity, the performance of CDMA systems in such an environment is difficult to assess analytically. Monte Carlo simulations can offer an alternative performance evaluation tool. Unfortunately, in practice, such an alternative is often limited by an excessive computational burden. Importance sampling is a variance reduction technique which can significantly reduce the computational cost required by Monte Carlo simulations. This paper describes the basic principles behind this powerful technique. In particular, it illustrates its use as a computationally efficient and accurate tool for computer-aided design and analysis of uncoded as well as coded CDMA in wireless communications.

This correspondence addresses the problem of finding an input signal u that maximizes the output energy of a linear filter subject to the constraint /spl verbar/u/spl verbar//spl les/e where e is a specified envelope. The main contribution is to provide a new direct proof of the necessity of a certain fixed point condition. Using the weak* topology on the input signal space, the author also proves certain compactness and continuity properties that further illuminate the nature of the problem.>

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.>

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.>

Some extensions that allow new estimating and bounding techniques for certain sequences of random variables controlled by a large deviation principle are given. These results can be thought of as generalizations and extensions of the Chernoff bound used in communications theory.>

Two importance sampling techniques are presented for estimating the distribution of computation of sequential decoding for specific convolutional codes (not ensemble averages). Only stack algorithm decoders are considered. Numerical examples are presented for both the BSC and the AWGN channel.>

Estimation of the large deviations probability p/sub n/=P(S/sub n/>or= gamma n) via importance sampling is considered, where S/sub n/ is a sum of n i.i.d. random variables. It has been previously shown that within the nonparametric candidate family of all i.i.d. (or, more generally, Markov) distributions, the optimized exponentially twisted distribution is the unique asymptotically optimal sampling distribution. As n to infinity , the sampling cost required to stabilize the normalized variance grows with strictly positive exponential rate for any suboptimal sampling distribution, while this sampling cost for the optimal exponentially twisted distribution is only O(n/sup 1/2/). Here, it is established that the optimality is actually much stronger. As n to infinity , this solution simultaneously stabilizes all error moments of both the sample mean and the sample variance estimators with sampling cost O(n/sup 1/2/). In addition, it is shown that the embedded parametric family of exponentially twisted distributions has a certain uniform asymptotic stability property. The technique is stable even if the optimal twisting parameter(s) cannot be precisely determined.>

A bit error probability analysis of direct detection optical receivers employing avalanche photodiodes is presented. An asymptotic analysis for large signal intensities is first presented. This analysis provides some useful insight into the balance between the Poisson statistics, the avalanche gain statistics, and the Gaussian thermal noise. The conjugate distribution is then developed, which is obtained by applying the large deviations exponential twisting formula. It is demonstrated that this conjugate distribution can be used to obtain numerically efficient Monte Carlo estimates of the biterror probability via the importance sampling method.

A direct-sequence spread-spectrum multiple-access bit-error probability analysis is developed using large-deviations theory. Let m denote the number of interfering spread-spectrum signals and let n denote the signature sequence length. Then the large deviations limit is as n to infinity with m fixed. A tight asymptotic expression for the bit-error probability is proven, and in addition, recent large-deviations results with the importance sampling Monte Carlo estimation technique are applied to obtain accurate and computationally efficient estimates of the bit-error probability for finite values of m and n. The large-deviations point of view is compared also to the conventional asymptotics of central limit theory and the associated Gaussian approximation. The Gaussian approximation is accurate and the ratio m/n is moderately large and all signals have roughly equal power. In the near/far situation, however, the Gaussian approximation is quite poor. In contrast, large-deviations techniques are more accurate in the near/far situation, and it is here that these methods provide some important practical insight.>

Each simulation run simulates a single error event-that is, a subsequence of incorrect trellis branching decisions-and importance sampling is used to emphasize important nontrivial error events. The fundamental principles of the error event simulation method in conjunction with importance sampling are reviewed. The importance sampling background for coded communications systems is discussed in the context of block codes, because it is easier to present the fundamentals of importance sampling without the additional complexities of Viterbi decoding. The details of the error event simulation method for Viterbi decoders and numerical examples that demonstrate this method are presented. These numerical examples involve both hard and soft decision decoding for the ideal additive Gaussian noise channel. The technique is shown to provide markedly improved efficiency.>

It is shown how large-deviations theory can be applied to construct an asymptotically efficient simulation distribution for Monte Carlo simulation using the importance sampling technique. A sufficient and necessary condition is given for the asymptotic efficiency of the candidate simulation distributions. This is done in the multidimensional setting that is required by many practical simulation problems. The result obtained is applied primarily in two areas. First, the generalization of previous work dealing with functionals of Markov chains is discussed. A second area of application is the simulation of nonlinear systems with Gaussian inputs. As an example of a system with Gaussian inputs, the simulation of a digital communication channel with nonlinear ISI (intersymbol interference) characteristic of satellite data links is considered. In the case of linear ISI, the asymptotically efficient exponentially twisted distribution turns out to agree with a method previously proposed by D. Lu and K. Yao (1988). The large-deviations point of view provides some useful insight on how to extent the Lu and Yao method to nonlinear channels.>

The development of an exponential transformation of measure not restricted to independent identically distributed data is presented for a class of semi-Markov processes known as Markov-additive (MA) process. The MA measure transformation is applied to obtain a new form of the Wald identity. To demonstrate the utility of this extension to MA processes, the application of sequential tests with Markov data to error probability performance analysis is investigated. In particular, it is demonstrated how previous work dealing with robust error probability evaluation in the presence of uncertain serial dependency can be applied to sequential tests.>

Large deviations theory is used to analyze the exponential rate of decrease of error probabilities for a sequence of decisions based on a test statistics sequence (T/sub n/). It is assumed that (for a given statistical hypothesis) the distributions of T/sub n/ are determined by some unknown member of a class of probability distributions. The worst case, or least favorably exponential rate of error probability decrease over this class, is sought. It is shown that the Legendre-Fenchel transform of the maximized cumulant function yields a lower bound for the minimized large deviations rate function, and that in many cases this bound is tight. Application of the result is illustrated by a detailed consideration of i.i.d memoryless detection with an epsilon -contamination distribution family.>

It is desired to determine the worst-case asymptotic error probability performance of a given detector operating in an environment of uncertain data dependency. A class of Markov data process distributions is considered which satisfy a one-shift dependency bound and agree with a specified univariate distribution. Within this dependency contamination class the distribution structure which minimizes the exponential rate of decrease of detection error probabilities is identified. This is a uniform least-favorability principle, because the least-favorable dependency structure is the same for all bounded memoryless detectors. The error probability exponential rate criterion used is a device of large deviations theory. The results agree well with previous results obtained using Pitman's asymptotic relative efficiency (ARE), which is a more tractable small-signal performance criterion. In contrast to ARE, large deviations theory is closely related to finite-sample error probabilities via the finite-sample Chernoff bounds and other exponentially tight bounds and other approximations.

Statistical decisions are considered between two hypotheses which consist of classes of Poisson distributions for random counting measures. Each Poisson distribution is generated by an intensity measure on a general observation space. The classes are specified by Choquet capacity bounds on the intensity measures. This problem was first posed and studied by Geraniotis and Poor. A minimax Neyman-Pearson result for error probability performance is the main contribution. Recursive computation of the minimax test statistic is also investigated.

A model that accounts for uncertain data dependency is developed by generating a large class of stationary stochastic processes, each with the same univariate distribution. This class can be considered to be a contamination class about the nominal independent and identically distributed (i.i.d.) process distribution. The class is developed specifically for application to robust detector and estimator design based on asymptotic variance. Application of this dependency class leads to an intuitively pleasing result: the minimax variance estimators and the maximin efficacy detectors are the same as obtained using i.i.d, asymptotic estimation and detection theory. Thus our technique generalizes previously obtained robust design results for i.i.d, data to this dependent data case.

Previous literature on detector design has presented many tractable results using efficacy as a performance functional, which is a small signal or local approach. Some nonlocal issues of memoryless detection are considered. We argue that the asymptotic exponential rate of decrease of error probabilities, or Bayes risk, provides the appropriate asymptotic measure of performance. A main contribution is the development of an approximation for nonlocal design that retains the tractability of the efficacy approach. We have proved the local consistency of our approximation. Also, as a consequence of the analysis, new proofs for Pitman-Noether-like theorems have been obtained.

Approximate performance results are obtained for optimal detection of weak discrete time signals. The analysis parallels the "locally optimum" approach for detector design and is based on a small-signal Taylor series approximation to the Chernoff error probability bounds.