Andreas Polydoros

A Unified Approach to Serial Search Spread-Spectrum Code Acquisition--Part I: General Theory

The purpose of this two-part paper is threefold: 1) Part I discusses the code-acquistion problem in some depth and 2) also provides a general extension to the approach of analyzing serial-search acquisition techniques via transform-domain flow graphs; 3) Part II illustrates the applicability of the proposed theoretical framework by evaluating a matchedfilter (fast-decision rate) noncoherent acquisition receiver as an example. The theory is formulated in a general manner which allows for significant freedom in the receiver modeling. The statistics of the acquisition time for the single-dwell [2], [3] andN-dwell [5] systems are shown to be special cases of this unified approach.

A Unified Approach to Serial Search Spread-Spectrum Code Acquisition--Part II: A Matched-Filter Receiver

The unified theory developed in Part I [1] is employed here in the analysis of a noncoherent, matched-filter (fast-decision-rate) code acquisition receiver in a direct-sequence spread-spectrum system. The results illustrate the dynamic dependence of the mean acquisition time on system parameters, such as the predetection signal-to-noise ratio (SNR), the decision threshold settings, and the ratio of the decision rate to the code rate.

On the detection and classification of quadrature digital modulations in broad-band noise

Optimal and suboptimal decision rules for the detection of constant-envelope quadrature digital modulations in broadband noise are derived and analyzed. The effect of various stochastic models for the carrier phase is examined in detail, while no epoch or frequency uncertainty is assumed. The delay-and-multiply type of detector is considered. A new binary/quadrature phase shift keying (BPSK/QPSK) classifier is compared to the more traditional ad hoc techniques of a square-law classifier and a phase-based classifier (weighting on the phase histogram). The new classifier is derived by approximating the likelihood-ratio functionals of phase-modulated digital signals in white Gaussian noise, hence is named the quasi-log-likelihood ratio (qLLR) rule. It is shown analytically that its performance is significantly better than that of intuitively designed phase-based rules or the conventional square-law classifier. >

Likelihood methods for MPSK modulation classification

New algorithms based on the likelihood functional (LF) and approximations thereof are proposed for the problem of classifying MPSK modulations in additive white Gaussian noise. Previously introduced classifiers for this problem are theoretically interpreted as simplified versions of the ones in here. The performance of a single-term approximation to the optimal LF classifier is evaluated analytically and is shown to be very close to that of the optimal. Furthermore, recursive algorithms for the implementation of this new quasi-log-likelihood-ratio (qLLR) classifier are derived which imply no significant increase in classifier complexity. The present method of generating classification algorithms can be generalized to arbitrary two-dimensional signal constellations. >

Likelihood ratio tests for modulation classification

We discuss modulation classification (MC) algorithms that are based on the decision theoretic approach, where the MC problem is viewed as a multiple-hypothesis testing problem. In particular, a random-phase AWGN channel is considered and possible solutions to this hypothesis testing problem are reviewed. We present two novel algorithms and we compare their performance with existing ones for a variety of modulation pairs. Simulation results show that these new algorithms can offer a significant performance gain for classification of dense, non-constant envelope constellations.

Slotted Random Access Spread-Spectrum Networks: An Analytical Framework

An analytic framework is proposed for the study of singlehop spread-spectrum networks using random access and packet switching under various network topologies and channel conditions. The key feature of the theory is the identification of a set of probabilistic parameters, which, based on a symmetry argument, serve to efficiently summarize the effect on performance of various network considerations such as transmitter-receiver configuration, spreadspectrum code allocation, error correction and detection mechanisms, spreading format, jamming conditions, etc. Examples investigating capture effects, coding tradeoffs, and scheduling optimizations are presented. Various previously known results are shown to be special cases of the framework that we describe.

MLSE for an unknown channel .I. Optimality considerations

The problem of performing joint maximum-likelihood (ML) estimation of a digital sequence and unknown dispersive channel impulse response is considered starting from a continuous-time (CT) model. Previous investigations of this problem have not considered the front-end (FE) processing in detail; rather, a discrete-time signal model has been assumed. We show that a fractionally-spaced whitened matched filter, matched to the known data pulse, provides a set of sufficient statistics when a tapped delay line channel model is assumed, and that the problem is ill-posed when the channel impulse response is generalized to a CT, finite-length model. Practical approximations are considered that circumvent this ill-posed condition. Recursive computation of the joint-ML metric is developed. Together, the FE processing and metric recursion provide a receiver structure which may be interpreted as the theoretical foundation for the previously introduced technique of per-survivor processing, and they lead directly to generalizations. Several FE processors representative of those suggested in the literature are developed and related to the practically optimal FE.

On sampling rate, analog prefiltering, and sufficient statistics for digital receivers

We consider the joint sequence estimation, timing and phase recovery for linear modulation. The paper differs from the classical ones in the sense that time-discrete algorithms suitable for fully digital receivers are discussed. Sufficient conditions are given such that the signal samples represent sufficient statistics. These conditions involve signal bandwidth, sampling/symbol rate and the analog prefilter characteristics. It is shown that the sampling rate need not be an exact multiple of the symbol rate, i.e., the samples can be taken from a free-running oscillator. All subsequent signal processing operations in the receiver then operate with the clock of this free-running oscillator. Timing recovery is then performed by a time-variant linear digital interpolator and a decimator. Carrier recovery and sequence estimation are performed at an average rate of one symbol per sample. The digital matched filter for this case is derived for an arbitrary colored noise spectrum. >

The principle of per-survivor processing: a general approach to approximate and adaptive MLSE

A class of algorithms for maximum likelihood sequence estimation (MLSE) is introduced. These algorithms are based on the principle of performing signal processing (PPSP) operations, necessary for the estimation of unknown parameters, in a per-survivor fashion. Introduced by several authors for state complexity reduction in an ISI (intersymbol interference) environment, DDFSE (delayed decision feedback sequence estimation) and RSSE (reduced state sequence estimation) make use of this principle. A number of algorithms which apply the PPSP to combined sequence estimation and channel identification are presented. The results of the simulation analysis are given.<<ETX>>

Genralized Serial Search Code Acquisition: The Equivalent Circular State Diagram Approach

A simple circular state-diagram method has been recently proposed [2] for the modeling, analysis and optimization of straight serial search code acquisition in spread spectrum systems. Herein, the approach is generalized to arbitrary serial strategies, such as the Z-search, expanding window search etc. The basic idea is the construction of equivalent circular state-diagrams, wherefrom the transform-domain description of the stochastic acquisition process is derived. The method circumvents any complicated time-domain combinatorial arguments, is readily systematized and includes recent results as special cases.