This paper deals with the multiuser medium access problem in the packet radio environment. Under the framework of network diversity multiple access (NDMA), a previously proposed medium access method, a blind collision resolution scheme is proposed employing rotational invariance and factor analysis techniques. The proposed approach (dubbed B-NDMA for blind NDMA) overcomes the difficulty of orthogonal identification codes required by the original protocol, thereby improving channel utilization and system capacity, while being insensitive to multipath effects and synchronization errors. Performance issues of the proposed technique are addressed both analytically and numerically.

Network-assisted diversity multiple access (NDMA) is a novel random medium access method, which provides impressive throughput efficiency for multiplexing variable-bit-rate data sources. We study the implementation of the NDMA scheme in the presence of multipath channels. A transceiver architecture and random access strategies are proposed, which are able to detect and resolve collisions in the presence of unknown propagation channels. The performance issues are studied both analytically and numerically.

The question addressed in this paper is whether and under what conditions blind source separation is possible using only second-order statistics. It is well known that for stationary, i.i.d. sources the answer is negative due to the inherent unitary matrix ambiguity of output second-order information. It is shown in this paper however, that if the sources' power is allowed to vary with time, unique identifiability can be achieved without resorting to higher order statistics. In many applications the sources' power does change with time (e.g., speech or fading communication signals), and therefore the result has practical relevance. A novel second-order source separation method is proposed based on a generalized eigen-decomposition of appropriate correlation matrices and the identifiability conditions are investigated. Asymptotic performance results for the output SIR are developed.

If error correcting coding is present in the information symbols, the channel estimation procedure may be further complicated, since the encoder introduces a nonlinear operation on the information symbols (in the field of reals). Moreover, because of the encoder's memory, the input to the channel may not be i.i.d. Therefore classical blind channel equalization methods may not be suitable for systems with coding. A blind stochastic maximum-likelihood channel estimation algorithm is proposed for convolutionally coded signals transmitted through a multipath channel. The performance of the estimator is explored, based on the evaluation of approximate Cramer-Rao bounds. The CRBs are used in turn to obtain approximate expressions for the probability of error. Finally, some illustrative simulations are presented.

Constrained optimization methods have received considerable attention as a means to derive blind multiuser receivers with low complexity. The receiver's output variance is minimized subject to appropriate constraints which depend on the multipath structure of the signal of interest. When multipath is present, the constraint equations can be written in parametric form, and the constraint parameters jointly optimized with the linear receiver's parameters. We develop adaptive solutions for this joint, constrained optimization problem. Both stochastic gradient and recursive least-square-type algorithms are developed. The performance of the proposed methods is compared with other blind and trained methods and turns out to be close to the trained minimum mean-square-error receiver.

This paper explores how joint network and spatial diversity can be employed to improve the throughput/delay performance of packet radio systems. In the context of the recently proposed network diversity multiple access (NDMA) technique, the challenges and advantages of using antenna arrays at the base station to complement network-assisted diversity are investigated. It is shown that remarkable improvement of the system performance can be achieved by combining network and spatial diversity. A blind method based on PARAFAC analysis tools is proposed to solve the collision resolution problem under joint network/spatial diversity. The method is also applicable to, e.g., joint network/spreading diversity.

Network-assisted diversity multiple access (NDMA) is a novel random access method, which provides remarkable throughput efficiency for multiplexing variable-bit-rate data sources. It explores diversity combining and signal separation techniques borrowed from signal processing problems to resolve collisions in random multiple access systems, so as not to introduce throughput penalties. We present a performance analysis of the NDMA approach under finite SNR and ARQ mechanisms. The embedded Markov chain and M/G/1 queue model were used to analyze the traffic in the system. Expressions of the maximum throughput and average system delay are provided. The analytical results are justified by numerical simulations.

In this paper, correlation-matching techniques are employed to estimate multipath channel parameters for a multiuser CDMA system with long spreading codes. For a given code sequences, the output correlation matrix (parametrized by the unknown channel coefficients) is compared with its instantaneous approximation. By minimizing the Frobenious norm of the resulting error matrix the channel parameters can be estimated up to a scalar ambiguity. Under the assumption of i.i.d. code sequences, identifiability for each channel is guaranteed and the asymptotic convergence of the proposed algorithm is established. Simulation results confirm our claims. Comparisons with other methods are also provided.

Constrained optimization techniques are studied for direct design of linear multichannel equalizers. Novel blind algorithms are derived by minimizing the equalizer's output variance subject to appropriate constraints. The constraints are chosen to guarantee no desired signal cancellation, and their parameters are jointly optimized to maximize the signal component at the output. The resulting blind algorithm was observed to have near optimal performance at high signal-to-noise ratio, i.e., close to the performance of the trained minimum mean-square-error receiver. Also, the proposed method is not sensitive to the color of the transmitted sequence. Analytical expressions are derived to quantify the algorithm's performance.

Constrained adaptive optimization techniques are employed to design direct blind equalizers. The method is based on minimizing the equalizer's output variance subject to appropriate constraints. The constraints are chosen to guarantee no desired signal cancellation and are also jointly and recursively optimized to improve the performance. Our method provides adaptive solutions which directly optimize the equalizer's parameters, while its performance compares favorably to that of the linear prediction based approaches. Global convergence is established and comparisons with other blind and trained methods are presented.

A blind stochastic maximum likelihood (ML) channel estimation algorithm is adapted to incorporate a known training sequence as part of the transmitted frame. A hidden Markov model (HMM) formulation of the problem is introduced, and the Baum-Welch (1970) algorithm is modified to provide a computationally efficient solution to the resulting optimization problem. The proposed method provides a unified framework for semi-blind channel estimation, which exploits information from both the training and the blind part of the received data record. The performance of the ML estimator is studied, based on the evaluation of Cramer-Rao bounds (CRBs). Finally, some preliminary simulation results are presented.

Direct sequence code division multiple access (CDMA) systems are considered, in a multipath environment. We address the problem of estimating each user's signature waveform without requiring training sequences. We show that the problem is considerably simplified if a novel transmission strategy is adopted, which combines spreading and interleaving. In this setup, chip sequences corresponding to successive bits are interleaved before transmission. Novel channel estimation algorithms are developed in this chip interleaving framework and their performance is analyzed. Adaptive implementations are derived and some illustrative simulations are presented.

When fractional samples are available at the receiver, blind channel estimation methods can be developed exploiting the cyclostationary nature of the received signal. In this paper, we show that different solutions are possible if cyclostationarity is introduced at the transmitter instead of the receiver. We propose specific coding and interleaving strategies at the transmitter which induce cyclostationarity and facilitate the equalization task. Novel subspace equalization algorithms are derived which make no assumptions whatsoever on the channel zero locations. Synchronization issues are briefly discussed and some simulation examples are presented.

The time-varying tap coefficients of frequency selective fading channels are typically modeled as random processes with low-pass power spectra. However, traditional adaptive techniques usually make no assumption on the channel's time variations and hence do not exploit this information. Kalman filtering methods are derived to track the channel by employing a multichannel autoregressive description of the time-varying taps in a decision-feedback equalization framework. Fitting a model to the variations of the channel's taps is a challenging task because the tap coefficients are not observed directly. A novel linear method is proposed in order to estimate the model parameters from input/output data. The consistency of the proposed method is shown, and some illustrative simulations are presented.

Blind estimation of time-varying (TV), rapidly fading channels is addressed, using a basis expansion approach. Each TV coefficient is expanded onto a basis and the expansion parameters are estimated for subsequent use in Viterbi or decision-feedback equalizers. Blind estimation of the expansion parameters is accomplished using higher order statistics. Identifiability of the channel is shown from second and fourth-order TV cumulants of the received signal. A cumulant matching criterion is adopted and instantaneous approximations are proposed in place of the nonstationary ensembles. Linear methods are also derived to initialize the nonlinear optimization procedure. Strong convergence of the proposed method is established. Finally, the method is tested on a simulated mobile radio channel with multipath.

Direct sequence, code-division multiple access (CDMA) schemes offer an attractive alternative for sharing a transmission medium among many users, while requiring minimal co-operation among them. A number of signal processing issues are related to the receiver's task of multiuser information extraction and detection. In this paper, a discrete-time multirate formulation is introduced for asynchronous CDMA systems, which can incorporate multipath effects. In this framework, linear receivers are derived which can completely suppress multiuser interference (decorrelating receivers). A criterion is introduced, which guarantees the decorrelating property, while providing optimal solutions in the presence of noise. The synchronization problem in a multipath environment is also studied, and identifiability conditions are established. A subspace algorithm is proposed, to estimate the user's delays and multipath channels in a blind scenario.

The problem of the detection and classification of deterministic objects and random textures in a noisy scene is discussed. An energy detector is developed in the cumulant domain by exploiting the noise insensitivity of higher order statistics. An efficient implementation of this detector is described, using matched filtering. Its performance is analyzed using asymptotic distributions in a binary hypothesis-testing framework. The object and texture discriminant functions are minimum distance classifiers in the cumulant domain and can be efficiently implemented using a bank of matched filters. They are immune to additive Gaussian noise and insensitive to object shifts. Important extensions, which can handle object rotation and scaling, are also discussed. An alternative texture classifier is derived from a ML viewpoint and is statistically efficient at the expense of complexity. The application of these algorithms to the texture-modeling problem is indicated, and consistent parameter estimates are obtained.>

Classification and estimation of non-Gaussian signals observed in additive Gaussian noise of unknown covariance are addressed using cumulants or polyspectra. By integrating ideas from pattern recognition and model identification, asymptotically optimum maximum-likelihood classifiers and ARMA (autoregressive moving average) parameter estimators are derived without knowledge of the data distribution. Identifiability of noncausal and nonminimum phase ARMA models is established using a finite number of cumulant or polyspectral lags of any order greater than two. A unifying view of cumulant and polyspectral discriminant measures utilizes these lags and provides a common framework for development and performance analysis of novel and existing estimation and classification algorithms. Tentative order determination and model validation tests for non-Gaussian ARMA processes are described briefly. Illustrative simulations are also presented.>

Parametric models obtained via second-order statistics (SOS) are appropriate when the available stationary data are linear, Gaussian, and time-reversible. On the other hand, evidence of nonlinearity, non-Gaussianity, or time-irreversibility favors the use of higher-order statistics (HOS). To quantify normality and time-reversibility, and thus resolve the title question, consistent, time-domain statistical tests are developed and analyzed in a Neyman-Pearson framework. The novel test statistics are computationally attractive and streamlined towards parametric modeling because they employ the minimal HOS lags which uniquely characterize autoregressive moving-average processes. Simulations illustrate the performance of the proposed tests.>