Jason Goldberg

Non-data-aided signal-to-noise-ratio estimation

Non-data-aided (NDA) signal-to-noise-ratio (SNR) estimation is considered for binary phase shift keying systems where the data samples are governed by a normal mixture distribution. Inherent estimation accuracy limitations are examined via a simple, closed-form approximation to the associated Cramer-Rao bound which eliminates the need for numerical integration. The expectation-maximization algorithm is proposed to iteratively maximize the NDA likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.

SNR estimation in time-varying fading channels

Signal-to-noise ratio (SNR) estimation is considered for phase-shift keying communication systems in time-varying fading channels. Both data-aided (DA) estimation and nondata-aided (NDA) estimation are addressed. The time-varying fading channel is modeled as a polynomial-in-time. Inherent estimation accuracy limitations are examined via the Cramer-Rao lower bound, where it is shown that the effect of the channels time variation on SNR estimation is negligible. A novel maximum-likelihood (ML) SNR estimator is derived for the time-varying channel model. In DA scenarios, where the estimator has a simple closed-form solution, the exact performance is evaluated both with correct and incorrect (i.e., mismatched) polynomial order. In NDA estimation, the unknown data symbols are modeled as random, and the marginal likelihood is used. The expectation-maximization algorithm is proposed to iteratively maximize this likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.

Downlink beamforming for spatially distributed sources in cellular mobile communications

This paper presents a new technique for downlink transmission beamformer design in cellular mobile communication systems using antenna arrays at the base station. The method is based on estimation of an underlying spatial distribution associated with each source’s spatial downlink channel. The algorithm is ‘blind’ in the sense that it depends only on uplink spatial channel statistics, requiring no mobile-to-base station feedback in the design procedure. The assumed underlying spatial distribution models are general enough to be used in a wide variety of mobile communications scenarios (e.g., rural, urban, sub-urban, indoor). The technique is of reasonable complexity with a significant portion of the computation carried out off-line. Simulation results verify the effectiveness of the approach.

Inherent limitations in the localization of a coherently scattered source

This paper addresses the problem of localizing a single coherently scattered, far-field, narrowband source emitting a Gaussian signal and observed in additive, Gaussian noise. An explicit expression for the Cramer-Rao bound (CRB) of the unknown spatial and spectral (i.e., source and noise power level) parameters of the source is presented for arbitrary array geometry. The CRB is used to study the inherent limitations in the estimation of the mean angle of a scattered source in comparison to the case of a point source at the same mean angle.

Source localization in shallow water in the presence of sensor location uncertainty

The problem of source localization in shallow water in the presence of sensor location uncertainty is considered. The Cramer-Rao Bound is used to carry out a feasibility study for the joint source and sensor location problem when the multipath propagation channel is modeled as a known, deterministic waveguide. Unlike the free-space propagation channel, the boundedness of the shallow-water waveguide along its vertical axis provides the key to joint determination of the source and sensor location parameters. It is seen that, when a set of intuitive identifiability conditions are satisfied, numerical examples indicate that, for the scenarios considered, the resulting loss in accuracy with which the source location can be estimated due to sensor location uncertainty may be tolerable.

Bearing estimation for a distributed source of nonconstant modulus ounds and analysis

The paper considers the problem of estimating the bearing of a single, far-field source, surrounded by local scatterers, using passive sensor array measurements. An associated source-bearing estimation problem is formulated, and the Crame/spl acute/r-Rao lower bound is evaluated. In addition, a comprehensive analysis is performed of the maximum likelihood estimates that (due to mismodeling) assume a constant modulus source, and the degradation in performance is quantified as a function of the sources empirical variance. It is shown that, for a limited price in terms of mean square error, the constant modulus maximum likelihood estimator can replace the optimal nonconstant modulus estimator.

Signal selective DOA tracking for multiple moving targets

A new algorithm for signal selective tracking of the directions-of-arrival (DOAs) of multiple moving targets with an array of passive sensors is presented. A new method based on the principles of maximum likelihood estimation and cyclostationarity is used to generate initial angle estimates which, in turn, are refined by a Kalman filter. Source angle dynamics are used to achieve correct data association. High performance is obtained with relatively low computational complexity.

Inherent limitations in data-aided time synchronization of continuous phase-modulation signals over time-selective fading channels

Time synchronization of continuous phase modulation (CPM) signals over time selective, Rayleigh fading channels is considered. The Cramer-Rao lower bound (CRLB) for this problem is studied for data-aided (DA) synchronization (i.e., known symbol sequence transmission) over a time-selective Rayleigh fading (i.e., Gaussian multiplicative noise) channel. Exact expressions for the bound are derived as well as simplified, approximate forms that enable derivation of a number of properties that describe the bounds dependence on key parameters such as signal-to-noise ratio (SNR), channel correlation, sampling rate, sequence length, and sequence choice. Comparison with the well-known slow fading (i.e., constant) channel bound is emphasized. Further simplifications are obtained for the special case of minimum phase keying (MSK), wherein it is shown how the bound may be used as a sequence design tool to optimize synchronization performance.

Data-Aided Signal-to-Noise-Ratio estimation in time selective fading channels

Data-Aided Signal-to-Noise-Ratio (SNR) estimation is considered for time selective fading channels whose time variation is described by a polynomial time model. The inherent estimation accuracy limitations associated with the problem are quantified via a Cramer-Rao Bound analysis. A maximum likelihood type class of estimators is proposed and its exact, non-asymptotic performance is computed. The standard, constant channel SNR estimator performance is determined in the presence of channel polynomial order mismatch. Simulations results are presented which verify the effectiveness of the technique as well as its performance advantage over previously proposed methods.

Robust data detection in asynchronous DS-CDMA in the presence of timing uncertainty

The decorrelating and minimum mean squared error data detectors for direct sequence code division multiple access (DS-CDMA) communications systems are known to exhibit low vulnerability to the near-far problem. Nevertheless, the performance of these algorithms is highly sensitive to an accurate knowledge of the user propagation delays as well as inter-symbol and/or inter-chip interference such as that produced by frequency-selective fading channels. A new sub-optimum symbol-by-symbol detector is presented which is robust in the presence of these two effects.