Matched illumination (MI) radar signals provide improved target signal to interference noise ratios (SINR) and better spread ambiguity function performance compared to conventional radar in the presence of range-spread targets. Performance improvements reported in literature are based on the assumption of perfect knowledge of responses of the extended targets as well as interference spectra. In this paper, we analyze the SINR performance of MI systems from the learning phase to illumination phase using complex geometric theory of diffraction (GTD)-based target models. We numerically evaluate the worst-case performance degradation arising from aspect change of the target models from learning to illumination phase. The results set the stage for inclusion of a scheduler, which would facilitate selective MI based on system parameters and the targets being tracked.

Importance sampling is used in this paper to address the classical yet important problem of performance estimation of block codes. Simulation distributions that comprise discrete- and continuous-mixture probability densities are motivated and used for this application. These mixtures are employed in concert with the so-called g-method, which is a conditional importance sampling technique that more effectively exploits knowledge of underlying input distributions. For performance estimation, the emphasis is on bit by bit maximum a-posteriori probability decoding, but message passing algorithms for certain codes have also been investigated. Considered here are single parity check codes, multidimensional product codes, and briefly, low-density parity-check codes. Several error rate results are presented for these various codes, together with performances of the simulation techniques.

Fast estimation methods for small outage probabilities of signaling in fading multiple-input multiple-output (MIMO) channels are developed. Communication over such channels is of much current interest, and quick and accurate methods for estimating outage capacities are needed. The methods described herein use adaptive importance sampling (IS) techniques as developed in a series of recent publications. Fast algorithms are provided for evaluating "nonergodic" capacities of Rayleigh fading MIMO channels. The methodology can be extended to more general models. Numerical results on outage capacity are provided, and these extend and complement known results in the literature.

We present new adaptive importance sampling techniques based on stochastic Newton recursions. Their applicability to the performance evaluation of communication systems is studied. Besides bit-error rate (BER) estimation, the techniques are used for system parameter optimization. Two system models that are analytically tractable are employed to demonstrate the validity of the techniques. As an application to situations that are analytically intractable and numerically intensive, the influence of crosstalk in a wavelength-division multiplexing (WDM) crossconnect is assessed. In order to consider a realistic system model, optimal setting of thresholds in the detector is carried out while estimating error rate performances. Resulting BER estimates indicate that the tolerable crosstalk levels are significantly higher than predicted in the literature. This finding has a strong impact on the design of WDM networks. Power penalties induced by the addition of channels can also be accurately predicted in short run-times.

A communication system using noiseless feedback for Rayleigh fading channels is proposed. Pilot tone signaling is used to provide channel amplitude and phase information for the detection of antipodal signals. Channel estimates generated in the receiver are fed back to control the transmitter power and rate. Power division and rate control rules are derived, and it is shown that definite performance improvement over diversity systems is obtained. Because of the requirement of small loop propagation delays, the technique is mainly applicable to low data rate multitone modems for short range HF channels.