Performance of the mobile user objective system is dominated by the channel conditions characterized by ionospheric scintillation and multipath fading. Detail analytical modeling for flat channel is provided in part I of this paper, while the emphasis for part II of this paper is on wideband waveforms. Efficient computer simulation techniques for the selective scintillation and terrestrial multipath fading channel impairment have been presented.

Performance of the mobile user objective system is dominated by the channel conditions characterized by ionospheric scintillation and multipath fading. Detail analytical channel modeling is provided, with the emphasis on narrowband waveforms in part I of this paper. Efficient computer simulation techniques for these flat fading channel impairment have also been presented.

This paper presents the results of simulation experiments that successfully demonstrate FM co-channel voice separation via cross coupled phase locked loops (CCPLL). Unlike previous CCPLL studies which are typically restricted to the situation where the FM modulation waveforms are steady state sinusoidal, triangular, etc., we have empirically determined CCPLL loop parameters that provide for stable separation of co-channel FM signals with comparable bandwidth (100% spectral overlap) and comparable modulation indices. The resulting CCPLL parameters differ somewhat from existing CCPLL design rules; however, the differences can yield a significant improvement in CCPLL performance.

A systolic Viterbi decoder for convolutional codes is developed which uses the trace-back method to reduce the amount of data needed to be stored in registers. It is shown that this new algorithm requires a smaller chip size and achieves a faster decoding time than other existing methods.>

This paper presents some preliminary results on the behavior of adaptive digital filters when implemented with fixed-point arithmetic. In particular, adaptive prediction error filters configured in both tapped-delay line and lattice forms are examined and their performance is compared in terms of the number of bits required to reach a given steady-state error level. The lattice structures are of special interest since, at least in the non-adaptive case, they are known to have well-behaved numerical properties (as compared with a tapped-delay line realization) when implemented with fixed-point arithmetic. The results of our study indicate that this is also true in the adaptive case. Ideally, this study should ultimately lead to efficient implementations (e.g., in VLSI) of adaptive digital filters.

In many applications of adaptive data equalization, rapid initial convergence of the adaptive equalizer is of paramount importance. Apparently, the fastest known equalizer adaptation algorithm is based on a recursive least squares estimation algorithm. In this paper we show how the least squares lattice algorithms, recently introduced by Morf and Lee, can be adapted to the equalizer adjustment algorithm. The resulting algorithm, although computationally more complex than certain other equalizer algorithms (including the fast Kalman algorithm), has a number of desirable features which should prove useful in many applications.

Lattice structures have recently been proposed for application to such areas as adaptive linear prediction, adaptive noise cancelling, and adaptive equalization. In this paper, we will consider the suppression of a sinusoidal interference by the use of an adaptive lattice structure implemented as a noise canceller. The performance of the adaptive lattice will be analyzed and compared with that of an LMS transversal adaptive noise canceller. Experimental results will also be presented and discussed.

In many applications of Wiener filtering to noise cancellation an external reference noise input is required. However, if an external reference is not available, it is still possible to suppress additive noise using a Wiener linear prediction filter (LPF) provided the signal bandwidth is significantly less than the noise bandwidth. The purpose of this paper is to provide an analytical basis for bounding the performance of a digital LPF when applied to the problem of cancelling broadband additive noise from narrow-band signals.

In this paper, a study of adaptive lattice algorithms as applied to channel equalization is presented. The orthogonalization properties of the lattice algorithms make them appear promising for equalizing channels which exhibit heavy amplitude distortion. Furthermore, unlike the majority of other orthogonalization algorithms, the number of operations per update for the adaptive lattice equalizers is linear with respect to the number of equalizer taps.

The optimal minimum-mean-square-error discrete filters are obtained for finite bandwidth signals in noise represented by a rational pole-zero model in the spectral domain. An analytical technique called the method of undetermined coefficients is used to solve the Wiener equation for the optimal prediction coefficients. These coefficients are then used to examine (a) the frequency response of the Wiener prediction filter, and (b) the maximum entropy spectral estimator. Special cases considered include the limiting case of narrow bandwidth signals and large prediction filter lengths.

A high resolution technique for estimating the frequencies, powers, and the number of sinusoids in noise is examined. Unlike the Pisarenko decomposition method which may only be applied to sinusoids in uncorrelated noise, the technique examined in this paper may be applied even when the noise is correlated. This technique consists of applying Prony's algorithm to a segment of the autocorrelation function, r(k), for which|k|is greater than Γcorr, where Γcorris the decorrelation time of the noise.