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This paper demonstrates through numerical simulation the performance of moderate (8x10 3 ) block length Low Density Parity Check (LDPC) codes on a High Frequency (HF) multipath fading channel utilizing Orthogonal Frequency Division Multiplexing (OFDM). Some results are also shown for the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The simulation parameters were chosen to compare results with the standard OFDM 39-tone HF waveform. LDPC codes are linear block codes that have outstanding error correction and detection capabilities.

Discrete-time linear systems that possess scale-invariance properties even in the presence of continuous dilation were proposed by Zhao and Rao. The paper presents results of subsequent investigation characterizing self-similarity properties of discrete-time signals synthesized by these systems. It is shown that white noise inputs to these linear scale invariant systems produce self-similar outputs regardless of the marginal distribution of the noise. We investigate this with different types of inputs and in most instances the outputs are fractional Gaussian and self-similar. This is confirmed by generating the fractional Gaussian noise from the fractional Brownian motion and comparing its characteristics with the system output. For heavy tailed input distributions, the output is also heavy-tailed and self-similar. It is also shown that it is possible to synthesize statistically self-similar signals whose self-similarity parameters are consistent with those observed in network traffic.

The problem of separating multiple, independent code division multiple access (CDMA) sources at a single antenna element receiver in the presence of inter symbol interference, co-channel interference and additive white Gaussian noise is addressed. The channels are not known and separation process is required to be blind and adaptive. The system is assumed to consist of multiple users. Only a single user separation is desired. The CDMA channel is assumed to be a multiple-input single-output dispersive channel. The user's codes are assumed to be orthogonal and known to the receiver. The adaptation process is blind and uses the well-known constant modulus (CM) algorithm. The CM approach uses a modulus-corrected version of the output signal in place of the training signal needed in nonblind equalization techniques. The CM algorithm minimizes the deviation of this modulus from a constant in a mean squared error sense. The minimization is done using an LMS-type algorithm.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We propose a new blind equalization technique based on second and fourth order cumulants. The algorithm proposed will equalize the second and fourth order cumulants of the input and output sequences. It is entirely driven by statistics, only requiring knowledge of the variance (power) of the input signal. Because of insensitivity of the higher-order cumulants to Gaussian processes, the algorithm performs well with additive Gaussian noise. Simulation examples are presented in which the proposed technique is compared with other existing equalization algorithms.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A two-channel, quadratic, nonlinear process driven by sinusoidal signals is considered. Equations for estimating the parameters of the process are derived from colored Gaussian noise contaminated observations of the process. The derivation is based on the bispectrum and cross-power spectrum of the two channels. The resulting equations are nonlinear and iterative techniques are needed to solve for the parameters. >

While the bispectrum has been used for the reconstruction of non-minimum phase 1-D signals not much work of a similar nature has been done for multidimensional signals. Here we present a technique for reconstructing 2-D non-minimum phase signals from samples of their bispectra. The reconstruction procedure involves recovering the magnitude of the Fourier transform of the signal from the bispectrum magnitude and its phase from the bispectrum phase. By using the bispectrum the ambiguity regarding phase is considerably removed (up to a linear phase factor) when compared to the spectral factorization approach.