Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Li, YeSubject: search.filters.subject.Systems Integration Methodology

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Now showing 1 - 10 of 10
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    Fast Blind Adaptive Algorithms for Equalization and Diversity Reception in Wireless Communications Using Antenna Arrays
    (1996) Li, Ye; Liu, K.J. Ray; ISR
    To combat the multipath and time-variant fading of wireless communication channels, antenna arrays are usually used to improve the quality and increase the capacity of communication service. This paper investigates the fast blind adaptive algorithms for the equalization and diversity combining in wireless communication systems using antenna arrays. Two second- order statistics based algorithms, SOSA and MSOS, for equalization and diversity combining are proposed and their convergence in noiseless and noisy channels is analyzed. Since the proposed algorithms use only second-order statistics or correlation of the channel outputs, they converge faster than the higher-order statistics based algorithms, which is also confirmed by computer simulations examples.
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    A Structured Low Rank Matrix Pencil for Spectral Estimation and System Identification
    (1996) Razavilar, J.; Li, Ye; Liu, K.J. Ray; ISR
    In this paper we propose a new parameter estimation algorithm for damped sinusoidal signals. Parameter estimation for damped sinusoidal signals with additive white noise is a problem of significant interests in many signal processing applications, such as analysis of NMR data and system identification. The proposed algorithm estimates the signal parameters using a matrix pencil constructed from the measured data. To reduce the noise effect, rank deficient Hankel approximation of prediction matrix is used. We show that the performance of the estimation can be significantly improved by structured low rank approximation of the prediction matrix. Computer simulations also show that the noise threshold of our new matrix pencil algorithm is significantly low than those of the existing algorithms.
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    Blind Adaptive Equalization of SIMO Channels Based on Second- Order Statistics
    (1996) Li, Ye; Liu, K.J. Ray; ISR
    This article investigates blind adaptive equalization for single- input/multiple-output (SIMO) channels. A second-order statistics based algorithm (SOSA) and a modified second-order statistics based algorithm (MSOSA) for equalization of SIMO channels are presented. Computer simulation demonstrates that the new algorithms converge faster than fractionally spaced constant- modulus algorithm (FS-CMA). The proposed algorithms can be applied in wireless communication systems with antenna arrays to combat the multipath fading.
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    Blind MIMO FIR Channel Identification Based on Second-Order Statistics with Multiple Signals Recovery
    (1996) Li, Ye; Liu, K.J. Ray; ISR
    To separate and recover multiple signals in data communications, antenna arrays and acoustic sensor arrays, the impulse responses of multiple-input/multiple-output (MIMO) channels have to be identified explicitly or implicitly. This paper deals with the blind identification of MIMO FIR channels based on second-order statistics of the channel outputs. We first investigate the identifiability of MIMO FIR channels, and obtain a necessary and sufficient condition for MIMO FIR channels to be identifiable up to a unitary matrix using second-order statistics. Then, we extend the identification algorithms for single-input/multiple- output (SIMO) FIR channels, such as the algebraic algorithm and the subspace algorithm to the identification of MIMO FIR channels. We also present the application of the above blind identification algorithms to the separation of multiple signals in digital communication systems. Finally, we demonstrate the effectiveness of the algorithms presented in this paper by computer simulations
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    Adaptive Blind Multi-Channel Equalization for Multiple Signals Separation
    (1995) Li, Ye; Liu, K.J. Ray; ISR
    This paper investigates adaptive blind equalization for multiple- input and multiple-output (MIMO) channels and its application to blind separation of multiple signals received by antenna arrays in communication systems. The performance analysis is presented for the CMA equalizer used in MIMO channels. Our analysis results indicate that a double infinite-length MIMO-CMA equalizer can recover one of the input signals, remove the intersymbol interference (ISI), and suppress the rest signals. In particular, for the MIMO FIR channels satisfying certain conditions, the MIMO-CMA FIR equalizer is able to remove the ISI and co-channel interference regardless of the initial setting of the blind equalizer. To recover all input signals simultaneously, a novel MIMO channel blind equalization algorithm is developed in this paper. The global convergence of the new algorithm for MIMO channels is proved. Hence, the new blind equalization algorithm for MIMO channels can be applied to separate and equalize the signals received by antenna arrays in communication systems. Finally, computer simulations are presented to confirm our analysis and illustrate the performance of the new algorithm.
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    A Fast Minimal-Symbol Subspace Approach to Blind Identification and Equalization
    (1995) Sampath, B.; Li, Ye; Liu, K.J. Ray; ISR
    A subspace-based blind channel identification algorithm using only the fact that the received signal can be oversampled is proposed. No direct use is made of the statistics of the input sequence or even of the fact that the symbols are from a finite set and therefore this algorithm can be used to identify even channels in which arbitrary symbols are sent. A modification of this algorithm which uses the extra information in the more common case when the symbols are from a finite set is also presented. This LS-Subspace algorithm operates directly on the data domain and therefore avoids the problems associated with other algorithms which use the statistical information contained in the received signal. In the noiseless case, it is possible for the proposed Basic Subspace algorithm to identify the channel exactly using the least number of symbols that can possibly be used. Thus, if the length of the impulse response of a channel is JT, T being the symbol interval, then it is possible to use this algorithm to identify the channel using an observation interval of just (J + 3)T. In the noisy case, simulations have shown that almost exact identification can be obtained by using a few more symbols than the theoretical minimum. This is orders of magnitude better than the other blind algorithms. Moreover, this algorithm is computationally very efficient and has no convergence problems.
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    Static and Dynamic Convergence Behavior of Adaptive Blind Equalizers
    (1995) Li, Ye; Liu, K.J. Ray; ISR
    This paper presents a theoretical analysis of the static and dynamic convergence behavior for a general class of adaptive blind equalizers. We first study the properties of prediction error functions of blind equalization algorithms, and then we use these properties to analyze the static and dynamic convergence behavior based on the independent assumption. We prove in this paper that with a small step-size, the ensemble average of equalizer coefficients will converge to the minimum of the cost function near the channel inverse. However, the convergence is not consistent. The correlation matrix of equalizer coefficients at equilibrium is determined by a Lyapunov equation. According to our analysis results, for a given channel and step-size, there is an optimal length for an equalizer to minimize the intersymbol interference. This result implies that a longer-length blind equalizer does not necessarily outperform a shorter one, as contrary to what conventionally conjectured. The theoretical analysis results are confirmed by computer simulations.
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    On the Convergence of Blind Channel Equalization
    (1995) Li, Ye; Liu, K.J. Ray; Ding, Zhi; ISR
    Baud-rate blind equalization algorithms may converge to undesirable stable equilibria due to different reasons. One is the use of FIR filter as an equalizer. It is proved in this paper that this kind of local minima exist for all blind equalization algorithms. The local minima generated by this mechanism are thus called unavoidable local minima. The other one is due to the cost function adopted by the blind algorithm itself, which has local minima even implemented with double infinite length equalizers. This type of local minima are called inherent local minima. It is also shown that the Godard algorithms [10] and standard cumulant algorithms [6] have no inherent local minimum. However, other algorithms, such as the decision-directed equalizer and the Stop- and-Go algorithm [17], have inherent local minima. This paper also studies the convergence of the Godard algorithms [10] and standard cumulant algorithms [6] under Gaussian noise, and derives the mean square error of the equalizer at the global minimum point. The analysis results are confirmed by computer simulations.
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    A Super-Resolution Parameter Estimation Algorithm for Multi- Dimensional NMR Spectroscopy
    (1995) Li, Ye; Razavilar, J.; Liu, K.J. Ray; ISR
    In this paper, we will propose a super-resolution scheme for the parameter estimation of multi-dimensional (M-D) NMR spectroscopy. M-D NMR signals can be modeled as the summation of M-D damped sinusoids. The frequencies and the damping factors of M-D damped sinusoids play important roles in protein structure determination using M-D NMR spectroscopy. We will develop a super-resolution frequency and damping factor estimation algorithm-damped MUSIC (DMUSIC) algorithm. Since the DMUSIC algorithm makes full use of the rank-deficiency and the Hankel property of the data matrix composed of the M-D NMR data, compared with other NMR data analysis algorithms, it can resolve the spectrum using very few data points. The performance of the DMUSIC algorithm is demonstrated by computer simulations.
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    Improved Parameter Estimation Schemes for Damped Sinusoidal Signals Based on Low-Rank Hankel Approximation
    (1995) Li, Ye; Liu, K.J. Ray; Razavilar, J.; ISR
    The parameter estimation of damped sinusoidal signals is an important issue in spectral analysis and many applications. The existing algorithms, such as the KT algorithms [8] and the TLS algorithms [13], are based on the low-rank approximation of prediction matrix, which ignores the Hankel property of the prediction matrix. We will prove in this paper that the performance of parameter estimation can be improved if both rank- deficient and Hankel properties of the prediction matrix are exploited in the matrix approximation. Based on this idea, a modified KT (MKT) algorithm and a super resolution algorithm- damped MUSIC (DMUSIC) algorithm are proposed. Computer simulation results demonstrate that, compared with the original KT algorithm, the MKT and MUSIC algorithms have about 5dB lower noise threshold and can estimate the parameters of signal with larger damping factors.