This thesis investigates reliable data communication techniques for wireless channels. The problem of data detection at the receiver is considered and several novel detectors and parameter estimators are presented.¶
It is shown that by using a noise-limiting prefilter, with a spectral support at least equal to the signal part of the received signal, and sampling its output at the Nyquist rate, a set of sufficient statistics for maximum likelihood sequence detection (MLSD) is obtained.¶
Observing that the time-variations of the multipaths in a wireless channel are
bandlimited, channel taps are closely approximated as polynomials in time. Using this representation, detection techniques for frequency-flat and frequency-selective channels are obtained. The proposed polynomial predictor based sequence
detector (PPSD) for frequency-flat channels is similar in structure to the MLSD that employs channel prediction. However, the PPSD uses {\em a priori} known
polynomial based predictor taps. It is observed that the PPSD, without any explicit knowledge of the channel autocovariance, performs close to the Innovations based MLSD.¶
New techniques for frequency-selective channel estimation are presented. They are based on a rectangular windowed least squares algorithm, and they employ a
polynomial model of the channel taps. A recursive form of the least squares algorithm with orthonormal polynomial basis vectors is developed. Given the appropriate window size and polynomial model order, the proposed method outperforms the conventional least mean squares (LMS) and the exponentially weighted recursive least squares (EW-RLS) algorithms. Novel algorithms are proposed to obtain near optimal window size and polynomial model order.¶
The improved channel estimation techniques developed for frequency-selective channels are incorporated into sliding window and fixed block channel estimators. The sliding window estimator uses received samples over a time window to calculate the channel taps. Every symbol period, the window is moved along another symbol period and a new estimate is calculated. A fixed block estimator uses all
received samples to estimate the channel taps throughout a data packet, all at once. In fast fading and at a high signal-to-noise ratio (SNR), both techniques
outperform the MLSD receivers which employ the LMS algorithm for channel estimation.¶
An adaptive multiuser detector, optimal in the weighted least squares (WLS) sense, is derived for direct sequence code division multiple access (DS-CDMA) systems. In a multicellular configuration, this detector jointly detects the users within the cell of interest, while suppressing the intercell interferers in a WLS sense. In the absence of intercell interferers, the detector reduces to the well-known multiuser MLSD structure that employs a bank of matched filters. The relationship between the proposed detector and a centralized decision feedback detector is derived. The effects of narrowband interference are investigated and compared with the multiuser MLSD.¶
Since in a fast time-varying channel, the LMS or the EW-RLS algorithms cannot track the channel variations effectively, the receiver structures proposed for single user communications are extended to multiuser DS-CDMA systems. The fractionally-chip-spaced channel taps of the convolution of the chip waveform with the multipath channel are estimated. Linear equalizer, decision feedback equalizer
and MLSDs are studied, and under fast fading, as the SNR increases, they are found to outperform the LMS based adaptive minimum mean squared error (MMSE) linear receivers.
| Identifer | oai:union.ndltd.org:ADTP/216785 |
| Date | January 2000 |
| Creators | Borah, Deva Kanta, dborah@nmsu.edu |
| Publisher | The Australian National University. Research School of Information Sciences and Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.anu.edu.au/legal/copyrit.html), Copyright Deva Kanta Borah |
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