Dissertation/ Thesis
Iterative algorithms for channel estimation and equalization
| Title: | Iterative algorithms for channel estimation and equalization |
|---|---|
| Authors: | Yao, Ning |
| Publication Year: | 2005 |
| Collection: | The Hong Kong University of Science and Technology: HKUST Institutional Repository |
| Subject Terms: | Iterative methods (Mathematics) -- Data processing, Adaptive signal processing |
| Description: | Adaptive filtering is one of the most important techniques in signal processing for tracking the status of any time-varying system responses. With this property, adaptive filtering techniques have been widely used for channel estimation and equalization for several decades. In adaptive filtering algorithms, the new channel estimator or equalizer are usually updated based on the past results and the new incoming signals. The computational complexity and performance of this kind of recursive processes usually can be improved using the iterative techniques. In this thesis, I have proposed a set of novel iterative algorithms to estimate and/or equalize finite impulse response (FIR) single-input single-output (SISO), single-input multi-output (SIMO), and multi-input multi-output (MIMO) systems. An iterative channel estimation algorithm based on input and output signal correlation is proposed which is shown to be robust to instantaneous strong dis-turbance as well as time-varying correlative noise. This algorithm extended the cost function of the traditional RLS algorithm in correlation domain, such that the squared differences of the correlation function of the transmitted and received signals are considered in the adaptation. The channel adaptation performance is shown to be improved by smoothing the time-varying effects of the disturbance. The correlation-based iterative algorithm is a second-order statistics (SOS) based algorithm. I further extend this SOS algorithm from a non-blind SISO channel estimation to a blind SIMO channel estimation problem. An iterative subspace tracking algorithm is proposed. This algorithm eliminates the compu-tational intensiveness of eigenvalue decomposition process for each channel esti-mated by a least-mean-squares-alike subspace tracking algorithm. The simulation results showed that, compared to the traditional subspace method given in [44], the proposed algorithm, not only reduces the computational complexity, but also provides a comparable accuracy to that of the method in [44]. ... |
| Document Type: | thesis |
| Language: | English |
| Availability: | http://repository.hkust.edu.hk/ir/Record/1783.1-4727 https://repository.hkust.edu.hk/ir/bitstream/1783.1-4727/1/b863994.pdf https://repository.hkust.edu.hk/ir/bitstream/1783.1-4727/2/b863994_c.pdf |
| Accession Number: | edsbas.2E2F030B |
| Database: | BASE |
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