Convergence of stochastic-approximation-based algorithms for blind channel identification
Abstract
We develop adaptive algorithms for multichannel (single-input-multiple-output, or SIMO) blind identification with both statistic and deterministic models. In these algorithms, the estimates are continuously improved while receiving new signals. Therefore, the algorithms can track the channel continuously and thus are amenable to real applications such as wireless communications. At each step, only a small amount of computation is involved. The algorithms are based on stochastic-approximation methods. The convergence properties of these algorithms are proved. Simulation examples are presented to show the performance of the algorithms.
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The two channel impulse responses h0 and h1 can be uniquely determined (up to a scale and a common delay) from the covariance relation if the polynomials formed from h0 and h1 are coprime and the autocorrelation matrix of the source signal is of full rank.
“It has been shown that the two channel impulse responses (i.e., h 0 and h 1) can be uniquely determined (up to a scale and a common delay) from (32) if the following two conditions hold [56-58]:”
From Time Delay Estimation in Room Acoustic Environments: An Overview (2006) · cites this paper as [57]
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References 17 · Cited by 19
- Authors
- Chen, H.-F., Cao, X.-R., & Zhu, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Hong Kong University of Science and Technology (HK)
- Nanyang Technological University (SG)
- Venue
- IEEE Transactions on Information Theory
- Published
- 2002-05
- Volume
- 48
- Issue
- 5
- Pages
- 1214-1225
- ISSN
- 0018-9448 (ISSN-L)
- OpenAlex
- W2097566063
- Cited by
- 19
- References
- 17
- Citations by year
- 2021: 1 · 2017: 1 · 2014: 4 · 2013: 1 · 2012: 1
- FWCI
- 1.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Theory (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Advanced Adaptive Filtering Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
- Keywords
- blind channel estimation, stochastic approximation, convergence analysis, adaptive algorithms, SIMO systems, channel tracking, wireless communication, statistical channel model, deterministic channel model
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