Objective
Research objectives and content
Channel equalization is very important for reliable high-speed digital communications. The recent development of second order statistics based blind identification/equalization algorithms has been considered as a major breakthrough and 1 created intense research interest in the area. While many methods can claim exact channel identification/equalization in the noiseless case, under the so-called zero forcing conditions. not much is known about their behavior under less id, conditions, like for example a) additive channel noise, b) channel undermodeling. The robustness of blind identification/cqualization methods with respect to channel noise and/or channel undermodeling is a very critical property, directly related to their applicability in 'real world' scenarios.
Our purpose is to study the behavlor of second order statistics or implicitly higher order statistics based blind channel
identification/equalization methods (like, for example, the fractionally spaced Constant Modulus algorithm, the Subspace, the Least-squares and the Linear Prediction methods) in the noisy undermodeled cases. The author has studied the Subspace a the Least Squares methods in the noiseless two channel case. The results can be found in
A. P. Liavas and P. A. Regalia. ''()n the robustness of least squares and the subspace methods for blind channel identification/equalization with respect to channel undermodeling,''submitted for publication to the IEEE Trans Signal Processing., Nov. 1997.
However, further study is required for a lucid understanding of the behavior of the fractionally spaced constant modulus and the linear prediction algorithms.
Training content (objective, benefit and expected Impact)
The benefits of a deep understanding or the factors that determine the hehavior of blind identification/equalization methods in realistic scenarios are very important from both a theoretical and a practical point of view. Some of them are as follows: a) Anticipation of performance of existing methods under nonideal conditions b) Rigorous comparison of relative performance offerefd by the various methods. c) Isolation of ' weak''points of existing methods and, probably, replacement by more robust ones d) Development of new more robust methods.
Links with industry / industrial relevance (22)
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering signal processing
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Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
15771 Athens
Greece
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