Channel and Data Estimation for Ad Hoc Networks and Cognitive Radio |
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Authors: | Larry N Singh Galigekere R Dattatreya |
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Institution: | (1) School of Medicine, University of Pennsylvania, Philadelphia, PA, USA;(2) Department of Computer Science, The University of Texas at Dallas, MS EC 3.1, 2601 North Floyd Road, 830688, Richardson, TX, 75083-0688, USA |
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Abstract: | Estimation of channel and data characteristics by the receiver is important in adaptive wireless transmission protocols and
in cognitive radio. This paper formulates the estimation problem with the help of an illustrative example from the IEEE 802.11a
OFDM standard. The problem reduces to the estimation of the common component variance and mixing probabilities in a finite
Gaussian mixture, with known values for component means. Using the known component means, μ1, ... , μ
M
, a set of non-linear transformations, and of the data (mixture random variable X) are used to develop convergent and computationally efficient estimators for both the noise variance and the vector of symbol
probabilities. The estimation equations can be implemented recursively or with a batch processing algorithm. Asymptotic variances
of the estimates and the Cramer–Rao minimum variance bounds are derived. The estimates converge to true unknowns even when
the sequences of noise and data symbols are dependent sequences. The OFDM example is simulated with parameters corresponding
to the highest acceptable error rate. For a time-varying channel model chosen from the literature, it is shown that our estimator
receives considerably more than adequate amount of data during an average time interval of unchanging channel characteristics.
Analytical results, numerical results and related issues are discussed. |
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Keywords: | Ad hoc networks cognitive radio channel and data estimation adaptive wireless transmission asymptotic variance |
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