Due to the complexity of the a posteriori probability computation,
the solution of the MAP detection problem cannot
be obtained directly. As a result, a stochastic search method
such as the simulated annealing (SA) algorithm [7] is employed.
Here, the SA algorithm generates a random sequence of CIs
in which a new configuration depends only on the previous CI
and observed images by using the Gibbs sampling procedure
[5]–[8]. The randomness of a new CI gradually decreases as the
number of iterations increases. Eventually, this sequence of CIs
converges to the solution of the MAP detector.
This paper is organized as follows. In Section II, the problem
is formulated, and the above assumptions are restated in a more
formal manner. The optimum ICD algorithm under the MAP
criterion is developed in Section III. Section IV provides some
examples for performance evaluation and illustration. A summary
is presented in Section V.
Due to the complexity of the a posteriori probability computation,the solution of the MAP detection problem cannotbe obtained directly. As a result, a stochastic search methodsuch as the simulated annealing (SA) algorithm [7] is employed.Here, the SA algorithm generates a random sequence of CIsin which a new configuration depends only on the previous CIand observed images by using the Gibbs sampling procedure[5]–[8]. The randomness of a new CI gradually decreases as thenumber of iterations increases. Eventually, this sequence of CIsconverges to the solution of the MAP detector.This paper is organized as follows. In Section II, the problemis formulated, and the above assumptions are restated in a moreformal manner. The optimum ICD algorithm under the MAPcriterion is developed in Section III. Section IV provides someexamples for performance evaluation and illustration. A summaryis presented in Section V.
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