| Summary: | 碩士 === 國立交通大學 === 電信工程系 === 89 === It is known that the error correction capability of the convolutional codes grows dramatically as the code constraint length increases. Yet, to employ codes with long constraint length may suffer a high decoding complexity. In [1], the authors proposed the Maximum-Likelihood soft-decision Sequential Decoding Algorithm (MLSDA) [1] for binary convolutional codes, and showed that its computational complexity turns out to be less affected by the code constraint length; therefore, it may apply to convolutional codes with long constraint lengths, and yield a good system performance. In order to evaluate the resultant system performance, sufficient simulation runs, usually requiring to induce hundred of errors, should be taken. This may render an unfeasibly long simulation time if the true system error is indeed low.
Unlike the brute-force it Monte Carlo (MC) simulation that often requires a very large number of simulation trials to achieve meaningful estimates of system performance, the Importance Sampling (IS) [2,3,4,7] simulation can achieve relatively accurate estimate with much less simulation trials. For IS technique, well-chosen channels to adapt to suitable error events can result in an efficient simulator.
In this thesis, we focused on the selection of good biased channels for Importance Sampling being applied to the MLSDA. By the simulation results, the best IS simulator among those we tried can save fairly large simulation trials, while an improperly chosen IS density may arise an IS simulator that performs even worse than an MC one.
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