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Unifying Performance Metric of Viterbi Decoders

Convolutional codes and Viterbi decoders were extensively used in error control systems. The survivor memory management (SMM) unit of Viterbi decoder is extremely important in determining the throughput, hardware area and coding gain performance of the whole system. Many SMM architectures were proposed in the past, but we lack an unifying metric to compare the coding gain performance of them. In this thesis, we define a metric, average traceback depth (ATBD), to unify the diversity of different SMM architectures. The ATBD metric can be used to equalize different SMM architectures and predict the optimal traceback depth (TBD) of them. The optimality is in terms of coding gain performance and hardware cost. We perform extensive computer simulations with three popular convolutional codes (DVB, DCII and UMTS) and many SMM architectures to verify the validity of the ATBD metric. Simulation results show that the difference between optimal TBD and ATBD is at most 10%. With this unifying metric, we can estimate the hardware cost of different SMM architectures under fixed coding gain performance. Besides, system architects can use it to fast evaluate the tradeoff among hardware cost, throughput and coding gain performance because the calculation of ATBD metric is very simple.