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The authors propose to design a Low-Power Memory-Reduced Traceback MAP iterative decoding of Convolutional Turbo Code (CTC) which has large data access with large memories consumption and verify the functionality by using simulation tool. The traceback Maximum a Posteriori Algorithm (MAP) decoding provides the best performance in terms of Bit Error Rate (BER) and reduce the power consumption of the State Metric Cache (SMC) without losing the correction performance. The computation and accessing of different metrics reduce the size of the SMC with no requires complicated reversion checker, path selection, and reversion flag cache.
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