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Methods to reduce memory requirements in turbo decoding

Since the introduction of turbo codes in 1993, researchers have turned their attention to the design, performance evaluation and practical application of these codes. The practical implementation of turbo decoding algorithms faces two major challenges: (1) heavy computations for the α and the β values, and (2) huge memory requirement. Using powerful fast processors, which are in hand nowadays, one can somehow compensate the former. However, incorporating a large amount of memory in the practical turbo code devices is still a problem. Hence, it is desirable to find methods to reduce the memory requirement in the turbo decoding algorithm. In this work, two novel approaches are investigated to reduce the memory usage in turbo decoding: (1) α-inverse matrix calculation, and (2) proper quantization of α and β values.