On the design of turbo codes with convolutional interleavers

Random interleavers are amongst the most effective interleavers for turbo codes. However, due to their random permutations, a compact representation of the code specification is a major obstacle. Thus, to date, much research has been conducted on the design of deterministic interleavers having performances close to random interleavers. These interleavers are mainly constructed as block interleavers, which allows the code to be analyzed as a block code. In contrast to block interleavers, there are non-block interleavers. These utilize a reduced number of memories in their structures and have self-synchronization with their deinterleavers; this simplifies their design. Because of their non-block structures, turbo codes constructed by these interleavers must usually be analyzed in terms of the continuous performance. Previous research confirms that the codes’ continuous performance is similar to their block performance, but at the expense of increased complexity of the code analysis and decoding. In order to analyze a turbo code constructed with non-block interleavers as a block code, it is necessary to consider the applied interleavers as block interleavers. This is accomplished by the insertion of stuff bits at the end of each input data block, returning the interleaver memories to zero state. This thesis is related to the application of convolutional interleavers which are the most popular non-block interleavers for turbo codes. It introduces convolutional interleavers as good deterministic interleavers that can perform similar or even better than previous deterministic and random interleavers. The thesis presents two different structures of block-wise convolutional interleavers, created on the basis of distribution of stuff bits in the interleaved data. On the basis of convolutional interleaver

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