Convolutional Coupled Codes

This thesis is about convolutional coupled codes - codes constructed via concatenation of several outer systematic convolutional encoders and several inner systematic block encoders linked by divers interleavers. The code is nonsystematic, since only the redundancy produced from the outer and inner encoders is transmitted. The focus of the work is on the understanding and design of convolutional coupled codes. This includes thorough investigation of central components that influence convolutional coupled code performance, such as the constituent encoders, as well as the procedure of iterative decoding. The investigations are carried out for transmission on additive white Gaussian noise channels. Two aspects that influence the performance of convolutional coupled codes are considered: (1) code properties, in terms of effective free distance, and (2) decoding properties, in terms of the performance of the iterative decoding. It is asserted that both these aspects are influenced by the choice of the inner and outer constituent encoders. Guidelines for the choice of constituent encoders are outlined. It is demonstrated that the “error floor” convolutional coupled codes are claimed to suffer from at medium- to high signal to noise ratios can be significantly lowered by proper chose of the inner encoding matrix. The aspect of convergence behavior of the iterative decoding is investigated. The influence of code memory, code polynomials as well as different inner codes on the convergence behavior is studied. Inside this thesis we show that convolutional coupled codes have potential of being a realistic alternative to other concatenated schemes especially Turbo codes