Repeat-punctured superorthogonal convolutional turbo codes on awgn channels

Abstract

The performance of turbo codes has been shown to be near the theoretical limit in the additive while Gaussian noise channel. By using orthogonal signaling, which allows for bandwidth expansion, the performance of the turbo coding scheme can be improved even further. Since this is a low-rate code, the code is mainly suitable for spread-spectrum modulation applications. In classical turbo codes the frame length is set equal to the interleaver size, however, the codeword distance spectrum of turbo codes improves with an increasing interleaver size. It has been reported that by using repetition and puncturing the performance of turbo codes can be improved. Repeat-Punctured Turbo Codes has shown a significant increase in performance at moderate to high signal-to-noise ratios. In this paper, we study the use of orthogonal signaling and parallel concatenation together with repetition and puncturing to improve the performance of superorthogonal convolutional turbo codes for reliable and effective communications. Simulation results for the additive white Gaussian noise channel are presented together with analytical upper bounds, which have been derived using transfer function bounding techniques.