Abstract
We model the density of extrinsic information in iterative turbo decoders by Gaussian density functions. This model is verified by experimental measurements. We consider evolution of these density functions through the iterative turbo decoder as a nonlinear dynamical system with feedback. Iterative decoding of turbo codes and of serially concatenated codes are analyzed based on this method. We define a for the iterative decoder, such that the turbo decoder will converge to the correct codeword if the noise figure is bounded below 0 dB. Many mysteries of turbo codes can be explained based on this analysis. For example we can explain why certain codes converge better with iterative decoding than more powerful codes which are only suitable for maximum likelihood decoding. The roles of systematic bits and of recursive convolutional codes as constituents of turbo codes are explained based on this analysis.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
