Reliability-Based Hybrid ARQ

We propose a new hybrid ARQ scheme that utilizes reliability estimates generated by soft-input, soft-output (SISO) decoders. The ARQ scheme that we propose transmits additional information for the unreliable bits, and this information is used to do additional decoding. We present results that show this ARQ technique can provide performance close to the channel capacity. SISO decoders can be used with many modern error-control coding schemes. These decoders typically accept estimates of the a priori probabilities and output estimates of the a posteriori probabilities (APPs) for the message bits. For the class of codes on graphs, suboptimal iterative decoding schemes are used that approximate an APP decoder. In this paper, we present results for a modified turbo coding scheme that uses an iterative SISO decoder. However, the results apply to iterative decoding of many other codes. The code we use has an extremely low error floor, so the performance is limited by the failure of the decoder to converge to the maximumlikelihood (ML) solution. Decoder convergence can be improved if additional information is provided for some subset of bits for which the decoder produces poor estimates. The reliabilitybased hybrid ARQ (RB-HARQ) scheme that we propose provides additional information for the bits that are determined to be unreliable in the iterative decoding process. The RB-HARQ technique is motivated by an understanding of the limitations of iterative decoding. At high Eb/N0 the performance of turbo codes with iterative decoding is the same as the ML decoder. However, at lower Eb/N0, the performance is limited by the failure of the decoder to converge to the ML solution. This decoder failure is linked to fixed points of the decoding algorithm [1]. We have observed that the fixed points of the iterative decoding algorithm are usually linked to a small subset of bits, which we refer to as weak bits. The weak bits are information bits that are involved in preventing the iterative decoding algorithm from converging to the correct This work was supported by the DoD Multidisciplinary University Research Initiative (MURI) program administered by the Office of Naval Research under Grant N00014-00-1-0565. This work was sponsored by the Department of the Air Force under Contract F19628-00-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Air Force.