Adaptive iterative decoding : block turbo codes and multilevel codes

New adaptive, iterative approaches to the decoding of block Turbo codes and multilevel codes are developed. Block Turbo codes are considered as they can readily provide high data rates, low decoding complexity and good performance. Multilevel codes are considered as they provide a moderate complexity approach to a high complexity code and can provide codes with good bandwidth efficiency. The work develops two adaptive sub-optimal soft output decoding algorithms for block Turbo codes. One is based on approximation and the other on the distance properties of the component codes. They can be used with different codes, modulation schemes, channel conditions and in different applications without modification. Both approaches provide improved performance compared to previous approaches on the additive white Gaussian noise (AWGN) channel. The approximation based adaptive algorithm is also investigated on the uncorrelated Rayleigh fiat fading channel and is shown to improve performance over previous approaches. Multilevel codes are typically decoded using a multistage decoder (MSD) for complexity reasons. Each level passes hard decisions to subsequent levels. If the approximation based adaptive algorithm is used to decode component codes in a traditional MSD it improves performance significantly. Performance can be improved further by passing reliability (extrinsic) information to all previous and subsequent levels using an iterative MSD. A new iterative multistage decoding algorithm for multilevel codes is developed by treating the extrinsic information as a Gaussian random variable. If the adaptive algorithms are used in conjunction with iterative multistage decoding on the AWGN channel, then a significant improvement in performance is obtained compared to results using a traditional MSD

Turbo Decoding and Detection for Wireless Applications

A historical perspective of turbo coding and turbo transceivers inspired by the generic turbo principles is provided, as it evolved from Shannon’s visionary predictions. More specifically, we commence by discussing the turbo principles, which have been shown to be capable of performing close to Shannon’s capacity limit. We continue by reviewing the classic maximum a posteriori probability decoder. These discussions are followed by studying the effect of a range of system parameters in a systematic fashion, in order to gauge their performance ramifications. In the second part of this treatise, we focus our attention on the family of iterative receivers designed for wireless communication systems, which were partly inspired by the invention of turbo codes. More specifically, the family of iteratively detected joint coding and modulation schemes, turbo equalization, concatenated spacetime and channel coding arrangements, as well as multi-user detection and three-stage multimedia systems are highlighted

Self-concatenated code design and its application in power-efficient cooperative communications

In this tutorial, we have focused on the design of binary self-concatenated coding schemes with the help of EXtrinsic Information Transfer (EXIT) charts and Union bound analysis. The design methodology of future iteratively decoded self-concatenated aided cooperative communication schemes is presented. In doing so, we will identify the most important milestones in the area of channel coding, concatenated coding schemes and cooperative communication systems till date and suggest future research directions

Iterative decoding for MIMO channels via modified sphere decoding

In recent years, soft iterative decoding techniques have been shown to greatly improve the bit error rate performance of various communication systems. For multiantenna systems employing space-time codes, however, it is not clear what is the best way to obtain the soft information required of the iterative scheme with low complexity. In this paper, we propose a modification of the Fincke-Pohst (sphere decoding) algorithm to estimate the maximum a posteriori probability of the received symbol sequence. The new algorithm solves a nonlinear integer least squares problem and, over a wide range of rates and signal-to-noise ratios, has polynomial-time complexity. Performance of the algorithm, combined with convolutional, turbo, and low-density parity check codes, is demonstrated on several multiantenna channels. The results for systems that employ space-time modulation schemes seem to indicate that the best performing schemes are those that support the highest mutual information between the transmitted and received signals, rather than the best diversity gain