Iterative correction of intersymbol interference: Turbo‐equalization

Abstract

This paper presents a receiving scheme intended to combat the detrimental effects of intersymbol interference for digital transmissions protected by convolutional codes. The receiver performs two successive soft‐output decisions, achieved by a symbol detector and a channel decoder, through an iterative process. At each iteration, extrinsic information is extracted from the detection and decoding steps and is then used at the next iteration as in turbo‐decoding. From the implementation point of view, the receiver can be structured in a modular way and its performance, in bit error rate terms, is directly related to the number of modules used. Simulation results are presented for transmissions on Gauss and Rayleigh channels. The results obtained show that turbo‐equalization manages to overcome multipath effects, totally on Gauss channels, and partially but still satisfactorily on Rayleigh channels.

Turbo Equalization: An Overview

“…Such performance gains can be anticipated for any communication link in which joint equalization and decoding would outperform separate detection and decoding, such as would be the case for intersymbol interference channels employing a single-carrier modulation. This paper is organized as follows: Section I begins with an overview of turbo equalization and shows some of the many applications and extensions to the original formulation of [2] that have arisen over the last decade and a half. For ease of description, Section II provides an overview of turbo equalization as applied to systems using BPSK modulation, and Section III applies these developments to more general QAM alphabets.…”

“…Over the last decade and a half, this joint, iterative approach has been applied to a vast array of problems that involve the use of an error control code to aid in the processing of another related task in the receive chain of a digital communication link. Turbo equalization systems were first proposed in [2], by noting that the transmitter in Fig. 1 can be viewed as employing a serially concatenated convolutional code (turbo code), where the inner code is taken over the reals (by means of the ISI channel), and further developed in a number of articles, including [27]- [30].…”

“…1 can be viewed as employing a serially concatenated convolutional code (turbo code), where the inner code is taken over the reals (by means of the ISI channel), and further developed in a number of articles, including [27]- [30]. In some of these systems, MAP-based techniques, as well as a Viterbi algorithm producing soft output information [31], were used for both equalization and decoding [2], [27]. The more complex BCJR algorithm [7] was implemented in [27].…”

“…The approaches in [29], [30], [39]- [41] address a major shortcoming of the classical turbo equalization scheme [2], [27], [28], which is the exponentially increasing complexity of the equalizer for channels with long memory or given a large signal alphabet. These replace the MAP equalizer with a linear or a decision feedback equalizer.…”

“…Similarly, the equalizer should communicate extrinsic soft information to the decoder computed using the observation and all except . This approach to passing extrinsic soft information between constituent algorithms was first proposed by [10] in the context of decoding turbo codes and has been extended to various concatenated communication systems such as coded data transmission over ISI channels [2], [27], [29], [39], where it is called turbo equalization.…”