IMPLEMENTATION OF ITERATIVE EQUALIZATION AND DECODING

Abstract

In iterative equalization and decoding the "turbo-principle" is used for iterative detection of coded data transmitted over a frequency selective channel. The decoding strategy used to decode convolutional codes is the basic of `turbo principle', which connotes the iterated exchange of soft information between different blocks of a communication receiver. The potential importance and applicability of this principle has been found to extend to a wide range of problems in communication. In iterative adaptive equalization and decoding, channel equalization and MAP decoding are jointly optimized in an iterative process. Furthermore, we present an alternate approach, called, iterative MAP equalization and MAP decoding, where we view the channel encoder and ISI-channel as a serially concatenated coding scheme and use MAP-algorithm for both equalization and decoding for first iteration. In this dissertation, simulation of iterative equalization and decoding has been carried out in MATLAB environment. The performance has been evaluated using LMS Algorithm for adaptive equalization and MAP algorithm for decoding. Finally, the performance is also evaluated for iterative MAP equalization and MAP decoding for the first iteration