| The design of any communication receiver needs to addresses the issues of operating under the lowest possible signal-to-noise ratio. Among various algorithms that facilitate this objective are those used for iterative decoding of two-dimensional systematic convolutional codes in applications such as spread spectrum communications and Code Division Multiple Access (CDMA) detection. A main theme of any decoding schemes is to approach the Shannon limit in signal-to-noise ratio. All these decoding algorithms have various complexity levels and processing delay issues. Hence, the optimality depends on how they are used in the system. The technique used in various decoding algorithms is termed as iterative decoding. Iterative decoding was first developed as a practical means for decoding turbo codes. With the Log-Likelihood algebra, it is shown that a decoder can be developed that accepts soft inputs as a priori information and delivers soft outputs consisting of channel information, a posteriori information and extrinsic information to subsequent stages of iteration.; Different algorithms such as Soft Output Viterbi Algorithm (SOVA), Maximum A Posteriori (MAP), and Log-MAP are compared and their complexities are analyzed in this thesis. A turbo decoder is implemented on the Digital Signal Processing (DSP) chip, TMS320C30 by Texas Instruments using a Modified-Log-MAP algorithm. For the Modified-Log-MAP-Algorithm, the optimal choice of the lookup table (LUT) is analyzed by experimenting with different LUT approximations. A low complexity decoder is proposed for a (7,5) code and implemented in the DSP chip. Performance of the decoder is verified under the Additive Wide Gaussian Noise (AWGN) environment. Hardware issues such as memory requirements and processing time are addressed for the chosen decoding scheme. Test results of the bit error rate (BER) performance are presented for a fixed number of frames and iterations. |
