Comparison of computationally efficient forms of FDTS/DF against PR4-ML

The authors compare the error-rate performance and circuit complexity of fixed-delay tree search with decision-feedback (FDTS/DF) to that of Class IV partial response equalization followed by Viterbi detection (PR4-ML). The impact of realistic impulse responses, misequalization, phase errors, and offtrack interference on both detection strategies is explored. Two computationally efficient methods for implementing FDTS/DF are presented. These FDTS/DF architectures avoid explicit multiplication-one uses a RAM-based table lookup and the other uses binary equalization over the tree-depth