A Comparison of Some Trellis- and Tree-Based SISO Algorithms for Decoding and Equalization

This paper reviews soft-input soft-output (SISO) algorithms that have been recently proposed as reduced-complexity alternatives to maximum a posteriori (MAP) trellis-based decoding or equalization. The M*-BCJR, LISS, and BEAST algorithms are chosen as representatives of reduced-state trellis- and tree- search procedures. The former two algorithms have been initially developed for intersymbol interference (ISI) equalization, while BEAST is tailored for decoding. All three algorithms are modified for either application and their advantages and shortcomings are studied for each case. Comparisons in terms of performance and complexity are presented.