Performance of the a priori and a posteriori QR-LSL algorithms in a limited precision environment

The performance of two minimal QR-LSL algorithms in a low precision environment is investigated. For both algorithms backward consistency and backward stability become guaranteed under simple numerical conventions. They present stable behaviour even when excited with ill conditioned signals such as predictable signals. Since the problem of ensuring numerical stability is solved for these algorithms, an investigation about their accuracy is in place. By simulating a channel equalizer configuration it is shown that, for small mantissa wordlengths and forgetting factors λ not too close to 1, the a priori algorithm performs better due to its dispensing with passive rotations. For forgetting factors very close to one and small wordlengths, both algorithms are sensitive to the accuracy of some well-identified computations. They are compared to an LSL algorithm, based on a priori prediction errors, whose good performance in limited precision environments is known