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ON THE LEARNING BEHAVIOR OF THE DR-LS ALGORITHM

Adaptive algorithms with data-reuse, like the UNDRLMS algorithm, have recently received more attention due to their simple structure and their capability to improve the estimates by repeating the same operation. Since the basic operation is that of an LMS algorithm, it is a common belief that these algorithms converge towards the Wiener solution. In this paper, a data-reuse algorithm is analyzed that converges to the LS solution instead. The algorithm is similar to Kaczmarz’s row projection method, allows, however, un-normalized regression vectors and a wide range of (time-variant) step-sizes. Similar to the LMS algorithm when compared to the Wiener solution, this algorithm also results in a misadjustment proportional to its step-size. Various step-size control strategies are investigated to improve this misadjustment.