Pipelining Extended Givens Rotation RLS Adaptive Filters

نویسندگان

  • Shing Tenqchen
  • Ji-Horn Chang
  • Wu-Shiung Feng
  • Bor-Sheng Jeng
چکیده

In this paper, we propose a new pipelining extended Givens Rotation Recursive Least Square (PEGR-RLS) architecture using look-ahead technique. The square-root-free forms of QRD-RLS are also difficult to pipeline. The PEGR-RLS algorithm (referred to as Scaled Tangent Rotation, STAR-RLS) is designed such that fine-grain pipelining can be accomplished with little hardware overhead. Similar to STAR-RLS, this algorithm is not exactly orthogonal transformations but tends to become orthogonal asymptotically. This algorithm also preserves the desired properties of the STAR-RLS algorithm. Specifically, it can be pipelined at very low forgetting factor by using extended look-ahead. Simulation results are presented to compare the performance of the STAR-RLS, QRD-RLS, and LMS algorithms.

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تاریخ انتشار 2002