Relaxed annihilation-reordering look-ahead QRD-RLS adaptive filters

Lijun Gao, Keshab K. Parhi, Jun Ma

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The optimum architecture design and mapping of QRD-RLS adaptive filters can be achieved through filter architecture selections, look-ahead transformations, and hierarchical pipelining/folding transformations. In this paper, a relaxed annihilation-reordering look-ahead (RARL) architecture is proposed, and shown to be more power and area efficient than pipelined processing architecture which was considered the most area efficient. The filters with this architecture are based on relaxed weight-update through filtering approximation, where a filter tap weight is updated upon arrival of every block of input data, and are speeded up with annihilation-reordering look-ahead transformation. As a result of the computational complexity reduction, this architecture does not change the iteration bound and filter clock frequency, and leads to speed up with linear increase in power consumption, while the pipelined processing architectures result in speedup with quadratic increase in power consumption. Upon hardware mapping, this architecture is also more advantageous to achieve low area designs. Two design examples are presented to illustrate mapping optimization using above transformations. These results are important for mapping designs onto ASICs, FPGAs or parallel computing machines. The results show significant improvements in throughput, power consumption and hardware requirement. It is also interesting to show through mathematics and simulations that the RARL QRD-RLS filters have no performance degradation in terms of convergence rate.

Original languageEnglish (US)
Pages (from-to)119-135
Number of pages17
JournalJournal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
Issue number2
StatePublished - Sep 2003

Bibliographical note

Funding Information:
∗This research was conducted while the authors worked on their Ph.D. degrees in University of Minnesota. It was supported by the Defense Advanced Research Project Agency under contract number DA/DABT63-96-C-0050 and by the Army Research Office under grant number DA/DAAG55-98-1-0315.


  • Beamformer
  • Filtering approximation
  • Givens rotation
  • Hardware mapping
  • High-speed
  • Low area
  • Low power
  • QRD-RLS adaptive filter
  • Relaxed annihilation-reordering look-ahead
  • Relaxed weight-update

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