Hybrid annihilation transformation (HAT) for pipelining QRD-based least-square adaptive filters

Abstract

A novel transformation, referred to as hybrid annihilation transformation (HAT), for pipelining the QR decomposition (QRD) based least square adaptive filters has been developed. HAT provides a unified framework for the derivation of high-throughput/low-power VLSI architectures of three kinds of QRD adaptive filters, namely, QRD recursive least-square (LS) adaptive filters, QRD LS lattice adaptive filters, and QRD multichannel LS lattice adaptive filters. In this paper, HAT is presented as a solution to break the bottleneck of a high-throughput implementation introduced by the inherent recursive computation in the QRD based adaptive filters. The most important feature of the proposed solution is that it does not introduce any approximation in the entire filtering process. Therefore, it causes no performance degradation no matter how deep the filter is pipelined. It allows a linear speedup in the throughput rate by a linear increase in hardware complexity. The sampling rate can be traded off for power reduction with lower supply voltage for applications where high-speed is not required. The proposed transformation is addressed both analytically, with mathematical proofs, and experimentally, with computer simulation results on its applications in wireless code division multiple access (CDMA) communications, conventional digital communications and multichannel linear predictions.