A Low Power Algorithm for Sparse System Identification using Cross-Correlation

Authors:
Finbarr O'Regan;Conor Heneghan
Affiliations:
Department of Electronic and Electrical Engineering, University College Dublin, Belfield, Dublin 4, IRELAND;Department of Electronic and Electrical Engineering, University College Dublin, Belfield, Dublin 4, IRELAND
Venue:
Journal of VLSI Signal Processing Systems
Year:
2005

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Abstract

In this paper we present a low power system identification algorithm suitable for echo and crosstalk cancellation in data communications. Crosstalk and echo channels tend to be sparse systems i.e. many taps are negligible with just a few taps significant or active. The proposed adaptive algorithm, called the sparse cross-correlation (SCC) algorithm is designed to exploit the sparsity of crosstalk and echo channels in order to lower the power consumption in the circuit implementation when compared to the normalized least mean squares (NLMS) algorithm.Mathematical analysis of the mean square error suggests that zeroing insignificant taps has a relatively small impact on the algorithm performance. This motivates the SCC algorithm, which uses a cross-correlation to identify the active taps in an unknown system and control circuitry to allocate cancellation hardware at these active tap lags. In the case of sparse systems and signal to noise ratios (SNR) of 10 dB or less, we found that the SCC algorithm can do up to 2 dB better than the NLMS algorithm in terms of the achievable mean square error floor.A detailed hardware implementation of this algorithm is also presented and compared to the benchmark NLMS algorithm in terms of area, critical path and power consumption. Results indicate that a power saving of up to 40% can be achieved at a realistic canceller length of 64 taps while maintaining algorithm performance.