Systems Seminar

Dimension Reduction via Subspace Mapping:
When Less is More in Signal Processing

Prof. Barry Van Veen
UW ECE Department

Abstract

Reducing dimension by mapping data into an appropriately chosen subspace is a powerful signal processing principle for reducing computational complexity and improving adaptive performance. These benefits usually accrue at the expense of steady-state performance. The key issue in managing the computational complexity and performance tradeoffs is selection of the subspace. In general, computational complexity and adaptive performance depend primarily on the subspace dimension, so the subspace is chosen to minimize the steady-state performance loss. These principles are illustrated using examples from Volterra filtering, adaptive detection, and adaptive beamforming. The talk concludes by emphasizing recent work in subspace mapping for space-time code division multiple access based wireless communication systems. The subspace framework provides an ideal vehicle for satisfying the stringent performance and complexity requirements of anticipated high data rate mobile systems. An appropriate subspace for signal processing of the received signal is identified and tradeoffs between steady-state detection performance, computational complexity, and adaptive performance are examined.

Time and Place: Wed., Feb. 24, 3:30-4:30 pm in 4610 Engr. Hall.

SYSTEMS SEMINAR WEB PAGE: http://www.cae.wisc.edu/~gubner/seminar/