Interpolation-Based Matrix Arithmetics for MIMO-OFDM Systems

Authors

Davide Cescato

Reference

Ph.D. dissertation, ETH Zurich, Switzerland, Series in Communication Theory, vol. 7, Hartung-Gorre Verlag Konstanz, Dec. 2010.

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Abstract

Detection algorithms for multiple-input multiple-output (MIMO) wireless communication systems based on orthogonal frequency-division multiplexing (OFDM) require channel preprocessing through the computation of a matrix inversion or factorization for each of the data-carrying OFDM tones. Motivated by the fact that the channel matrices arising in MIMO-OFDM systems result from oversampling of a polynomial matrix, we propose interpolation-based algorithms that solve the general problem of arithmetics of Laurent polynomial matrices sampled on the unit circle, both for generic arithmetics and for the special cases, relevant for MIMO-OFDM detection, of regularized matrix inversion, (regularized) QR decomposition, and LU decomposition. An in-depth complexity analysis, based on a metric relevant for very large scale integration (VLSI) implementations, shows that the proposed algorithms can exhibit significantly smaller complexity than brute-force per-tone MIMO-OFDM preprocessing.

Keywords

multiple-input multiple-output (MIMO) systems, orthogonal frequency-division multiplexing (OFDM), matrix inversion, QR decomposition, LU decomposition, Laurent polynomial matrices, interpolation, very large scale integration (VLSI)

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