Interpolation-Based QR Decomposition in MIMO-OFDM Systems

TL;DR

This paper introduces interpolation-based QR decomposition algorithms for MIMO-OFDM systems, significantly reducing computational complexity by exploiting the polynomial structure of channel matrices, especially for large numbers of OFDM tones.

Contribution

The paper proposes novel interpolation-based QR decomposition algorithms tailored for MIMO-OFDM systems, offering a complexity reduction over traditional methods for large-scale OFDM systems.

Findings

Algorithms achieve lower complexity than brute-force methods.

Complexity reduction is significant for high number of OFDM tones.

Performance depends on channel polynomial order and number of tones.

Abstract

Detection algorithms for multiple-input multiple-output (MIMO) wireless systems based on orthogonal frequency-division multiplexing (OFDM) typically require the computation of a QR decomposition for each of the data-carrying OFDM tones. The resulting computational complexity will, in general, be significant, as the number of data-carrying tones ranges from 48 (as in the IEEE 802.11a/g standards) to 1728 (as in the IEEE 802.16e standard). Motivated by the fact that the channel matrices arising in MIMO-OFDM systems are highly oversampled polynomial matrices, we formulate interpolation-based QR decomposition algorithms. An in-depth complexity analysis, based on a metric relevant for very large scale integration (VLSI) implementations, shows that the proposed algorithms, for sufficiently high number of data-carrying tones and sufficiently small channel order, provably exhibit significantly smaller complexity than brute-force per-tone QR decomposition.