Two Methods for Decreasing the Computational Complexity of the MIMO ML Decoder

TL;DR

This paper introduces two methods, QR factorization with sort and Dijkstra's algorithm, to significantly reduce the computational complexity of the MIMO sphere decoder for maximum likelihood detection.

Contribution

It presents novel combinations of QR factorization with sort and Dijkstra's algorithm to improve decoding efficiency in MIMO systems.

Findings

Complexity reduction demonstrated through computer simulations.

QR factorization with sort slightly increases preprocessing complexity.

Dijkstra's algorithm reduces search complexity at the cost of higher storage requirements.

Abstract

We propose use of QR factorization with sort and Dijkstra's algorithm for decreasing the computational complexity of the sphere decoder that is used for ML detection of signals on the multi-antenna fading channel. QR factorization with sort decreases the complexity of searching part of the decoder with small increase in the complexity required for preprocessing part of the decoder. Dijkstra's algorithm decreases the complexity of searching part of the decoder with increase in the storage complexity. The computer simulation demonstrates that the complexity of the decoder is reduced by the proposed methods significantly.