Computational complexity reduction for MIMO-OFDM channel estimation algorithms

Abstract

Channel estimation algorithms have a key role in signal detection in MIMO-OFDM systems. In this system, the number of channel components which need to be estimated is much more than conventional SISO wireless systems. Consequently, the computational process of channel estimation is highly intensive. In addition, the high performance channel estimation algorithms mostly suffer from high computational complexity. In the other words, the system undergoes intensive computations if high performance efficiency is desired. However, there is an alternative solution to achieve both high performance efficiency and relatively low level of computational complexity. In this solution, high efficient channel estimation is firstly designed, and then it is simplified using alternative mathematical expressions. In this research, QR decomposition (QRD) as an alternative mathematical expression to alleviate the computational complexity of those complex algorithms which need matrix inversion is investigated. Herein, the channel estimation algorithm which is targeted to simplify is Least Square (LS) method. The results show QR decomposition can greatly reduce the complexity of LS channel estimation. As an example, for particular scenario, it achieves reduction of computational complexity as much as 77% while it keeps the performance efficiency of the system at the same level.