Abstract
In this paper, normalized least mean (NLMS) square and recursive least squares (RLS) adaptive channel estimator are described for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. These CE methods uses adaptive estimator which are able to update parameters of the estimator continuously, so that the knowledge of channel and noise statistics are not necessary. This NLMS/RLS CE algorithm requires knowledge of the received signal only. Simulation results demonstrated that the RLS CE method has better performances compared NLMS CE method for MIMO OFDM systems. In addition, the utilizing of more multiple antennas at the transmitter and/or receiver provides a much higher performance compared with fewer antennas. Furthermore, the RLS CE algorithm provides faster convergence rate compared to NLMS CE method. Therefore, in order to combat the more channel dynamics, the RLS CE algorithm is better to use for MIMO OFDM systems.
Highlights
Multiple input multiple output (MIMO) channels have been introduced to achieve high data speed requisite by the next-generation communication systems [1]
One can observed that the recursive least squares (RLS) channel estimation (CE) method has better performances compared NLMS CE method
NLMS and RLS adaptive channel estimator are described for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems
Summary
Multiple input multiple output (MIMO) channels have been introduced to achieve high data speed requisite by the next-generation communication systems [1]. In SISO flat channels, channel estimation (CE) and its precision do not have a drastic impact on the performance of the receiver. Whereas in outdoor MIMO channels, the precision and speed of convergence of the channel estimator can drastically affect the performance of the receiver [3]. In SISO communications, the channel estimators may or may not use the training sequence or not. In full-rank MIMO channels, the use of an initial training data is mandatory, and without it, the channel estimator does not converge [2], [5]
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