Small sample support adaptive mmse filtering for mimo ofdm systems

As the number of mobile devices has increased during the past decades the utilization of advanced multiple-access techniques for multiple-input-multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) technology can ensure better performance gain and allows mobile networks to achieve the latest wireless standards requirements for a large number of subscribers. The challenge addressed in this thesis is to compare the performances of least-mean-square (LMS), recursive-least-square (RLS) and auxiliary vector (AV) algorithms for the estimation of the ideal minimum-mean-square-error (MMSE)/minimum-variance-distortionless-response (MVDR) filter. The sample-inversion-matrix (SMI) estimator is added as a reference as well. The simulation results demonstrate that AV estimator outperforms LMS, RLS and SMI estimators in terms of bit-error-rate (BER). On one hand, for small input data records, in other words when the number of input sample is shorter than the length of the filter which implies the non-existence of SMI estimator, AV estimator provides significantly distinct BER performances from LMS and RLS estimators. Logically, BER increases with the number of users and decreases with a higher signal-noise-ratio (SNR) of the user of interest. One the other hand, for large input data records, both AV, LMS, RLS and SMI estimators have comparable BER performances even though AV estimator offers slightly better ones. Therefore, the use of AV algorithm in future wireless communication systems in general could provide significant improvements in order to achieve higher data rates.