Adaptive and Linear Prediction Channel Tracking Algorithms for Mobile OFDM-MIMO Applications

Abstract

This paper presents low overhead channel tracking algorithms for mobile orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) communication systems. In this paper we explore the use of period training for channel estimation used in conjunction with adaptive decision-feedback LMS, RLS and Kalman filter with linear prediction. In this paper, we show that the linear prediction algorithm can substantially improve the performance of adaptive tracking methods. MIMO communication systems typically require knowledge of the channel state information, however, mobile communication systems exhibit a time and frequency-varying channel matrix. Consequently, channel tracking methods are required to accurately estimate the channel. This paper presents results of V-BLAST (Vertical Bell Laboratories Layered Space-Time) MIMO simulations using the geometric wide-band time-varying channel model (GWTCM) with Rayleigh faded environments. Flat fading is assumed for each OFDM subcarrier. Results indicate that robust channel tracking for OFDM-MIMO applications can be improved by using adaptive methods with linear prediction techniques. OFDM-MIMO architectures such as OFDM coupled with V-BLAST can be easily implemented by exploiting the built-in and flexible multi-channel architectures of advanced software defined radios (SDR)