Abstract
Multiple-input multiple-output (MIMO) systems hold the potential to drastically improve the spectral efficiency and link reliability in future wireless communications systems. A particularly promising candidate for next-generation fixed and mobile wireless systems is the combination of MIMO technology with Orthogonal Frequency Division Multiplexing (OFDM). OFDM has become the standard method because of its advantages over single carrier modulation schemes on multipath, frequency selective fading channels. Doppler frequency shifts are expected in fast-moving environments, causing the channel to vary in time, that degrades the performance of OFDM systems. In this paper, we present a time-varying channel modeling and estimation method based on the Discrete Evolutionary Transform to obtain a complete characterization of MIMO-OFDM channels. Performance of the proposed method is evaluated and compared on different levels of channel noise and Doppler frequency shifts.
Highlights
The major challenges in future wireless communications systems are increased spectral efficiency and improved link reliability
In recent years the use of spatial diversity has become very popular, which is mostly due to the fact that it can be provided without loss in spectral efficiency
Intercarrier interference (ICI) due to Doppler shifts, phase offset, local oscillator frequency shifts, and multi-path fading severely degrades the performance of Orthogonal frequency division multiplexing (OFDM) systems [10]
Summary
The major challenges in future wireless communications systems are increased spectral efficiency and improved link reliability. In recent years the use of spatial (or antenna) diversity has become very popular, which is mostly due to the fact that it can be provided without loss in spectral efficiency. We present a time-varying MIMO-OFDM channel estimation based on the discrete evolutionary representation of the channel output. The Discrete Evolutionary Transform (DET) [13] provides a time-frequency representation of the received signal by means of which the spreading function of the multipath, fading, and frequency selective channel can be modeled and estimated. A time-frequency receiver is given in Section 3 for the detection of data symbols using estimated channel parameters.
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