Abstract
OFDM combined with the MIMO technique has become a core and attractive technology in future wireless communication systems and can be used to both improve capacity and quality of mobile wireless systems. Accurate and efficient channel estimation plays a key role in MIMO-OFDM communication systems, which is typically realized by using pilot or training sequences by virtue of low complexity and considerable performance. In this paper, we discuss some methods for channel estimation based training symbols in MIMO-OFDM systems. The results confirm the superiority of the represented methods over the existing ones in terms of bandwidth efficiency and estimation error.
Highlights
Future mobile wireless communication systems will require high-bit-rate technologies
Accurate and efficient channel estimation plays a key role in Multi-input multi output (MIMO)-Orthogonal frequency division multiplexing (OFDM) communication systems, which is typically realized by using pilot or training sequences by virtue of low complexity and considerable performance
We study some novel methods for channel estimation based training symbols in MIMO-OFDM systems
Summary
Future mobile wireless communication systems will require high-bit-rate technologies. In [3], the training sequence design for MIMO-OFDM systems under the minimum mean square error (MMSE) criterion is addressed. One reason is that the number of channel parameters to be estimated are proportional to the channel delay spread and the number of antennas Another reason is that each received signal depends on multiple channel parameters. Authors in [3] focus on the optimal design of time-division multiplexed (TDM) training sequences for channel estimation. Authors focus on the optimal design of training sequences for MIMO-OFDM systems with the general case of spatial correlations. To formulate the optimization problem of the training sequence design, they adopt the minimum mean square error (MMSE) criterion subject to the power constraint. We study some novel methods for channel estimation based training symbols in MIMO-OFDM systems
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