Prof. Robert W. Heath Profile

Prof. Robert W. Heath

at Univ of Texas at Austin

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Proceedings Article | 23 May 2005 Paper

Algorithms for quantized precoding in MIMO OFDM beamforming systems

Bishwarup Mondal, Robert Heath

Proceedings Volume 5847, (2005) https://doi.org/10.1117/12.612595

KEYWORDS: Orthogonal frequency division multiplexing, Transmitters, Quantization, Signal to noise ratio, Receivers, Antennas, Algorithm development, Computer simulations, Einsteinium, Systems modeling

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Multiple input multiple output (MIMO) wireless systems can offer significant diversity and transmit beamforming with receive combining provides a method to achieve this diversity with simple receive processing. The maximum gains in terms of array gain and diversity, however, requires perfect channel knowledge at the transmitter. In the absence of perfect channel knowledge, the channel information can be quantized at the receiver and sent back to the transmitter using a low-rate feedback link. In the case of narrowband channels, considerable work has been done in reducing the feedback information while maintaining bit-error-rate performance close to the case of perfect channel knowledge. This work, however, does not naturally extend to the case of frequency selective channels and leads to an explosion in the feedback overhead. In this paper, orthogonal frequency division multiplexing (OFDM)is considered as a low complexity implementation of MIMO beamforming combining over frequency selective channels. Two broad classes of algorithms are discussed for quantizing channel information - clustering and transform. The clustering algorithms group the subcarriers and choose a common frequency-domain representation of the channel information for each group. Thus the feedback rate depends on the number of groups and not on the number of subcarriers. The transform algorithms quantize the channel information in time-domain where the transform essentially decorrelates the channel information. Both the algorithms provide significant compression of channel information maintaining bit-error-rate performance close to the case of perfect channel knowledge.

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Noise in Communication Systems

24 May 2005 | Austin, Texas, United States

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