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