Algorithms for Quantized Precoding in MIMO OFDM Beamforming Systems


B. Mondal and Robert W. Heath Jr


To appear in the Proc. of Third SPIE Int. Symp. On Fluctuations and Noise, Austin, May 23-26, 2005.


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.

This preprint is available here.