Algorithms for quantized precoded MIMO-OFDM systems

The knowledge of the wireless channel is crucial for realizing the capacity and diversity gains of a MIMO system. In the absence of perfect channel knowledge at the transmitter, channel information can be quantized at the receiver and sent back using a low-rate feedback link. In the case of flat-fading channels, considerable work has been done in reducing the feedback information. 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, OFDM is considered as an implementation of linearly precoded MIMO spatial multiplexing systems over frequency selective channels. Two 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.