MMSE-based IA Schemes for Severely Time-Dispersive Channels with Limited Feedback

Sara Teodoro, Adão Silva, Rui Dinis, and Atílio Gameiro


Cooperative systems, Interference alignment, Severely time-dispersive channels


Interference alignment (IA) is a promising technique that allows high capacity gains in interfering channels. In this paper we consider the design of iterative minimum mean squared error (MMSE)-based IA techniques for the downlink of broadband wireless systems with limited feedback and employing orthogonal frequency division multiplexing (OFDM) signals. A quantized version of the channel state information (CSI) associated with the different links between base station (BS) and user terminal (UT) is feedback from the UT to the BS which sends it to the other BSs through a limited-capacity backhaul network. This information is then used by each BS to perform the overall IA design. A low-complexity channel quantization technique is employed, requiring the quantization of only a fraction of the channel frequency response samples. Therefore, we need a relatively small number of quantization bits to transmit and share CSI, while allowing performance close to the one obtained with perfect channel knowledge.

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