Codebook-based interference alignment for uplink MIMO interference channels

In this paper, we propose a codebook-based interference alignment (IA) scheme in the constant multiple-input multiple-output (MIMO) interference channel especially for the uplink scenario. In our proposed scheme, we assume cooperation among base stations (BSs) through reliable backhaul links so that global channel knowledge is available for all BSs, which enables BS to compute the transmit preceder and inform its quantized index to the associated user via limited rate feedback link. We present an upper bound on the rate loss of the proposed scheme and derive the scaling law of the feedback load to maintain a constant rate loss relative to IA with perfect channel knowledge. Considering the impact of overhead due to training, cooperation, and feedback, we address the effective degrees of freedom (DOF) of the proposed scheme and derive the maximization of the effective DOF. From simulation results, we verify our analysis on the scaling law to preserve the multiplexing gain and confirm that the proposed scheme is more effective than the conventional IA scheme in terms of the effective DOF.