Interference alignment (IA) can use channel state information (CSI) to precode, align, and reduce the dimension of interference at each of the receivers, enabling systems to achieve their maximum multiplexing gain. CSI, estimated at the receivers, can be shared with the transmitters by limited feedback. The number of channels to be shared grows with the square of the number of users creating too much overhead in conventional feedback methods. This paper proposes Grassmannian differential feedback to take advantage of temporal correlation in the channel and reduce overhead. Grassmannian differential feedback uses two manifold tools, tangent spaces and geodesic paths, to track the evolution of CSI on the manifold. Simulation results show that the proposed feedback strategy allows IA to perform well over a wide range of Doppler spreads, and to approach perfect CSI performance in slowly varying channels.
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