Channel state information (CSI) in the interference channel can be used toprecode, align, and reduce the dimension of interference at the receivers, toachieve the channel's maximum multiplexing gain, through what is known asinterference alignment. Most interference alignment algorithms requireknowledge of all the interfering channels to compute the alignment precoders.CSI, considered available at the receivers, can be shared with the transmittersvia limited feedback. When alignment is done by coding over frequencyextensions in a single antenna system, the required CSI lies on theGrassmannian manifold and its structure can be exploited in feedback.Unfortunately, the number of channels to be shared grows with the square of thenumber of users, creating too much overhead with conventional feedback methods.This paper proposes Grassmannian differential feedback to reduce feedbackoverhead by exploiting both the channel's temporal correlation and Grassmannianstructure. The performance of the proposed algorithm is characterized bothanalytically and numerically as a function of channel length, mobility, and thenumber of feedback bits. The main conclusions are that the proposed feedbackstrategy allows interference alignment to perform well over a wide range ofDoppler spreads, and to approach perfect CSI performance in slowly varyingchannels. Numerical results highlight the trade-off between the frequency offeedback and the accuracy of individual feedback updates.
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