  Interference alignment (IA) is a multiplexing gain optimal transmission
strategy for the interference channel. While the achieved sum rate with IA is
much higher than previously thought possible, the improvement often comes at
the cost of requiring network channel state information at the transmitters.
This can be achieved by explicit feedback, a flexible yet potentially costly
approach that incurs large overhead. In this paper we propose analog feedback
as an alternative to limited feedback or reciprocity based alignment. We show
that the full multiplexing gain observed with perfect channel knowledge is
preserved by analog feedback and that the mean loss in sum rate is bounded by a
constant when signal-to-noise ratio is comparable in both forward and feedback
channels. When signal-to-noise ratios are not quite symmetric, a fraction of
the multiplexing gain is achieved. We consider the overhead of training and
feedback and use this framework to optimize the system's effective throughput.
We present simulation results to demonstrate the performance of IA with analog
feedback, verify our theoretical analysis, and extend our conclusions on
optimal training and feedback length.
