  In interference channels, channel state information (CSI) can be exploited to
reduce the interference signal dimensions and thus achieve the optimal capacity
scaling, i.e. degrees of freedom, promised by the interference alignment
technique. However, imperfect CSI, due to channel estimation error, imperfect
CSI feedback and time selectivity of the channel, lead to a performance loss.
In this work, we propose a novel limited feedback algorithm for single-input
single-output interference alignment in time-variant channels. The feedback
algorithm encodes the channel evolution in a small number of subspace
coefficients, which allow for reduced-rank channel prediction to compensate for
the channel estimation error due to time selectivity of the fading process and
feedback delay. An upper bound for the rate loss caused by feedback
quantization and channel prediction is derived. Based on this bound, we develop
a dimension switching algorithm for the reduced-rank predictor to find the best
tradeoff between quantization- and prediction-error. Besides, we characterize
the scaling of the required number of feedback bits in order to decouple the
rate loss due to channel quantization from the transmit power. Simulation
results show that a rate gain over the traditional non-predictive feedback
strategy can be secured and a 60% higher rate is achieved at 20 dB
signal-to-noise ratio with moderate mobility.
