A Time-Variant Channel Prediction and Feedback Framework for Interference Alignment

Abstract

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 paper, 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 nonpredictive feedback strategy can be secured and a $60\%$ higher rate is achieved at 20-dB signal-to-noise ratio with moderatesxst mobility.