Application of compressive sensing to limited feedback strategy in large-scale multiple-input single-output cellular networks

A novel limited feedback strategy based on a compressive sensing technique is proposed for the downlink large-scale multiple-input single-output (MISO) system. The effectiveness of transmit beamformers relies on the feedback quality of channel state information (CSI). The instantaneous CSI is compressed and sent back to the base station (BS) by receivers. An efficient recovery algorithm is applied at the BS in the presence of measurement noise and feedback link noise. The proposed feedback strategy can provide the BS with much more precise CSI as compared with some conventional vector quantisation schemes. The proposed scheme also has lower-computational complexity. Furthermore, the authors derive a tight lower bound on the user sum rate, assuming that zero-forcing beamforming is applied at the BS, and that active users are semi-orthogonal to each other. Numerical results show that the proposed compressive sensing-based limited feedback scheme yields better performance gain than the conventional channel quantisation approaches.