Abstract : Massive multiple-input multiple-output (MIMO) is apromising approach for cellular communication due to its energyefficiency and high achievable data rate. These advantages, however,can be realized only when channel state information (CSI) isavailable at the transmitter. Since there are many antennas, CSI istoo large to feed back without compression. To compress CSI, priorwork has applied compressive sensing (CS) techniques and the factthat CSI can be sparsified. The adopted sparsifying bases fail, however,to reflect the spatial correlation and channel conditions or tobe feasible in practice. In this paper, we propose a new sparsifyingbasis that reflects the long-term characteristics of the channel, andneeds no change as long as the spatial correlation model does notchange. We propose a new reconstruction algorithm for CS, andalso suggest dimensionality reduction as a compression method. Tofeed back compressed CSI in practice, we propose a new codebookfor the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channelfeedback mechanisms show better performance in point-to-point(single-user) and point-to-multi-point (multi-user) scenarios.
Index terms : MIMO system, multi-user system, channel feedback, compressed feedback