Multidimensional Compressive Sensing Based Analog CSI Feedback for Massive MIMO-OFDM Systems

We study the analog feedback of channel state information (CSI) in the massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) transmission. In a massive MIMO-OFDM system, the amount of CSI required by the transmitter increases linearly with the number of transmit antennas and subcarriers. To address this challenge, in this paper we propose a novel CSI feedback method for massive MIMO-OFDM systems based on the multidimensional compressive sensing theory. Taking advantage of a mathematical advance, the Tucker tensor decomposition, and spatial and frequency correlation of the channel, we reveal the connection between the tensor decomposition model and CS involving multidimensional signals (tensors). Then by making use of this connection, we exploit the structure contained in all different dimensions of the original channel matrix and compress it in each dimension simultaneously, thereby resulting in a large reduction in amount of CSI to be fed back and enabling a significant enhancement of spectral efficiency. The numerical results validate the effectiveness of the proposed scheme.