Deep CNN for Wideband Mmwave Massive Mimo Channel Estimation Using Frequency Correlation
For millimeter wave (mmWave) systems with large-scale arrays, hybrid processing structure is usually used at both transmitters and receivers to reduce the complexity and cost, which poses a very challenging issue in channel estimation, especially at the low transmit signal-to-noise ratio regime. In this paper, deep convolutional neural network (CNN) is employed to perform wideband channel estimation for mmWave massive multiple-input multiple-output (MIMO) systems. In addition to exploiting spatial correlation, our joint channel estimation approach also exploits the frequency correlation, where the tentatively estimated channel matrices at multiple adjacent subcarriers are input into the CNN simultaneously. The complexity analysis and numerical results show that the proposed CNN based joint channel estimation outperforms the non-ideal minimum mean-squared error (MMSE) estimator with reduced complexity and achieves the performance close to the ideal MMSE estimator. It is also quite robust to different propagation scenarios.
