Acquisition of Channel State Information Based on K-Learning Sub-6GHz Assist mmWave

Millimeter-wave (mmWave) communication is a practicable scheme for big data communication, such as next-generation cellular communication. However, mmWave frequencies have an extremely large path loss, for this, hybrid analog/digital beamforming could serve as an awesome technique to reduce such loss. This paper concentrates on the channel state information acquirement problem in mmWave communication systems with massive multiple-input multiple-output (MIMO) arrays. Because the channel state information acquirement is a method of significant overhead, we consider an accurate channel estimation scheme with low overhead. This paper proposes using support information extracted at Sub-6GHz to aid the mmWave channel state information acquirement. We formulate mmWave channel state information acquirement as a compressive sensing problem and use generalized approximate message passing (GAMP) algorithm. We also extend the GAMP algorithm with support distribution information from Sub-6GHz channel. Furthermore, based on the K nearest neighbor idea, we redesign the GAMP algorithm depending on Sub-6GHz support distribution information. Simulation results show that the out-of-band information aided mmWave channel estimation is capable of reducing the pilot overhead greatly and channel estimation accuracy can be improved as well.