Deep Learning Enabled Joint Adaptive Compressive Channel Sensing and Hybrid Beamforming for mmWave Cellular Systems

Millimeter wave(mmWave) supported Massive MIMO systems with directional beamforming enables gigabitper-second data rates with large bandwidth and achieves sufficient link margin but algorithms, system operations still rely on microwave-based modifications. The mmWave channel has a very poor scattering nature, so sensing channel parameters need very large training pilot signals. The Presence of large antenna arrays with analog beamforming and giga-sample mixed-signal devices cause computational complexities and high power consumption. Realizing these issues, we propose a mmWave specific deep-learning enabled adaptive-compressive based channel sensing and hybrid beamforming solution that jointly optimizes the channel vectors in an unsupervised manner and generate RF beamforming/combining vectors that achieve precoding gains comparable to near-optimal achievable rates. Simulation results show that for a 64 by 64 transmit-receive antenna system, existing exhaustive search algorithm needs 4096 channel pilot signals, compressive algorithms need minimum 300 pilots. In contrast, our proposed solution needs only 4 pilots to sense the channel parameters and directly predicts RF beamforming/combining vectors for the hybrid architectures. Lesser channel training pilots cause low power consumption and proposed hybrid RF precoding solution overcomes hardware limitations faced by analog-only beamforming that shows up a promising development for the solution of mmwave channel estimation and hybrid precoding problem.