Spectral Efficient Beamforming for mmWave MISO Systems using Deep Learning Techniques

The eMBB (enhanced Mobile BroadBand), URLLC (Ultra-Reliable, Low latency communication) and mMTC (massive Machine Type communication) are the drivers for 5G communication. To realize these use-cases, enhancing the throughput in available bandwidth is the fundamental requirement in the next-generation networks. If all these use-cases are satisfied without increasing the spectral efficiency, the day is not far when we start looking for even higher frequencies (probably 6G). Applying machine learning at all possible avenues in the physical layer will be a game-changer. In this paper, we propose a novel deep learning (DL) method for hybrid precoding to maximize the spectral efficiency. we consider a special case of the MIMO system with a single-output (MISO) and implement DL technique in hybrid precoding for perfect and imperfect Channel State Information (CSI). Though the blackbox method suits for massive MIMO systems with perfect CSI, we introduce a new deep learning method which directly outputs optimized beamforming even in imperfect CSI conditions. Simulation results show that the proposed DL-based beamformer improves spectrum throughput while being more robust to imperfect CSI over the traditional beamforming approaches. This work paves a way to implement machine learning in physical layer beamforming technique for 5G millimeter wave (mmWave) communications, thereby realizing cognition in wireless networks.