Beamforming design with fully connected analog beamformer using deep learning

Beamforming (BF) architecture, an emerging approach for large‐scale antenna arrays, is a key task for the next decade's communication systems. The proposed approach in millimeter wave transmission is a fully connected analog phase‐shifter‐based BF architecture with limited radio frequency chains and imperfect channel state information (CSI). Deep learning (DL) is a powerful method for channel estimation and signal identification in wireless communications. Hence, this research proposes a DL‐enabled beamforming neural network (BFNN) which can be programmed to optimize the beamformer to attain better spectral efficiency. Simulation findings reveal that the proposed BFNN achieves significant performance gain and high robustness to imperfect CSI. The proposed BFNN greatly decreases the computational complexity by 0.16 million floating point operations (FLOPs) over 0.26 million FLOPs by conventional BF algorithms. This paper presents a deep learning‐enabled beamforming architecture with fully connected analog beamformer. There are two design phases, namely, offline training phase and online training phase. Simulation samples are created via system model in the offline training phase, and then updated weights obtained from offline training are fed into beamforming neural network (BFNN) with new loss function. The performance analysis of the proposed design is provided with respect to computational complexity and spectral efficiency (bits/s/Hz) and is also contrasted with current literature work.