Model-Driven Beamforming Neural Networks
This addresses real-time implementation challenges in mobile communication networks, offering a novel deep learning-based approach that is incremental over existing heuristic methods.
The paper tackles the high complexity and computational delay of iterative beamforming optimization in mobile networks by introducing data- and model-driven beamforming neural networks (BNNs), achieving promising results with lower complexity as shown in simulations and testbed evaluations.
Beamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing (ZF) are simpler but at the expense of performance loss. Alternatively, deep learning (DL) is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data- and model-driven beamforming neural networks (BNNs), presents various possible learning strategies, and also discusses complexity reduction for the DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions.