Deep Learning Methods for Universal MISO Beamforming
This work addresses the need for efficient beamforming optimization in wireless communication systems, but it is incremental as it builds on existing deep learning methods with a focus on universal applicability.
The paper tackled the problem of optimizing beamforming vectors in multi-user multi-antenna systems with arbitrary transmit power constraints, achieving effectiveness over existing schemes as demonstrated by numerical results.
This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.