A Deep Learning Framework for Optimization of MISO Downlink Beamforming
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Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. This paper studies fast optimal downlink beamforming strategies by leveraging the powerful deep learning techniques. Traditionally, finding the optimal beamforming solution relies on iterative algorithms which leads to high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the structure of known optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem and the sum rate maximization problem. The BNNs for the former two problems adopt the supervised learning approach, while the BNN for the sum rate maximization problem employs a hybrid method of supervised and unsupervised learning to improve the performance beyond the state of the art. Simulation results show that with much reduced computational complexity, the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and outperform the existing algorithms that maximize the sum rate. In summary, this work paves the way for fast realization of the optimal beamforming in multiuser MISO systems.
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