A Deep Learning Framework for Optimization of MISO Downlink Beamforming
Xia, W, Zheng, G, Zhu, Y, Zhang, J, Wang, J and Petropulu, AP (2019). A Deep Learning Framework for Optimization of MISO Downlink Beamforming. IEEE Transactions on Communications. 68 (3), pp. 1866 - 1880.
|Authors||Xia, W, Zheng, G, Zhu, Y, Zhang, J, Wang, J and Petropulu, AP|
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IEEE Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime 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 known structure of 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. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.
|Keywords||Deep learning; beamforming; MISO; beamforming neural network|
|Journal||IEEE Transactions on Communications|
|Journal citation||68 (3), pp. 1866 - 1880|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Digital Object Identifier (DOI)||doi:10.1109/TCOMM.2019.2960361|
|17 Dec 2019|
|Publication process dates|
|Accepted||11 Dec 2019|
|Deposited||25 Jun 2020|
|Accepted author manuscript|
CC BY 4.0
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