A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Abstract

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (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 signalto-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 nearoptimal 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.Index Terms-Deep learning, beamforming, MISO, beamforming neural network.

Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration

“…Based on results in Theorem 1, next we find solutions through designing the neural networks with the DL technique. Similar to our previous work [47], we introduce a general DL structure to approximate the mapping function from the channel coefficients to the beamforming solutions, as shown in Fig. 2.…”

“…The adopted DL structure includes two main modules: the neural network module and the signal processing module [51]. Here we give a short description about the two modules, and for more details readers are referred to [47]. MSE Figure 2.…”

“…The learned key features and the functionalities in the signal processing module are designated according to the expert knowledge. Note that the expert knowledge is problem-dependent and has no unified form, but what is in common is that the expert knowledge can significantly reduce the number of variables to be predicted compared to the beamforming matrix [47]. For example, the dual forms of the original problems are the typical expert knowledge for beamforming optimization.…”

“…However, there are few works that focus on the learning approach to optimize the beamforming design in multiantenna communications, with the exception of [42], [43], [44], [45], [46], [47]. The difficulty is partly due to the large number of complex variables contained in the beamforming matrix that need to be optimized.…”

Model-Driven Beamforming Neural Networks

“…The approach means that most complexity is shifted to offline training an artificial neural network (NN) with a large dataset [4]. An online solution can then be obtained by going through a trained NN generalizable from the dataset, with some simple linear and standard nonlinear operations [5]. Researchers have applied DL to network deployment and planning, resource management, and network operation and maintenance.…”