Learning Adaptive Beamforming Policy for Different Optimization Problems

Deep learning has been widely used for wireless optimization. In most existing studies, a deep neural network (DNN) is trained for a particular optimization problem and then tested on the same problem with samples drawn from the same distribution as the training data. Practical systems, however, typically involve multiple optimization problems with conflicting objective functions. In this paper, we study the learning for multi-objective optimization (MOO) by using beamforming in multi-user multi-antenna system as an illustrative example. We seek to learn a beamforming policy for two distinct optimization problems: power-constrained sum rate maximization and signal-to-interference-plus-noise ratio-constrained power minimization. Different from conventional MOO methods, which primarily find Pareto-optimal solutions to balance the conflicting objective functions, we resort to transfer learning (TL) and model-agnostic meta-learning (MAML) to learn an adaptive beamforming policy, allowing efficient fine-tuning of trained DNNs with limited samples for either of the two optimization problems. Simulation results demonstrate that both TL and MAML enable the trained DNNs to efficiently adapt to the optimization problems, and graph neural network is a promising network architecture for learning adaptive beamforming policies.