Deep Learning Methods for Universal MISO Beamforming
Junbeom Kim, Hoon Lee, Seung-Eun Hong, Seok-Hwan Park
Abstract
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.
Topics & Concepts
BeamformingComputer scienceDeep learningExploitConstraint (computer-aided design)Base stationTelecommunications linkPower (physics)Deep neural networksTransmitter power outputArtificial neural networkProcess (computing)Artificial intelligencePower budgetInterference (communication)Mathematical optimizationAlgorithmBudget constraintComputer engineeringCellular networkElectronic engineeringOptimization problemTransmitterSignal-to-noise ratio (imaging)Base (topology)Advanced MIMO Systems OptimizationMillimeter-Wave Propagation and ModelingWireless Signal Modulation Classification