Deep Learning-Assisted Beamforming Design and BER Evaluation in Multi-User Downlink Systems

Abstract

This paper studies deep learning-based beamforming design schemes for multi-user downlink systems. Two distinct objectives are considered: sum-rate maximization and min-rate maximization. Each of formulations is first tackled by classical majorization-minimization (MM) algorithms that find a locally optimum point iteratively. To reduce computational overheads of the MM algorithms, deep neural networks (DNNs) are introduced which yield optimized beamforming solutions from channel vector inputs. Performance of trained DNNs is evaluated in terms of bit-error rate (BER) measure. Numerical results show that deep learning approaches achieve the BER performance very close to MM algorithms with much reduced complexity. Also, it is desirable to adopt the minimum-rate criterion to achieve low BER performance rather than sum-rate.