  In this work, we investigate the optimal beamformer design for the downlink
of Multiple-Input Single-Output (MISO) Non-Orthogonal Multiple Access (NOMA),
mainly focusing on a two-user scenario. We derive novel closed-form expressions
for the Bit Error Rate (BER) experienced by both users when Quadrature
Amplitude Modulation (QAM) is employed. Using these expressions, we formulate a
fairness-based optimal beamforming problem aiming to minimize the maximum BER
encountered by the users. Due to the complexity of this problem and the
time-consuming nature of Constraint Optimization (CO) algorithms for real-time
telecommunication systems, we propose a deep learning (DL) approach for its
solution. The proposed DL architecture possesses specific input and output
characteristics that enable the simultaneous training and use of the system by
multiple different antenna schemes. By conducting extensive simulations, we
demonstrate that our proposed approach outperforms existing beamforming
solutions and achieves BER performance close to that given by CO algorithms
while significantly reducing the computational time needed. Finally, we conduct
simulations to examine the robustness and efficiency of our system in different
test scenarios.
