  To address practical challenges in establishing and maintaining robust
wireless connectivity such as multi-path effects, low latency, size reduction,
and high data rate, the digital beamformer is performed by the hybrid antenna
array at the frequency of operation of 10 GHz. The proposed digital beamformer,
as a spatial filter, is capable of performing Direction of Arrival (DOA)
estimation and beamforming. The most well-established machine learning
technique of support vector machine (SVM) for the DoA estimation is limited to
problems with linearly-separable datasets.
  To overcome the aforementioned constraint, in the proposed beamformer, the
QS-SVM classifier with a small regularizer has been used for the DoA estimation
in addition to the two beamforming techniques of LCMV and MVDR. The
QS-SVM-based beamformer has been deployed in an FPGA board, as demonstrated in
detail in this work. The implementation results have verified the strong
performance of the QS-SVM-based beamformer in suppressing undesired signals,
deep nulls with powers less than -10 dB in undesired signals, and transferring
desired signals. Furthermore, we have demonstrated that the performance of the
QS-SVM-based beamformer consists of other advantages of average latency time in
the order of milliseconds, performance efficiency of more than 90\%, and
throughput of about 100\%.
