Efficient Optimization of a Ka-Band Branch Waveguide Power Divider

This paper presents an efficient optimization technique, Support Vector Regression (SVR) approach, for the designing of a Ka-band branch waveguide power divider. This SVR approach comes from the Support Vector Machine (SVM) learning theory, which is based on the structural risk minimization (SRM) principle and leads good generalization ability. With this method, a Ka-band branch waveguide power divider is proposed. Unlike the traditional Chebyschev-type branch-line coupler, this branch waveguide power divider has the uniform main guide and all the branches in same size to minimize the difficulty and sensitivity in the mechanical fabrication. A validation of the proposed design is also presented by means of full-wave simulation and measurement.