HyperHyperNetworks for the Design of Antenna Arrays
This addresses the problem of efficient antenna design for engineers, but it appears incremental as it builds on existing hypernetwork and simulation techniques.
The paper tackled the problem of designing antennas and antenna arrays that meet specific radiation patterns, spatial constraints, and predetermined locations, using deep learning methods. The result was that their approach designed novel, compliant antennas and arrays considerably better than baselines, with improved properties in a cellular phone array case.
We present deep learning methods for the design of arrays and single instances of small antennas. Each design instance is conditioned on a target radiation pattern and is required to conform to specific spatial dimensions and to include, as part of its metallic structure, a set of predetermined locations. The solution, in the case of a single antenna, is based on a composite neural network that combines a simulation network, a hypernetwork, and a refinement network. In the design of the antenna array, we add an additional design level and employ a hypernetwork within a hypernetwork. The learning objective is based on measuring the similarity of the obtained radiation pattern to the desired one. Our experiments demonstrate that our approach is able to design novel antennas and antenna arrays that are compliant with the design requirements, considerably better than the baseline methods. We compare the solutions obtained by our method to existing designs and demonstrate a high level of overlap. When designing the antenna array of a cellular phone, the obtained solution displays improved properties over the existing one.