HyperHyperNetwork for the Design of Antenna Arrays
Authors: Shahar Lutati, Lior Wolf
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. The results are reported in Tab. 1. |
| Researcher Affiliation | Collaboration | 1Tel Aviv University 2Facebook AI Research. |
| Pseudocode | No | The paper includes architectural diagrams but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We share our implementation here. |
| Open Datasets | Yes | The dataset used in our experiments consists of 3, 000 randomly generated PCB antenna structures, with a random metal polygons structure. The Open EMS FDTD engine (Liebig, 2010) was used to obtain the far-field radiation pattern U. |
| Dataset Splits | Yes | For the simulating network, an average of 0.95 Multiscale-SSIM score over the validation set was achieved. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions several software components like 'Adam optimizer (Kingma & Ba, 2014)', 'Open EMS FDTD engine (Liebig, 2010)', and 'ELU activations (Clevert et al., 2015)' but does not specify their version numbers. |
| Experiment Setup | Yes | For all networks, the Adam optimizer (Kingma & Ba, 2014) is used with a learning rate of 10 4 and a decay factor of 0.98 every 2 epochs for 2, 000 epochs, the batch size is 10 samples per mini-batch. |