Name
Abstract | ||
|---|---|---|
This article investigates fast deep learning based transmit beamforming design for simultaneous wireless information and power transfer in the multiuser multiple-input-single-output downlink, with specific absorption rate (SAR) constraints. The problem of interest is to maximize the received signal-to-interference-plus-noise ratio and the energy harvested for all receivers, while satisfying the transmit power and the SAR constraints. The optimal solution can be obtained via convex optimization but incurs a high complexity. To reduce the computational complexity, this article proposes a model-driven deep learning technique that only needs to predict key features of the problem with much reduced dimension but enhanced performance compared to widely used data-driven machine learning. Simulation results demonstrate that our proposed algorithms can significantly reduce the algorithm execution time, while maintaining satisfactory performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TVT.2020.3028010 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY |
Keywords | DocType | Volume |
SWIPT, specific absorption rate, beamforming, deep learning | Journal | 69 |
Issue | ISSN | Citations |
12 | 0018-9545 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juping Zhang | 1 | 7 | 2.12 |
| Gan Zheng | 2 | 2199 | 115.78 |
| Ioannis Krikidis | 3 | 3348 | 180.98 |
| Rui Zhang | 4 | 13983 | 620.75 |