| Autor: |
Zhe WANG, Taoshen LI, Lina GE, Guifen ZHANG, Min WU |
| Jazyk: |
čínština |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Tongxin xuebao, Vol 42, Pp 176-188 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1000-436X |
| DOI: |
10.11959/j.issn.1000-436x.2021131 |
| Popis: |
To solve the problems of high complexity and poor real-time performance caused by traditional wireless resource management based on optimization methods, the energy efficiency maximization problem of sink node and its mathematical model were established for SWIPT-enabled sensor-cloud system, then the deep learning method was introduced to realize the solving and online decision-making with lower complexity and higher real-time performance.The mathematical model was transformed into a high-dimensional solvable form, and then a SWIFT-WMMSE algorithm with iterated forms was designed to solve optimal beamforming vector.The convergence of SWIPT-WMMSE algorithm was proved.Then, based on error propagation of DNN approximation, the scale design criteria for the DNN was deduced, and the approximation was realized through DNN training.Finally, the simulation results verify the effectiveness of SWIPT-WMMSE and DNN algorithm, as well as the approximation effect of DNN and its system performance gains. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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