Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication
By Woongsup Lee, Minhoe Kim, Dong Ho Cho. 2019
44 citations recorded by the source corpus.
In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted sum rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved in an iterative manner, which possibly requires long computation time, in our proposed scheme the transmit power can be determined with a relatively low computation time. Through simulations, we confirm that the proposed scheme achieves a sufficiently high WSR with a sufficiently low computation time.
Subfields: Electrical and Electronic Engineering
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In Electrical and Electronic Engineering
- Tony Q. S. Quek
- Sang Hyun Lee
- Hoon Lee
- Paul Patras
- Hamed Haddadi
- Chaoyun Zhang
- Erik G. Larsson
- Meysam Sadeghi
Citations in the corpus
- Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, methodology): “Then, the real-time computation of the trained DNN units can be implemented in a distributed manner, otherwise not applicable for the DL techniques in [7], [10]–[12], [19], since they all require the perfect knowledge of other cells’ CSI.”
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[401] Transmit power Control in device-to-device communications Imitation learning MLP”