Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication

• Published in: IEEE systems journal Vol. 13; no. 3; pp. 2551 - 2554
• Main Authors: Lee, Woongsup, Kim, Minhoe, Cho, Dong-Ho
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Channel capacity; Computational modeling; Computer simulation; Computing time; Control equipment; Deep learning; Device-to-device communication; interference; Iterative methods; Machine learning; Optimization; Power control; Production scheduling; Radio transmitters; Radios; Training; wireless communication

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...