Deep Learning-Based Power Control for Non-Orthogonal Random Access

Han Seung Jang, Hoon Lee, Tony Q.S. Quek

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)

摘要

This letter presents deep learning (DL) based non-orthogonal random access (NORA) where multiple nodes utilizing the identical preamble simultaneously transmit data over the same time-frequency resources. Effective power control algorithms are essential for the NORA, however, only partial information of channels such as the timing advance (TA) is available. This poses challenges for existing algorithms requiring full channel knowledge. We propose unsupervised DL-based power control schemes which maximize the minimum rate based only on the TA information. Numerical results verify the effectiveness of the proposed DL-based NORA over conventional methods.

原文English
文章編號8807130
頁(從 - 到)2004-2007
頁數4
期刊IEEE Communications Letters
23
發行號11
DOIs
出版狀態Published - 2019 11月

All Science Journal Classification (ASJC) codes

  • 建模與模擬
  • 電腦科學應用
  • 電氣與電子工程

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