TY - JOUR
T1 - Deep Learning-Based Power Control for Non-Orthogonal Random Access
AU - Jang, Han Seung
AU - Lee, Hoon
AU - Quek, Tony Q.S.
N1 - Funding Information:
Manuscript received July 21, 2019; accepted August 15, 2019. Date of publication August 20, 2019; date of current version November 11, 2019. This work was supported in part by the NRF grant funded by the Korea government Ministry of Science and ICT (No. 2019R1F1A1061023), in part by the NRF grant funded by the Korea government Ministry of Science and ICT (No. 2019R1F1A1060648), and in part by the SUTD Growth Plan Grant for AI. The associate editor coordinating the review of this letter and approving it for publication was F. Wang. (corresponding author: Hoon Lee.) H. S. Jang is with the School of Electrical, Electronic Communication, and Computer Engineering, Chonnam National University, Yeosu 59626, South Korea (e-mail: [email protected]).
Funding Information:
This work was supported in part by the NRF grant funded by the Korea government Ministry of Science and ICT (No. 2019R1F1A1061023), in part by the NRF grant funded by the Korea government Ministry of Science and ICT (No. 2019R1F1A1060648), and in part by the SUTD Growth Plan Grant for AI.
Publisher Copyright:
© 1997-2012 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
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U2 - 10.1109/LCOMM.2019.2936473
DO - 10.1109/LCOMM.2019.2936473
M3 - Article
AN - SCOPUS:85077739175
SN - 1089-7798
VL - 23
SP - 2004
EP - 2007
JO - IEEE Communications Letters
JF - IEEE Communications Letters
IS - 11
M1 - 8807130
ER -