PMID- 37631599
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230828
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 16
DP  - 2023 Aug 9
TI  - User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep 
      Reinforcement Learning.
LID - 10.3390/s23167062 [doi]
LID - 7062
AB  - In this paper, we investigate a user pairing problem in power domain 
      non-orthogonal multiple access (NOMA) scheme-aided satellite networks. In the 
      considered scenario, different satellite applications are assumed with various 
      delay quality-of-service (QoS) requirements, and the concept of effective 
      capacity is employed to characterize the effect of delay QoS limitations on 
      achieved performance. Based on this, our objective was to select users to form a 
      NOMA user pair and utilize resource efficiently. To this end, a power allocation 
      coefficient was firstly obtained by ensuring that the achieved capacity of users 
      with sensitive delay QoS requirements was not less than that achieved with an 
      orthogonal multiple access (OMA) scheme. Then, considering that user selection in 
      a delay-limited NOMA-based satellite network is intractable and non-convex, a 
      deep reinforcement learning (DRL) algorithm was employed for dynamic user 
      selection. Specifically, channel conditions and delay QoS requirements of users 
      were carefully selected as state, and a DRL algorithm was used to search for the 
      optimal user who could achieve the maximum performance with the power allocation 
      factor, to pair with the delay QoS-sensitive user to form a NOMA user pair for 
      each state. Simulation results are provided to demonstrate that the proposed 
      DRL-based user selection scheme can output the optimal action in each time slot 
      and, thus, provide superior performance than that achieved with a random 
      selection strategy and OMA scheme.
FAU - Zhang, Qianfeng
AU  - Zhang Q
AD  - Guangxi Key Laboratory of Ocean Engineering Equipment and Technology, Qinzhou 
      535011, China.
AD  - Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology (Beibu 
      Gulf University), Education Department of Guangxi Zhuang Autonomous Region, 
      Qinzhou 535011, China.
FAU - An, Kang
AU  - An K
AD  - Sixty-Third Research Institute, National University of Defense Technology, 
      Nanjing 210007, China.
FAU - Yan, Xiaojuan
AU  - Yan X
AUID- ORCID: 0000-0001-5042-1688
AD  - Guangxi Key Laboratory of Ocean Engineering Equipment and Technology, Qinzhou 
      535011, China.
AD  - School of Information Science and Engineering, Southeast University, Nanjing 
      210096, China.
FAU - Xi, Hongxia
AU  - Xi H
AD  - Guangxi Key Laboratory of Ocean Engineering Equipment and Technology, Qinzhou 
      535011, China.
FAU - Wang, Yuli
AU  - Wang Y
AD  - Guangxi Key Laboratory of Ocean Engineering Equipment and Technology, Qinzhou 
      535011, China.
LA  - eng
GR  - 2020GXNSFBA159051/Guangxi Natural Science Foundation/
GR  - 2020M681457/China Postdoctoral Science Foundation/
GR  - 2019KYQD40/Scientific Research Foundation of Beibu Gulf University/
PT  - Journal Article
DEP - 20230809
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10459489
OTO - NOTNLM
OT  - NOMA-based satellite networks
OT  - delay QoS limitation
OT  - user pairing
COIS- The authors declare that they have no known competing financial interest or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/08/26 10:41
MHDA- 2023/08/26 10:42
PMCR- 2023/08/09
CRDT- 2023/08/26 01:32
PHST- 2023/07/11 00:00 [received]
PHST- 2023/08/07 00:00 [revised]
PHST- 2023/08/09 00:00 [accepted]
PHST- 2023/08/26 10:42 [medline]
PHST- 2023/08/26 10:41 [pubmed]
PHST- 2023/08/26 01:32 [entrez]
PHST- 2023/08/09 00:00 [pmc-release]
AID - s23167062 [pii]
AID - sensors-23-07062 [pii]
AID - 10.3390/s23167062 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Aug 9;23(16):7062. doi: 10.3390/s23167062.