PMID- 34069303
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210604
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 23
IP  - 5
DP  - 2021 May 14
TI  - DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC.
LID - 10.3390/e23050613 [doi]
LID - 613
AB  - Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are 
      regarded as promising technologies to improve the computation capability and 
      offloading efficiency of mobile devices in the sixth-generation (6G) mobile 
      system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple 
      users were first grouped into pairs, and users in each pair offloaded their tasks 
      simultaneously by NOMA, then a dedicated time duration was scheduled to the more 
      delay-tolerant user for uploading the remaining data by orthogonal multiple 
      access (OMA). For the conventional NOMA uplink transmission, successive 
      interference cancellation (SIC) was applied to decode the superposed signals 
      successively according to the channel state information (CSI) or the quality of 
      service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, 
      which dynamically adapts the SIC decoding order among all NOMA groups. To solve 
      the user grouping problem, a deep reinforcement learning (DRL)-based algorithm 
      was proposed to obtain a close-to-optimal user grouping policy. Moreover, we 
      optimally minimized the offloading energy consumption by obtaining the 
      closed-form solution to the resource allocation problem. Simulation results 
      showed that the proposed algorithm converged fast, and the NOMA-MEC scheme 
      outperformed the existing orthogonal multiple access (OMA) scheme.
FAU - Li, Haodong
AU  - Li H
AUID- ORCID: 0000-0001-8184-400X
AD  - Department of Electrical and Electronic Engineering, The University of 
      Manchester, Manchester M13 9PL, UK.
FAU - Fang, Fang
AU  - Fang F
AUID- ORCID: 0000-0002-6582-6570
AD  - Department of Engineering, Durham University, Durham DH1 3LE, UK.
FAU - Ding, Zhiguo
AU  - Ding Z
AUID- ORCID: 0000-0001-5280-384X
AD  - Department of Electrical and Electronic Engineering, The University of 
      Manchester, Manchester M13 9PL, UK.
LA  - eng
PT  - Journal Article
DEP - 20210514
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC8156925
OTO - NOTNLM
OT  - deep reinforcement learning (DRL)
OT  - multi-access edge computing (MEC)
OT  - resource allocation
OT  - sixth-generation (6G)
OT  - user grouping
COIS- The authors declare no conflict of interest.
EDAT- 2021/06/03 06:00
MHDA- 2021/06/03 06:01
PMCR- 2021/05/14
CRDT- 2021/06/02 01:25
PHST- 2021/03/31 00:00 [received]
PHST- 2021/05/06 00:00 [revised]
PHST- 2021/05/11 00:00 [accepted]
PHST- 2021/06/02 01:25 [entrez]
PHST- 2021/06/03 06:00 [pubmed]
PHST- 2021/06/03 06:01 [medline]
PHST- 2021/05/14 00:00 [pmc-release]
AID - e23050613 [pii]
AID - entropy-23-00613 [pii]
AID - 10.3390/e23050613 [doi]
PST - epublish
SO  - Entropy (Basel). 2021 May 14;23(5):613. doi: 10.3390/e23050613.