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  3. Reinforcement learning based joint resource allocation and user fairness optimization in mmWave-NOMA HetNets
Reinforcement learning based joint resource allocation and user fairness optimization in mmWave-NOMA HetNets
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Reinforcement learning based joint resource allocation and user fairness optimization in mmWave-NOMA HetNets

Sobhi-Givi, S

Reinforcement learning based joint resource allocation and user fairness optimization in mmWave-NOMA HetNets

Sobhi-Givi, S ; Sharif University of Technology | 2023

130 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ICEE59167.2023.10334709
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. In this paper, we propose heterogeneous network (HetNet) with mmWave and hybrid non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission, where uplink (UL) of macrocell users (MCUs) and downlink (DL) of small cell users (SCUs) share the same resource block (RB) to increase the network capacity. Also, an imperfect successive interference cancelation (I-SIC) decoding is considered due to hardware impairment of the real-world NOMA systems. We formulate the problem of joint power and resource block (RB) allocation to maximize rate-fairness and sum-rate by considering minimum quality of service (QoS) requirements. Reinforcement learning (RL) named Q-learning (QL) and deep Q-learning network (DQN) algorithms are employed to solve these problems. The results indicate that the proposed QL and DQN algorithms increase the fairness and sum-rate while low number of iterations is required. © 2023 IEEE
  6. Keywords:
  7. Heterogenous networks (HetNet) ; Imperfect SIC ; mmWave ; Non-orthogonal multiple access (NOMA) ; Power allocation ; Rate-fairness ; Reinforcement learning
  8. Source: 2023 31st International Conference on Electrical Engineering, ICEE 2023 ; 2023 , Pages 781-786 ; 979-835031256-0 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10334709