[en] Reducing waiting time due to scheduling process and exploiting multi-access transmission, grant-free non-orthogonal multiple access (GF-NOMA) has been considered as a promising access technology for URLLC-enabled 5G system with strict requirements on reliability and latency. However, GF-NOMAbased systems can suffer from severe interference caused by the grant-free (GF) access manner which may degrade the system performance and violate the URLLC-related requirements. To overcome this issue, the paper proposes a novel reinforcementlearning (RL)-based random access (RA) protocol based on which each device can learn from the previous decision and its corresponding performance to select the best subchannels and transmit power level for data transmission to avoid strong cross-interference. The learning-based framework is developed to maximize the system access efficiency which is defined as the ratio between the number of successful transmissions and the number of subchannels. Simulation results show that our proposed framework can improve the system access efficiency significantly in overloaded scenarios.
Centre de recherche :
- Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM - Signal Processing & Communications
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
TRAN, Duc Dung ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
HA, Vu Nguyen ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Novel Reinforcement Learning based Power Control and Subchannel Selection Mechanism for Grant-Free NOMA URLLC-Enabled Systems
H. Ji, S. Park, J. Yeo, Y. Kim, J. Lee, and B. Shim, "Ultra-reliable and low-latency communications in 5G downlink: Physical layer aspects, " IEEE Wireless Commun., vol. 25, no. 3, pp. 124-130, Jun. 2018.
S. K. Sharma and X. Wang, "Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions, " IEEE Commun. Surveys Tuts., vol. 22, no. 1, pp. 426-471, Firstquarter 2020.
T. T. Nguyen, V. N. Ha, and L. B. Le, "Wireless scheduling for heterogeneous services with mixed numerology in 5g wireless networks, " IEEE Communications Letters, vol. 24, no. 2, pp. 410-413, 2020.
V. N. Ha, T. T. Nguyen, L. B. Le, and J.-F. Frigon, "Admission control and network slicing for multi-numerology 5g wireless networks, " IEEE Networking Letters, vol. 2, no. 1, pp. 5-9, 2020.
D.-D. Tran, S. K. Sharma, S. Chatzinotas, I. Woungang, and B. Ottersten, "Short-packet communications for MIMO NOMA systems over Nakagami-m fading: BLER and minimum blocklength analysis, " IEEE Trans. Veh. Technol., vol. 70, no. 4, pp. 3583-3598, 2021.
Q.-V. Pham, F. Fang, V. N. Ha, M. J. Piran, M. Le, L. B. Le, W.-J. Hwang, and Z. Ding, "A survey of multi-access edge computing in 5g and beyond: Fundamentals, technology integration, and state-of-the-art, " IEEE Access, vol. 8, pp. 116 974-117 017, 2020.
A. C. Cirik, N. M. Balasubramanya, L. Lampe, G. Vos, and S. Bennett, "Toward the standardization of grant-free operation and the associated NOMA strategies in 3GPP, " IEEE Commun. Stand. Mag., vol. 3, no. 4, pp. 60-66, Dec. 2019.
M. Shirvanimoghaddam, M. Condoluci, M. Dohler, and S. J. Johnson, "On the fundamental limits of random non-orthogonal multiple access in cellular massive IoT, " IEEE J. Sel. Areas Commun., vol. 35, no. 10, pp. 2238-2252, Jul. 2017.
R. Abbas, M. Shirvanimoghaddam, Y. Li, and B. Vucetic, "A novel analytical framework for massive grant-free NOMA, " IEEE Trans. Commun., vol. 67, no. 3, pp. 2436-2449, Nov. 2018.
S. K. Sharma and X. Wang, "Collaborative distributed Q-learning for RACH congestion minimization in cellular IoT networks, " IEEE Commun. Lett., vol. 23, no. 4, pp. 600-603, Apr. 2019.
M. V. Da Silva, R. D. Souza, H. Alves, and T. Abrao, "A NOMA-based Q-learning random access method for machine type communications, " IEEE Wireless Commun. Lett., vol. 9, no. 10, pp. 1720-1724, Oct. 2020.
S. Han, X. Xu, Z. Liu, P. Xiao, K. Moessner, X. Tao, and P. Zhang, "Energy-efficient short packet communications for uplink NOMA-based massive MTC networks, " IEEE Trans. Veh. Technol., vol. 68, no. 12, pp. 12 066-12 078, Dec. 2019.
D.-D. Tran, S. K. Sharma, and S. Chatzinotas, "BLER-based adaptive Qlearningfor efficient random access in NOMA-based mMTC networks, " in IEEE Veh. Technol. Conf. (VTC-Spring), Helsinki, Finland, Apr. 2021, pp. 1-5.
D.-D. Tran, S. K. Sharma, S. Chatzinotas, and I. Woungang, "Qlearning-based scma for efficient random access in mmtc networks with short packets, " in IEEE Int. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC), Helsinki, Finland, Sep. 2021, pp. 1334-1338.
D. Tran, S. K. Sharma, S. Chatzinotas, and I. Woungang, "Learningbased multiplexing of grant-based and grant-free heterogeneous services with short packets, " in IEEE Global Commun. Conf. (GLOBECOM), Madrid, Spain, Dec. 2021, pp. 1-6.
W. Yang, G. Durisi, T. Koch, and Y. Polyanskiy, "Quasi-static multiple antenna fading channels at finite blocklength, " IEEE Trans. Inf. Theory, vol. 60, no. 7, pp. 4232-4265, Jul. 2014.
R. Li, Z. Zhao, X. Zhou, G. Ding, Y. Chen, Z. Wang, and H. Zhang, "Intelligent 5G: When cellular networks meet artificial intelligence, " IEEE Wireless Commun., vol. 24, no. 5, pp. 175-183, Oct. 2017.
