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Performance Optimization in RSMA-assisted Uplink xURLLC IIoT Networks with Statistical QoS Provisioning (2405.16471v1)

Published 26 May 2024 in cs.NI

Abstract: Industry 5.0 and beyond networks have driven the emergence of numerous mission-critical applications, prompting contemplation of the neXt-generation ultra-reliable low-latency communication (xURLLC). To guarantee low-latency requirements, xURLLC heavily relies on short-blocklength packets with sporadic arrival traffic. As a disruptive multi-access technique, rate-splitting multiple access (RSMA) has emerged as a promising avenue to enhance quality of service (QoS) and flexibly manage interference for next-generation communication networks. In this paper, we investigate an innovative RSMA-assisted uplink xURLLC industrial internet-of-things (IIoT) (RSMA-xURLLC-IIoT) network. To unveil reliable insights into the statistical QoS provisioning (SQP) for our proposed network with sporadic arrival traffic, we leverage stochastic network calculus (SNC) to develop a dependable theoretical framework. Building upon this theoretical framework, we formulate the SQP-driven short-packet size maximization problem and the SQP-driven transmit power minimization problem, aiming to guarantee the SQP performance to latency, decoding, and reliability while maximizing the short-packet size and minimizing the transmit power, respectively. By exploiting Monte-Carlo methods, we have thoroughly validated the dependability of the developed theoretical framework. Moreover, through extensive comparison analysis with state-of-the-art multi-access techniques, including non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), we have demonstrated the superior performance gains achieved by the proposed RSMA-xURLLC-IIoT networks.

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References (42)
  1. J. Park et al., “Extreme ultra-reliable and low-latency communication,” Nat. Electron., vol. 5, no. 3, pp. 133–141, Mar. 2022.
  2. C. She et al., “A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning,” Proc. IEEE, vol. 109, no. 3, pp. 204–246, Mar. 2021.
  3. Y. Chen et al., “Statistical QoS provisioning analysis and performance optimization in xURLLC-enabled massive MU-MIMO networks: A stochastic network calculus perspective,” IEEE Trans. Wireless Commun., pp. 1–1, 2024.
  4. Y. Chen et al., “When xURLLC meets NOMA: A stochastic network calculus perspective,” IEEE Commun. Mag., Jul. 2023.
  5. Y. Chen et al., “Enhancing xURLLC with RSMA-assisted massive-MIMO networks: Performance analysis and optimization,” arXiv preprint arXiv:2402.16027, 2024.
  6. Y. Liu et al., “Deep reinforcement learning-based grant-free NOMA optimization for mURLLC,” IEEE Trans. Commun., vol. 71, no. 3, pp. 1475–1490, 2023.
  7. W. Xian et al., “Advanced manufacturing in industry 5.0: A survey of key enabling technologies and future trends,” IEEE Trans. Ind. Informat., 2023.
  8. M. Khoshnevisan et al., “5G industrial networks with CoMP for URLLC and time sensitive network architecture,” IEEE J. Sel. Areas Commun., vol. 37, no. 4, pp. 947–959, 2019.
  9. Y. Polyanskiy et al., “Channel coding rate in the finite blocklength regime,” IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2307–2359, May 2010.
  10. W. Yang et al., “Quasi-static multiple-antenna fading channels at finite blocklength,” IEEE Trans. Inf. Theory, vol. 60, no. 7, pp. 4232–4265, Jul. 2014.
  11. Y. Mao et al., “Rate-splitting multiple access: Fundamentals, survey, and future research trends,” IEEE Commun. Surv. Tutorials, 2022.
  12. B. Clerckx et al., “A primer on rate-splitting multiple access: Tutorial, myths, and frequently asked questions,” IEEE J. Sel. Areas Commun., 2023.
  13. A. Mishra et al., “Rate-splitting multiple access for 6G—part i: Principles, applications and future works,” IEEE Commun. Lett., vol. 26, no. 10, pp. 2232–2236, 2022.
  14. B. Rimoldi and R. Urbanke, “A rate-splitting approach to the gaussian multiple-access channel,” IEEE Trans. Inf. Theory, vol. 42, no. 2, pp. 364–375, 1996.
  15. H. Li et al., “Synergizing beyond diagonal reconfigurable intelligent surface and rate-splitting multiple access,” IEEE Trans. Wireless Commun., 2024.
  16. O. Dizdar and S. Wang, “Rate-splitting multiple access for semantic-aware networks: an age of incorrect information perspective,” IEEE Wireless Commun. Lett., 2024.
  17. H. Lei et al., “On secure mmWave RSMA systems,” IEEE Internet Things J., 2024.
  18. L. Qin et al., “Joint transmission and resource optimization in NOMA-assisted IoVT with mobile edge computing,” IEEE Trans. Veh. Technol., pp. 1–16, 2024.
  19. S. Khisa et al., “Power allocation and beamforming design for uplink rate-splitting multiple access with user cooperation,” IEEE Trans. Veh. Technol., 2024.
  20. M. Sarker and A. O. Fapojuwo, “Uplink power allocation for RSMA-aided user-centric cell-free massive MIMO systems,” in 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring).   IEEE, 2023, pp. 1–5.
  21. O. Abbasi et al., “Transmission scheme, detection and power allocation for uplink user cooperation with NOMA and RSMA,” IEEE Trans. Wireless Commun., vol. 22, no. 1, pp. 471–485, 2023.
  22. Y. Chen et al., “Streaming 360-degree VR video with statistical QoS provisioning in mmWave networks from delay and rate perspectives,” arXiv preprint arXiv:2305.07935, 2023.
  23. M. Bennis et al., “Ultrareliable and low-latency wireless communication: Tail, risk, and scale,” Proc. IEEE, vol. 106, no. 10, pp. 1834–1853, Oct. 2018.
  24. H. Al-Zubaidy et al., “Network-layer performance analysis of multihop fading channels,” IEEE/ACM Trans. Networking, vol. 24, no. 1, pp. 204–217, Feb. 2014.
  25. M. Fidler, “Survey of deterministic and stochastic service curve models in the network calculus,” IEEE Commun. Surv. Tutorials, vol. 12, no. 1, pp. 59–86, First Quarter 2010.
  26. M. Fidler and A. Rizk, “A guide to the stochastic network calculus,” IEEE Commun. Surv. Tutorials, vol. 17, no. 1, pp. 92–105, Firstquarter 2014.
  27. S. K. Singh et al., “RSMA for hybrid RIS-UAV-aided Full-Duplex communications with finite blocklength codes under imperfect SIC,” IEEE Trans. Wireless Commun., 2023.
  28. X. Ou et al., “Resource allocation in MU-MISO rate-splitting multiple access with SIC errors for URLLC services,” IEEE Trans. Commun., 2022.
  29. Y. Wang et al., “Flexible rate-splitting multiple access with finite blocklength,” IEEE J. Sel. Areas Commun., vol. 41, no. 5, pp. 1398–1412, 2023.
  30. S. Kurma et al., “URLLC-based cooperative industrial IoT networks with nonlinear energy harvesting,” IEEE Trans. Ind. Informat., vol. 19, no. 2, pp. 2078–2088, 2022.
  31. I. Muhammad et al., “Mission effective capacity—a novel dependability metric: A study case of multiconnectivity-enabled URLLC for IIoT,” IEEE Trans. Ind. Informat., vol. 18, no. 6, pp. 4180–4188, 2021.
  32. S.-Y. Lien and D.-J. Deng, “Intelligent session management for URLLC in 5G open radio access network: A deep reinforcement learning approach,” IEEE Trans. Ind. Informat., vol. 19, no. 2, pp. 1844–1853, 2022.
  33. J. Hu et al., “Low-complexity resource allocation for uplink RSMA in future 6G wireless networks,” IEEE Wireless Commun. Lett., vol. 13, no. 2, pp. 565–569, 2024.
  34. J. Xu et al., “Rate-splitting multiple access for short-packet uplink communications: A finite blocklength analysis,” IEEE Commun. Lett., vol. 27, no. 2, pp. 517–521, 2023.
  35. O. L. A. López et al., “Statistical tools and methodologies for ultrareliable low-latency communication—a tutorial,” Proc. IEEE, vol. 111, no. 11, pp. 1502–1543, 2023.
  36. O. Adamuz-Hinojosa et al., “A stochastic network calculus (SNC)-based model for planning B5G uRLLC RAN slices,” IEEE Trans. Wireless Commun., vol. 22, no. 2, pp. 1250–1265, 2023.
  37. P. Cui et al., “End-to-end delay performance analysis of industrial internet of things: A stochastic network calculus perspective,” IEEE Internet Things J., vol. 11, no. 3, pp. 5374–5387, 2024.
  38. C. Wu et al., “Cross-layer optimization for statistical QoS provision in C-RAN with finite-length coding,” IEEE Trans. Commun., pp. 1–1, 2024.
  39. X. Du et al., “Sequential optimization and reliability assessment method for efficient probabilistic design,” J. Mech. Des., vol. 126, no. 2, pp. 225–233, 2004.
  40. G. Zheng et al., “Joint hybrid precoding and rate allocation for RSMA in near-field and far-field massive MIMO communications,” IEEE Wireless Commun. Lett., pp. 1–1, 2024.
  41. L. Qin et al., “Energy-efficient blockchain-enabled user-centric mobile edge computing,” IEEE Trans. Cogn. Commun. Netw., pp. 1–1, 2024.
  42. H. Forssell et al., “Physical layer authentication in mission-critical MTC networks: A security and delay performance analysis,” IEEE J. Sel. Areas Commun., vol. 37, no. 4, pp. 795–808, 2019.
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Authors (5)
  1. Yuang Chen (19 papers)
  2. Hancheng Lu (47 papers)
  3. Chang Wu (21 papers)
  4. Langtian Qin (10 papers)
  5. Xiaobo Guo (32 papers)

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