Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Joint User Association and Resource Allocation for Tailored QoS Provisioning in 6G HetNets (2403.03492v2)

Published 6 Mar 2024 in cs.NI

Abstract: The proliferation of wireless-enabled applications with divergent quality of service (QoS) requirements necessitates tailored QoS provisioning. With the growing complexity of wireless infrastructures, application-specific QoS perceived by a user equipment (UE) is jointly determined by its association with the supporting base station in heterogeneous networks (HetNets) and the amount of resource allocated to it. However, conventional application-agnostic objective-based user association and resource allocation often ignore the differences among applications' specific requirements for resources, inevitably preventing tailored QoS provisioning. Hence, in this paper, the problem of joint user association and resource allocation with application-specific objectives is investigated for achieving tailored QoS provisioning in 6G HetNets. This problem is intrinsically difficult to solve directly due to the extremely large solution space and the combination of discrete and continuous variables. Therefore, we decompose the original problem into two subproblems, i.e. user association and resource allocation, and propose an interactive optimization algorithm (IOA) to solve them iteratively in an interactive way until convergence is achieved. Specifically, matching theory is utilized to solve resource allocation and user association is solved heuristically. Extensive experimental results confirm that IOA algorithm outperforms several baseline algorithms in terms of both average utility and UE satisfaction ratio.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. K. Qu, W. Zhuang, Q. Ye, W. Wu, and X. Shen, “Model-assisted learning for adaptive cooperative perception of connected autonomous vehicles,” IEEE Trans. Wirel. Commun., 2024, (Early Access).
  2. R. Gupta, S. Tanwar, S. Tyagi, and N. Kumar, “Tactile-internet-based telesurgery system for healthcare 4.0: An architecture, research challenges, and future directions,” IEEE Netw., vol. 33, no. 6, pp. 22–29, 2019.
  3. I.-S. Comşa, G.-M. Muntean, and R. Trestian, “An innovative machine-learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments,” IEEE Trans. Multimed., vol. 67, no. 1, pp. 212–224, 2021.
  4. T. Dang and M. Peng, “Joint radio communication, caching, and computing design for mobile virtual reality delivery in fog radio access networks,” IEEE J. Sel. Areas Commun., vol. 37, no. 7, pp. 1594–1607, 2019.
  5. K. Chi, L. Huang, Y. Li, Y.-h. Zhu, X.-z. Tian, and M. Xia, “Efficient and reliable multicast using device-to-device communication and network coding for a 5G network,” IEEE Netw., vol. 31, no. 4, pp. 78–84, 2017.
  6. A. Stavridis, S. Sinanovic, M. Di Renzo, and H. Haas, “Energy evaluation of spatial modulation at a multi-antenna base station,” in Proc. IEEE VTC Fall, Las Vegas, NV, USA, 2013, pp. 1–5.
  7. H. Mehrpouyan, S. D. Blostein, and T. Svensson, “A new distributed approach for achieving clock synchronization in heterogeneous networks,” in Proc. IEEE GLOBECOM, Houston, TX, USA, 2011, pp. 1–5.
  8. P. Jia and X. Wang, “A new virtual network topology-based digital twin for spatial-temporal load-balanced user association in 6G HetNets,” IEEE J. Sel. Areas Commun., vol. 41, no. 10, pp. 3080–3094, 2023.
  9. A. Alizadeh, B. Lim, and M. Vu, “Multi-agent Q-learning for real-time load balancing user association and handover in mobile networks,” IEEE Trans. Wirel. Commun., 2024, (Early Access).
  10. W. Wu, F. Yang, F. Zhou, Q. Wu, and R. Q. Hu, “Intelligent resource allocation for IRS-enhanced OFDM communication systems: A hybrid deep reinforcement learning approach,” IEEE Trans. Wirel. Commun., vol. 22, no. 6, pp. 4028–4042, 2023.
  11. F. Wang, W. Chen, H. Tang, and Q. Wu, “Joint optimization of user association, subchannel allocation, and power allocation in multi-cell multi-association OFDMA heterogeneous networks,” IEEE Trans. Commun., vol. 65, no. 6, pp. 2672–2684, 2017.
  12. Y. Chen, J. Li, W. Chen, Z. Lin, and B. Vucetic, “Joint user association and resource allocation in the downlink of heterogeneous networks,” IEEE Trans. Veh. Technol., vol. 65, no. 7, pp. 5701–5706, 2015.
  13. B. Agarwal, M. A. Togou, M. Ruffini, and G.-M. Muntean, “A low complexity ML-assisted multi-knapsack-based approach for user association and resource allocation in 5G HetNets,” in Proc. IEEE BMSB, Beijing, China, 2023, pp. 1–6.
  14. Z. Cheng, N. Chen, B. Liu, Z. Gao, L. Huang, X. Du, and M. Guizani, “Joint user association and resource allocation in HetNets based on user mobility prediction,” Comput. Netw., vol. 177, p. 107312, 2020.
  15. W. Deng, Y. Liu, M. Li, and M. Lei, “GNN-aided user association and beam selection for mmWave-integrated heterogeneous networks,” IEEE Wirel. Commun. Lett., vol. 12, no. 11, pp. 1836–1840, 2023.
  16. Q. Han, B. Yang, G. Miao, C. Chen, X. Wang, and X. Guan, “Backhaul-aware user association and resource allocation for energy-constrained HetNets,” IEEE Trans. Veh. Technol., vol. 66, no. 1, pp. 580–593, 2016.
  17. E. Vaezpour, M. Dehghan, and H. Yousefi’zadeh, “Robust joint user association and resource partitioning in heterogeneous cloud RANs with dual connectivity,” Comput. Commun., vol. 138, pp. 1–10, 2019.
  18. R. Liu, Q. Chen, G. Yu, and G. Y. Li, “Joint user association and resource allocation for multi-band millimeter-wave heterogeneous networks,” IEEE Trans. Commun., vol. 67, no. 12, pp. 8502–8516, 2019.
  19. S. Somesula, N. Sharma, and A. Anpalagan, “Artificial bee optimization aided joint user association and resource allocation in hcran,” Appl. Soft Comput., vol. 125, p. 109152, 2022.
  20. J.-S. Liu, C.-H. R. Lin, and Y.-C. Hu, “Joint resource allocation, user association, and power control for 5G LTE-based heterogeneous networks,” IEEE Access, vol. 8, pp. 122 654–122 672, 2020.
  21. A. Mughees, M. Tahir, M. A. Sheikh, A. Amphawan, Y. K. Meng, A. Ahad, and K. Chamran, “Energy-efficient joint resource allocation in 5G HetNet using multi-agent parameterized deep reinforcement learning,” Phys. Commun., vol. 61, p. 102206, 2023.
  22. A. Mughees, M. Tahir, M. A. Sheikh, A. Amphawan, K. M. Yap, M. H. Habaebi, and M. R. Islam, “Energy efficient joint user association and power allocation using parameterized deep DQN,” in Proc. IEEE ICCCE, Kuala Lumpur, Malaysia, 2023, pp. 35–40.
  23. Y. Li, M. Sheng, Y. Sun, and Y. Shi, “Joint optimization of BS operation, user association, subcarrier assignment, and power allocation for energy-efficient HetNets,” IEEE J. Sel. Areas Commun., vol. 34, no. 12, pp. 3339–3353, 2016.
  24. F. Yin, A. Wang, D. Liu, and Z. Zhang, “Energy-aware joint user association and resource allocation for coded cache-enabled HetNets,” IEEE Access, vol. 7, pp. 94 128–94 142, 2019.
  25. Y. Kim, J. Jang, and H. J. Yang, “Distributed resource allocation and user association for max-min fairness in HetNets,” IEEE Trans. Veh. Technol., vol. 73, no. 2, pp. 2983–2988, 2024.
  26. B. Huang and A. Guo, “A dynamic hierarchical game approach for user association and resource allocation in HetNets with wireless backhaul,” IEEE Wirel. Commun. Lett., vol. 13, no. 1, pp. 59–63, 2024.
  27. N. Noorivatan and B. Mahboobi, “Joint user association and power-bandwidth allocation in heterogeneous cellular networks,” in Proc. IEEE IST, Tehran, Iran, 2020, pp. 96–102.
  28. H. U. Sokun, R. H. Gohary, and H. Yanikomeroglu, “A novel approach for QoS-aware joint user association, resource block and discrete power allocation in HetNets,” IEEE Trans. Wirel. Commun., vol. 16, no. 11, pp. 7603–7618, 2017.
  29. C. Chaieb, F. Abdelkefi, and W. Ajib, “Joint user association and sub-channel assignment in wireless networks with heterogeneous multiple access and heterogeneous base stations,” in Proc. IEEE PIMRC, London, UK, 2020, pp. 1–6.
  30. N. Zhao, Y.-C. Liang, D. Niyato, Y. Pei, M. Wu, and Y. Jiang, “Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks,” IEEE Trans. Wirel. Commun., vol. 18, no. 11, pp. 5141–5152, 2019.
  31. M. A. AboulHassan, M. Yassin, S. Lahoud, M. Ibrahim, D. Mezher, B. Cousin, and E. A. Sourour, “Classification and comparative analysis of inter-cell interference coordination techniques in LTE networks,” in Proc. IEEE NTMS, Paris, France, 2015, pp. 1–6.
  32. B. Shi, F.-C. Zheng, C. She, J. Luo, and A. G. Burr, “Risk-resistant resource allocation for eMBB and URLLC coexistence under M/G/1 queueing model,” IEEE Trans. Veh. Technol., vol. 71, no. 6, pp. 6279–6290, 2022.
  33. D. Liu, Y. Chen, K. K. Chai, and T. Zhang, “Backhaul aware joint uplink and downlink user association for delay-power trade-offs in HetNets with hybrid energy sources,” Trans. Emerg. Telecommun. Technol., vol. 28, no. 3, p. e2968, 2017.
  34. F. Shi, K. Sun, W. Huang, and Y. Wei, “User association for on-grid energy minimizing in HetNets with hybrid energy supplies,” in Proc. IEEE ICCT, Chongqing, China, 2018, pp. 778–783.
  35. Y. Barayan and I. Kostanic, “Performance evaluation of proportional fairness scheduling in LTE,” in Proc. WCECS, vol. 2, San Francisco, USA, 2013, pp. 712–717.
  36. Y. J. Zhang and K. B. Letaief, “Multiuser adaptive subcarrier-and-bit allocation with adaptive cell selection for OFDM systems,” IEEE Trans. Wirel. Commun., vol. 3, no. 5, pp. 1566–1575, 2004.
  37. Y. Cai, J. Yu, Y. Xu, and M. Cai, “A comparision of packet scheduling algorithms for OFDMA systems,” in Proc. IEEE ICSPCS, Gold Coast, QLD, Australia, 2008, pp. 1–5.
  38. X. Ling, B. Wu, H. Wen, L. Pan, and F. Luo, “Fast and efficient parallel-shift water-filling algorithm for power allocation in orthogonal frequency division multiplexing-based underlay cognitive radios,” IET Commun., vol. 7, no. 12, pp. 1269–1278, 2013.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now