Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Energy-optimized 6G communication framework with intelligent resource allocation for massive IoT networks
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 06 April 2026

Energy-optimized 6G communication framework with intelligent resource allocation for massive IoT networks

  • Mian Muhammad Kamal1,
  • Syed Zain Ul Abideen2,
  • Muhammad Sheraz3,
  • Habib Khan1,
  • Jamal N. A. Hassan1,
  • Hamedalneel B. A. Hamid1,
  • Luo Yinsheng1,
  • Tianjun Ma1,
  • Husam S. Samkari4,5,
  • Mohammed F. Allehyani4,
  • Muneera Altayeb6 &
  • …
  • Teong Chee Chuah3 

Scientific Reports , Article number:  (2026) Cite this article

  • 878 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing

Abstract

This paper proposes an energy-optimized uplink resource allocation framework for 6G massive Internet of Things (IoT) networks assisted by a Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS). Unlike prior works that optimize radio resources and STAR-RIS coefficients separately, we jointly control transmit power, subchannel assignment, and the full set of STAR-RIS amplitude splitting and phase-shift coefficients using a single Soft Actor-Critic (SAC) agent with Gumbel-Softmax relaxation. The resulting policy is trained offline in a centralized manner and executed online with edge cloud coordination. Extensive simulations based on 3GPP Urban Micro channels with up to 200 devices and a 128-element STAR-RIS show that the proposed framework achieves 24.3% higher energy efficiency, 18.7% higher aggregate throughput, 19.1% lower latency, and 21.6% longer network lifetime compared to state-of-the-art successive convex approximation baselines, while maintaining near-optimal fairness. The results demonstrate that tight cross-layer integration of propagation control and radio resource allocation via deep reinforcement learning is a scalable and effective solution for green 6G massive machine-type communications.

Similar content being viewed by others

Energy aware resource management in 6G IoT networks using STAR RIS

Article Open access 01 July 2025

Phase shift optimization in reconfigurable intelligent surface-assisted UAV in hierarchical aerial computing networks

Article Open access 03 March 2026

Deep learning optimization of STAR-RIS for enhanced data rate and energy efficiency in 6G wireless networks

Article Open access 20 July 2025

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Alqahtani, A., Taneja, N., Taneja, A. & Alqahtani, N. Energy aware resource management in 6G IoT networks using STAR RIS. Sci. Rep. 15(1), 20941. https://doi.org/10.1038/s41598-025-05338-w (2025).

    Google Scholar 

  2. Kumar, A. S., Zhao, L. & Fernando, X. Task offloading and resource allocation in vehicular networks: A Lyapunov-based deep reinforcement learning approach. IEEE Trans. Veh. Technol. 72(10), 13360–13373. https://doi.org/10.1109/TVT.2023.3271613 (2023).

    Google Scholar 

  3. Guo, Y., Fang, F., Cai, D. & Ding, Z. Energy-efficient design for a NOMA assisted STAR-RIS network with deep reinforcement learning. IEEE Trans. Veh. Technol. 72(4), 5424–5428. https://doi.org/10.1109/TVT.2022.3224926 (2023).

    Google Scholar 

  4. Ansere, J. A., Kamal, M., Khan, I. A. & Aman, M. N. Dynamic resource optimization for energy-efficient 6G-IoT ecosystems. Sensors 23(10), 4711. https://doi.org/10.3390/s23104711 (2023).

    Google Scholar 

  5. Fernando, X. & Lăzăroiu, G. Energy-efficient industrial internet of things in green 6G networks. Appl. Sci. 14(18), 8558. https://doi.org/10.3390/app14188558 (2024).

    Google Scholar 

  6. Abbas, M. T. et al. Towards zero-energy: Navigating the future with 6G in cellular internet of things. J. Netw. Comput. Appl. 230, 103945. https://doi.org/10.1016/j.jnca.2024.103945 (2024).

    Google Scholar 

  7. Mahmood, M. R., Matin, M. A., Sarigiannidis, P. & Goudos, S. K. A comprehensive review on artificial intelligence/machine learning algorithms for empowering the future IoT toward 6G era. IEEE Access 10, 87535–87562. https://doi.org/10.1109/ACCESS.2022.3199689 (2022).

    Google Scholar 

  8. Lin, Y., Shen, Y. & Li, A. Simultaneous transmission and reflection beamforming design for RIS-aided MU-MISO. IEEE Trans. Veh. Technol. 72(3), 4040–4045. https://doi.org/10.1109/TVT.2022.3214829 (2023).

    Google Scholar 

  9. Wu, C., Mu, X., Liu, Y., Gu, X. & Wang, X. Resource allocation in STAR-RIS-aided networks: OMA and NOMA. IEEE Trans. Wireless Commun. 21(9), 7653–7667. https://doi.org/10.1109/TWC.2022.3160151 (2022).

    Google Scholar 

  10. Zuo, J., Liu, Y., Ding, Z., Song, L. & Poor, H. V. Joint design for simultaneously transmitting and reflecting (STAR) RIS assisted NOMA systems. IEEE Trans. Wirel. Commun. 22(1), 611–626. https://doi.org/10.1109/TWC.2022.3197079 (2023).

    Google Scholar 

  11. Wang, Y. et al. Energy-efficient method based on dynamic topology switching and reliability in SDNs. IEEE Trans. Sustain. Comput. 7(2), 427–440. https://doi.org/10.1109/TSUSC.2021.3116325 (2022).

    Google Scholar 

  12. Gururaj, H. L. et al. Collaborative energy-efficient routing protocol for sustainable communication in 5G/6G wireless sensor networks. IEEE Open J. Commun. Soc. 4, 2050–2061 (2023).

    Google Scholar 

  13. Taneja, A., Rani, S., Dhanaraj, R. K. & Nkenyereye, L. GCIRM: Toward green communication with intelligent resource management scheme for radio access networks. IEEE Trans. Green Commun. Netw. 8(3), 1018–1025. https://doi.org/10.1109/TGCN.2024.3384542 (2024).

    Google Scholar 

  14. Israr, A., Yang, Q. & Israr, A. Emission-aware sustainable energy provision for 5G and B5G mobile networks. IEEE Trans. Sustain. Comput. 8(2), 670–681. https://doi.org/10.1109/TSUSC.2023.3271789 (2023).

    Google Scholar 

  15. Gu, L., Zhang, W., Wang, Z., Zeng, D. & Jin, H. Service management and energy scheduling toward low-carbon edge computing. IEEE Trans. Sustain. Comput. 8(1), 109–119. https://doi.org/10.1109/TSUSC.2022.3210564 (2023).

    Google Scholar 

  16. da Silva, M. D. M., Gamatié, A., Sassatelli, G., Poss, M. & Robert, M. Optimization of data and energy migrations in mini data centers for carbon-neutral computing. IEEE Trans. Sustain. Comput. 8(1), 68–81. https://doi.org/10.1109/TSUSC.2022.3197090 (2023).

    Google Scholar 

  17. Sirojuddin, A., Putra, D. D. & Huang, W.-J. Low-complexity sum-capacity maximization for intelligent reflecting surface-aided MIMO systems. IEEE Wirel. Commun. Lett. 11(6), 1354–1358. https://doi.org/10.1109/LWC.2022.3167731 (2022).

    Google Scholar 

  18. Soleymani, M., Santamaria, I. & Jorswieck, E. A. Spectral and energy efficiency maximization of MISO STAR-RIS-assisted URLLC systems. IEEE Access 11, 70833–70852 (2023).

    Google Scholar 

  19. Papazafeiropoulos, A., Elbir, A. M., Kourtessis, P., Krikidis, I. & Chatzinotas, S. Cooperative RIS and STAR-RIS assisted mMIMO communication: Analysis and optimization. IEEE Trans. Veh. Technol. 72(9), 11975–11989 (2023).

    Google Scholar 

  20. Taneja, A., Rani, S., Alhudhaif, A., Koundal, D. & Gündüz, E. S. An optimized scheme for energy efficient wireless communication via intelligent reflecting surfaces. Expert Syst. Appl. 190, 116106. https://doi.org/10.1016/j.eswa.2021.116106 (2022).

    Google Scholar 

  21. Katwe, M. V. et al. Spectrally-efficient beamforming design for STAR-RIS-aided URLLC NOMA systems. IEEE Trans. Commun. 72(6), 4414–4431. https://doi.org/10.1109/TCOMM.2024.3367941 (2024).

    Google Scholar 

  22. Song, Y., Xu, S., Xu, R. & Ai, B. Weighted sum-rate maximization for multi-STAR-RIS-assisted mmWave cell-free networks. IEEE Trans. Veh. Technol. https://doi.org/10.1109/TVT.2023.3332334 (2023).

    Google Scholar 

  23. Mu, X., Liu, Y., Guo, L., Lin, J. & Schober, R. Simultaneously transmitting and reflecting (STAR) RIS aided wireless communications. IEEE Trans. Wirel. Commun. 21(5), 3083–3098. https://doi.org/10.1109/TWC.2021.3118225 (2022).

    Google Scholar 

  24. Cheng, X. et al. Joint optimization for RIS-assisted wireless communications: From physical and electromagnetic perspectives. IEEE Trans. Commun. 70(1), 606–620. https://doi.org/10.1109/TCOMM.2021.3120721 (2022).

    Google Scholar 

  25. Liu, Y., Wang, Y. & Xu, W. Beamforming design for STAR-RIS-assisted NOMA with binary and coupled phase-shifts. Entropy 27(2), 210. https://doi.org/10.3390/e27020210 (2025).

    Google Scholar 

  26. Maraqa, O., Aboagye, S. & Ngatched, T. M. N. Optical STAR-RIS-aided VLC systems: RSMA versus NOMA. IEEE Open J. Commun. Soc. 5, 430–441. https://doi.org/10.1109/OJCOMS.2023.3347534 (2024).

    Google Scholar 

  27. Zhang, Z. et al. Active RIS vs. passive RIS: Which will prevail in 6G?. IEEE Trans. Commun. 71(3), 1707–1725. https://doi.org/10.1109/TCOMM.2022.3231893 (2023).

    Google Scholar 

  28. Ahmed, M. et al. A survey on STAR-RIS: Use cases, recent advances, and future research challenges. IEEE Internet Things J. 10(16), 14689–14711. https://doi.org/10.1109/JIOT.2023.3279357 (2023).

    Google Scholar 

  29. Malik, U. M., Javed, M. A., Zeadally, S. & Islam, S. Energy-efficient fog computing for 6G-enabled massive IoT: Recent trends and future opportunities. IEEE Internet Things J. 9(16), 14572–14594. https://doi.org/10.1109/JIOT.2021.3068056 (2022).

    Google Scholar 

  30. Taneja, A., Rani, S., Garg, S., Hassan, M. M. & AlQahtani, S. A. Energy aware resource control mechanism for improved performance in future green 6G networks. Comput. Netw. 217, 109333. https://doi.org/10.1016/j.comnet.2022.109333 (2022).

    Google Scholar 

  31. Wang, J.-B. et al. Power control and passive beamforming for the STAR-RIS with rotatable angles. IEEE Trans. Veh. Technol. 73(8), 12121–12125. https://doi.org/10.1109/TVT.2024.3369617 (2024).

    Google Scholar 

  32. Ma, X., Lei, X., Mathiopoulos, P. T. & da Costa, D. B. Active STAR-RIS aided cell-free massive MIMO: A performance study. IEEE Trans. Veh. Technol. 73(2), 2936–2941. https://doi.org/10.1109/TVT.2023.3319407 (2024).

    Google Scholar 

  33. Zhao, B., Zhang, C., Yi, W. & Liu, Y. Ergodic rate analysis of STAR-RIS aided NOMA systems. IEEE Commun. Lett. 26(10), 2297–2301. https://doi.org/10.1109/LCOMM.2022.3194363 (2022).

    Google Scholar 

  34. Chen, J. & Yu, X. Ergodic rate analysis and phase design of STAR-RIS aided NOMA with statistical CSI. IEEE Commun. Lett. 26(12), 2889–2893. https://doi.org/10.1109/LCOMM.2022.3202346 (2022).

    Google Scholar 

  35. Xu, W., Chen, J. & Yu, X. Joint design of power allocation and amplitude coefficients for ergodic rate optimization in STAR-RIS-aided NOMA system. IEEE Syst. J. 17(4), 5452–5463. https://doi.org/10.1109/JSYST.2023.3314890 (2023).

    Google Scholar 

  36. Xu, J., Zuo, J., Zhou, J. T. & Liu, Y. Active simultaneously transmitting and reflecting (STAR)-RISs: Modeling and analysis. IEEE Commun. Lett. 27(9), 2466–2470. https://doi.org/10.1109/LCOMM.2023.3289066 (2023).

    Google Scholar 

  37. Zhai, X., Han, G., Cai, Y., Liu, Y. & Hanzo, L. Simultaneously transmitting and reflecting (STAR) RIS assisted over-the-air computation systems. IEEE Trans. Commun. 71(3), 1309–1322. https://doi.org/10.1109/TCOMM.2023.3235915 (2023).

    Google Scholar 

  38. Wen, Y. et al. STAR-RIS-assisted-full-duplex jamming design for secure wireless communications system. IEEE Trans. Inf. Forensics Secur. https://doi.org/10.1109/TIFS.2024.3376248 (2024).

    Google Scholar 

  39. Zhu, G., Mu, X., Guo, L., Huang, A. & Xu, S. Robust resource allocation for STAR-RIS assisted SWIPT systems. Wirel. Commun. https://doi.org/10.1109/TWC.2023.3327502 (2023).

    Google Scholar 

  40. Cui, K., Wang, J., Liu, Y., Gao, Y., Yang, H. & He, Z. Energy-efficient resource allocation for 6G hybrid network based on native AI. In 2023 IEEE Globecom Workshops (GC Wkshps), Kuala Lumpur, Malaysia, 1758–1763. https://doi.org/10.1109/GCWkshps58843.2023.10464463. (2023).

  41. Hou, P. et al. Efficient edge server activation and service association for green computing in MEC-enabled internet of vehicles. IEEE Trans. Intell. Veh. https://doi.org/10.1109/TIV.2024.3379582 (2024).

    Google Scholar 

  42. A. Balaram, T. D. N. S. S. S. Rao, P. Rangaree et al. Energy–efficient distribution of resources in cyber-physical Internet of Things with 5G/6G communication framework. Wirel. Pers. Commun. https://doi.org/10.1007/s11277-024-11145-9 (2024).

  43. Moloudian, G. et al. RF energy harvesting techniques for battery-less wireless sensing, Industry 4.0, and Internet of Things: A review. IEEE Sens. J. 24(5), 5732–5745. https://doi.org/10.1109/JSEN.2024.3352402 (2024).

    Google Scholar 

  44. Zhang, Y. et al. Operation optimization of battery swapping stations with photovoltaics and battery energy storage stations supplied by transformer spare capacity. IET Gener. Transm. Distrib. 17(17), 3872–3882. https://doi.org/10.1049/gtd2.12938 (2023).

    Google Scholar 

  45. Mohammadi, E. et al. Application of soft actor-critic algorithms in optimizing wastewater treatment with time delays integration. Expert Syst. Appl. 277, 127180. https://doi.org/10.1016/j.eswa.2025.127180 (2025).

    Google Scholar 

  46. Miuccio, L., Riolo, S., Samarakoon, S., Bennis, M. & Panno, D. On learning generalized wireless MAC communication protocols via a feasible multi-agent reinforcement learning framework. IEEE Trans. Mach. Learn. Commun. Netw. 2, 298–317. https://doi.org/10.1109/TMLCN.2024.3368367 (2024).

    Google Scholar 

  47. Rafique, A., Mehmood, A., Hassan, N. U., Mehmood, M. Q. & Zubair, M. Power consumption analysis of a reconfigurable intelligent surface for self-sustained operations. In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), Singapore, Singapore, 1–5. https://doi.org/10.1109/VTC2024-Spring62846.2024.10683501 (2024).

Download references

Acknowledgements

The authors would like to thank all individuals and institutions that contributed to this research.

Funding

This work was supported in part by Multimedia University under the Research Fellow Grant MMUI/250008, and in part by Telekom Research & Development Sdn Bhd under Grant RDTC/241149. The authors express their gratitude to Quanzhou University of Information Engineering, and the Artificial Intelligence and Sensing Technologies Research Center, University of Tabuk for its support in this research.

Author information

Authors and Affiliations

  1. School of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China

    Mian Muhammad Kamal, Habib Khan, Jamal N. A. Hassan, Hamedalneel B. A. Hamid, Luo Yinsheng & Tianjun Ma

  2. College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China

    Syed Zain Ul Abideen

  3. Centre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor, Malaysia

    Muhammad Sheraz & Teong Chee Chuah

  4. Department of Electrical Engineering, University of Tabuk, 47713, Tabuk, Saudi Arabia

    Husam S. Samkari & Mohammed F. Allehyani

  5. Artificial Intelligence and Sensing Technologies Research Center, University of Tabuk, 47713, Tabuk, Saudi Arabia

    Husam S. Samkari

  6. Faculty of Engineering, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 11942, Jordan

    Muneera Altayeb

Authors
  1. Mian Muhammad Kamal
    View author publications

    Search author on:PubMed Google Scholar

  2. Syed Zain Ul Abideen
    View author publications

    Search author on:PubMed Google Scholar

  3. Muhammad Sheraz
    View author publications

    Search author on:PubMed Google Scholar

  4. Habib Khan
    View author publications

    Search author on:PubMed Google Scholar

  5. Jamal N. A. Hassan
    View author publications

    Search author on:PubMed Google Scholar

  6. Hamedalneel B. A. Hamid
    View author publications

    Search author on:PubMed Google Scholar

  7. Luo Yinsheng
    View author publications

    Search author on:PubMed Google Scholar

  8. Tianjun Ma
    View author publications

    Search author on:PubMed Google Scholar

  9. Husam S. Samkari
    View author publications

    Search author on:PubMed Google Scholar

  10. Mohammed F. Allehyani
    View author publications

    Search author on:PubMed Google Scholar

  11. Muneera Altayeb
    View author publications

    Search author on:PubMed Google Scholar

  12. Teong Chee Chuah
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Authorship Contribution Statement: Mian Muhammad Kamal: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft, Software. Syed Zain Ul Abideen: Investigation, Validation, Writing – Review & Editing. Muhammad Sheraz: Writing – Review & Editing, Resources, Supervision. Habib Khan: Methodology, Software, Data Curation. Jamal N.A Hassan: Investigation, Validation, Visualization. Hamedalneel Babiker Hamid: Software, Investigation and Validation. Luo Yinsheng: Investigation, Data Curation, Supervision. Tianjun Ma: Resources, Investigation. Husam S. Samkari: Validation, Formal Analysis. Mohammed F. Allehyani: Resources, Supervision. Muneera Altayeb: Investigation, Data Curation. Teong Chee Chuah: Conceptualization, Resources, Writing – Review & Editing, Supervision, Funding Acquisition.

Corresponding authors

Correspondence to Mian Muhammad Kamal or Teong Chee Chuah.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamal, M.M., Ul Abideen, S.Z., Sheraz, M. et al. Energy-optimized 6G communication framework with intelligent resource allocation for massive IoT networks. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47110-8

Download citation

  • Received: 11 December 2025

  • Accepted: 30 March 2026

  • Published: 06 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-47110-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • 6G communication
  • Massive IoT
  • Energy optimization
  • Intelligent resource allocation
  • Deep reinforcement learning (DRL)
  • STAR-RIS
  • Sustainable networks
  • Low-latency systems
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics